Research ArticleOptimization Approach for Resource Allocation on CloudComputing for IoT
Yeongho Choi1 and Yujin Lim2
1Department of Computer Science University of Suwon San 2-2 Wau-ri Bongdam-eup HwaseongGyeonggi-do 445-743 Republic of Korea2Department of Information Technology Engineering Sookmyung Womenrsquos University Cheongpa-ro 47-gil 100Yongsan-gu Seoul 04310 Republic of Korea
Correspondence should be addressed to Yujin Lim yujin91sookmyungackr
Received 28 December 2015 Accepted 28 January 2016
Academic Editor Fan Wu
Copyright copy 2016 Y Choi and Y Lim This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
Combinatorial auction is a popular approach for resource allocation in cloud computing One of the challenges in resourceallocation is that QoS (Quality of Service) constraints are satisfied and providerrsquos profit is maximized In order to increase theprofit the penalty cost for SLA (Service Level Agreement) violations needs to be reduced We consider execution time constraintas SLA constraint in combinatorial auction system In the system we determine winners at each bidding round according to thejobrsquos urgency based on execution time deadline in order to efficiently allocate resources and reduce the penalty cost To analyze theperformance of our mechanism we compare the providerrsquos profit and success rate of job completion with conventional mechanismusing real workload data
1 Introduction
Over recent years the growth of the IoT (Internet of Things)is expected to create a pervasive connection of things such asembedded devices sensors and actuatorsThiswill inevitablyresult in the generation of enormous amount of datawhich have to be autonomously stored processed accessedand managed Cloud computing has been recognized as aparadigm for the big data problem [1] Cloud computingallows the sensing data to be stored and used intelligently forsmart applications
One of challenges which arose when IoT meets cloudis resource allocation by which a cloud provider efficientlyallocates its resources to cloud users with SLA constraintsThe dominating performance factors in resource allocationinclude providerrsquos and userrsquos profit resource utilization andQoS (Quality of Service) [2] For resource allocation in cloudauction-based model is a popular approach in resource allo-cation and pricing [3] In particular combinatorial auction ispreferred in cloud computing because it allows a user to buy apackage of resources rather than an individual resource Since
the auction is done on group of resources it provides efficientresource allocation and helps to improve the profit to both aprovider and a user
The other issue of resource allocation in cloud computingis meeting SLA (Service Level Agreement) established witha user Before a provider provisions a service to a user aprovider and a user need to establish SLA contract The SLAis an agreement that specifies QoS between a provider and auser [4] We define two-levels of SLA to represent differentobjectives class-based SLA and job-based SLA [5] In class-based SLA for each job class QoS is measured based onperformance metrics In job-based SLA QoS is measuredusing the metrics of individual jobs Any single job with apoor service quality immediately affects the measured QoSand incurs some SLA penalty cost We believe the job-basedSLA is a more robust type of SLAs from the usersrsquo perspectivethan providersrsquo perspective and we focus on this In generalSLA is defined in terms of various performance metricssuch as service latency throughput consistency and securityIn our paper SLA is defined in terms of deadline alongwith execution time of each job The deadline violation is
Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2016 Article ID 3479247 6 pageshttpdxdoiorg10115520163479247
2 International Journal of Distributed Sensor Networks
important from QoS guarantees perspective It causes lossof profit for a provider due to incompletion to execute thecertain jobs and SLA penalty cost [6]
Considering SLA guarantee we propose a resourceallocation mechanism in combinatorial auction system forcloud computing For efficient resource allocation winnerdetermination problem in the auction should be solvedThe conventional winner determination mechanisms areproposed to maximize resource utilization and a providerrsquosprofit for each time interval However the profit can bemaximized by reducing the penalty cost for SLA violationTo reduce SLA violations the deadline constraints need tobe considered when winner is determined in the auctionThus we propose a winner determination mechanism withconsideration for the deadline constraints to maximize theproviderrsquos profit
The rest of the paper is organized as follows In Section 2we discuss the related work In Section 3 we present ourwinner determination mechanism In Sections 4 and 5 weevaluate the performance of ourmechanism and conclude thepaper with the future research plan
2 Related Work
We investigate resource allocation strategies for the SLA-based cloud computing framework Several proposals inthe literature are based on utility functions to dynamicallyallocate resources In [7] a two-tier resource managementapproach based on utility functions is presented It defines theVM (Virtual Machine) utility function as a linear function ofthe CPU resources In [8] an autonomic resource manageris presented to control the virtualized environment whichdecouples the provisioning of resources from the dynamicplacement of VMs It finds an optimal number and sizeof VMs to allocate CPU resources to applications whilemaximizing the utility function
There are several proposals that dynamically manageVMs by optimizing objective functions Reference [9] deter-mines dynamic placement of VMs based on minimizing anobjective cost function using linear programming The func-tion is to provide an economic model between infrastructureproviders and users The pMapper [10] system includes apower-aware application placement controller to optimize acost-performance function It uses the bin-packing algorithmbased on a modified version of FFD (First-Fit Decreasing) toplace the VMs on the servers while trying to meet the targetutilization
Researchers have investigated maximizing the profitunder SLA constraints In [11] a multilevel generalizedassignment problem is formulated To solve the problem thefirst-fit heuristic is used for maximizing the profit under SLAand the power budget constraint In [12] a combinatorialoptimization problem is defined to maximize the providerrsquosprofit from SLA compliant placement The problem is pre-sented as a multiunit combinatorial auction To solve theproblem a column generation method is presented to obtainnear optimal solutions
A combinatorial auction allows bidders to bid for acombination of multiple resources In other words bids aresubmitted for the whole bundle as a single unit After the auc-tion each bidder receives either the whole bundle or nothingIn the auction the goal of a cloud provider is to efficientlyallocate the available resources to users and to maximallygenerate its revenue In [13] CA-provision is proposed as anallocation mechanism to maximize the revenue for a cloudprovider as well as maximize the resource utilization Theproviderrsquos revenue has three elements the revenue of theresource the cost of the VM instances that are allocated tothe users and the cost of keeping the remaining resourcesidle The purpose of considering the two costs is to reducethe cloud providerrsquos losses Reference [14] proposes a modelcalled ABRA (Auction-Based Resource Co-Allocation) tosolve the resource coallocation problem It imposes penaltycosts on unallocated resources after an auction in order toimprove the resource utilization First a new neighborhoodstructure is proposed to define an ordering as a permutationof the bids in the auctionThen the search space is defined asthe set of all possible orderings to find the optimum solutionFinally various neighbor selection methods are used to findthe solution
The conventional resource allocation mechanisms incombinatorial auction system present winner determinationalgorithms to reduce the penalty cost in each bidding roundHowever since they do not consider the probability of SLAviolation when deadline gets to end the performance islimited We present a new winner determination algorithmwith consideration for deadline constraints of jobs (ieurgency) to reduce the penalty cost for SLA violation andmaximize the profit of the provider
3 Proposed Mechanism
A cloud provider offers computing services to users through119898 different types of VM instances VM
1VM2 VM
119898
The computing power of a VM instance of type VM119894 119894 =
1 119898 is 119908119894 where 119908
1= 1 119908
1lt 1199082
lt sdot sdot sdot lt 119908119898 and
119908 = (1199081 1199082 119908
119898) [13] We consider that 119908 is determined
mainly by the number of cores like Amazon EC2 [15] Let 119896119894
be the number of VM119894instances provisioned by the provider
Let119872 be themaximumnumber of VM instances provisionedby the provider The provider provisions a combination ofinstances given by the vector (119896
1 1198962 119896
119898) as long as
sum119898
119894=1119908119894119896119894le 119872 We consider 119899 users 119906
1 119906
119899who request
computing resources from the provider specified as bundlesof VM instances A user 119906
119895requests VM instances for its job
job119895by submitting a bid 119861
119895= (119903119895
1 119903
119895
119898 V119895) to the provider
where 119903119895
119894is the number of requested instances of type VM
119894
and V119895is the price user 119906
119895is willing to pay to use the requested
bundle of VMs for a unit of time In addition let us denote by119901119895the amount paid by user 119906
119895for using its requested bundle
of VMs and 119901119895and V119895can be different (usually 119901
119895le V119895) We
assume that the users are single minded which means a userbids for only one bundle
The provider runs auction mechanism periodically toallocate the VM instances Thus users bid for obtaining
International Journal of Distributed Sensor Networks 3
the VM bundles for a unit of time If the userrsquos job requiresa bundle for more than one unit of time the user has tobid again in the next round of the auction separately Auser bids until its application is completed or its deadline isexceeded Conventional winner determination mechanismsdo not consider that each job has different urgency In otherwords the job which has impending deadline needs to havea weight to the job with the time left before the deadline inthe competitive bidding To maximize the providerrsquos profitby reducing the penalty cost for SLA violations we use theprobability of deadline violations by considering the jobrsquosurgency when winners are determined [16]
In our paper SLA is defined in terms of completiondeadline 119889
119895for job job
119895of user 119906
119895 For simplicityrsquos sake we
assume that a user has one job at a time Our problem is that119870119895 119870119895le 119889119895biddings should be succeeded to complete job
119895
before 119889119895gets to endThe current bidding round 120579
119895is the sum
of the number of successful biddings 119896119895 119896119895
lt 119870119895and the
number of bidding failures 119891119895 in other words 120579
119895= 119896119895+ 119891119895
and 120579119895le 119889119895 We define the problem as the combination of 119889
119895
taken119870119895
119889119895119862119870119895
(1)
subject to119889119895ge 119870119895 (2)
The success probability of119870119895biddings before 119889
119895is as follows
1
119889119895119862119870119895
(3)
We identify that the probability of successful job completionneeds to be increased as 119891
119895or 120579119895increases At the current
round 120579119895with 119896
119895successful bidding to complete the job
before 119889119895 119870119895minus 119896119895successful biddings need more The
remaining rounds before 119889119895are 119889119895minus120579119895= 119889119895minus119891119895minus119896119895 Thus to
get the probability of job completion before119889119895 (3) is rewritten
as follows
Pr119895=
1
(119889119895minus119891119895minus119896119895)119862
(119870119895minus119896119895)
=
(119870119895minus 119896119895)
(119889119895minus119891119895minus119896119895)119875
(119870119895minus119896119895)
(4)
Using (4) we calculate the expected value of the providerrsquosprofit for each user to determine the winners at a biddinground The profit is divided into two cases When the jobis completed before deadline the profit is the differencebetween revenue and running cost 119862
119877of the VM instances
to be allocated to the user When the job is not completedbefore deadline and SLA is violated the provider should paythe penalty cost119862penalty
119895With the probability of SLA violation
in (4) we define 119864profit119895
the expected value of the providerrsquosprofit for 119906
119895as follows
119864profit119895
= Pr119895(V119895minus 119862119877
119898
sum
119894=1
119908119894119903119895
119894)
+ (1 minus Pr119895)(V119895minus 119862119877
119898
sum
119894=1
119908119894119903119895
119894minus 119862
penalty119895
)
(5)
0 2 4 6 8 10Number of biddings
Number of bidding successes (kj)Number of bidding failures (fj)
00
02
04
06
08
10
Prj
Figure 1 Pr119895with varying the number of biddings
In (6) we normalize 119864profit119895
with the maximum value of 119864profit119895
to remove the dependence on V119895 The maximum value of
119864profit119895
indicates the expected value when there is no SLAviolation
119864profit119895
V119895minus 119862119877sum119898
119894=1119908119894119903119895
119894
(6)
Using (6) winners are determined at each roundFigure 1 shows that Pr
119895increases as 119896
119895or 119891119895increases in
our mechanism We set 119889119895and 119870
119895to 15 and 10 respectively
Thus when 119891119895gt 5 job
119895is not completed before the deadline
In the figure Pr119895gets to 1 in order to complete the job before
the deadline when 119891119895= 5 Besides as you can see 119891
119895has
the bigger impact than 119896119895on Pr119895 Because 119864profit
119895increases as
Pr119895increases the probability that 119906
119895is to be a winner also
increases Thus the jobs with impending deadlines are likelyto be determined as winners Through the determinationdeadline violation can be reduced and the profit of theprovider can be improved
4 Performance Analysis
To evaluate the performance of our mechanism we con-duct simulation with real workload data and compare theperformance of the conventional mechanism (CA-provision[13]) In preliminary experiments we use UniLu-Gaia-2014workload logs from the ParallelWorkloadArchive [17] In thelogs the average number of submitted jobs per round is set toabout 24 and the average number of processors required perjob is 997 In the experiments we set 119862
119877to 05 and 119862
penalty119895
to10 percent of V
119895 119870119895is randomly selected from (1 20) and the
average execution time of a job is multiplying bidding timeinterval Δ119905 with 119870
119895
Figure 2 shows the probability of bidding success atcurrent round 120579
119895when the remaining bidding rounds before
119889119895vary In CA-provision the probability does not show
4 International Journal of Distributed Sensor Networks
OurCA-provision
0 5 10 15 20 2500
02
04
06
08
10
Prob
abili
ty o
f bid
ding
succ
ess
at cu
rren
t rou
nd120579j
Remaining bidding rounds before dj
Figure 2 The probability of bidding success at a current round
OurCA-provision
00
02
04
Succ
ess r
ate o
f job
com
plet
ion
06
08
10
10 20 30 400User ID
Figure 3 The success rate of job completion for users
the big change In our mechanism the probability increasesas the remaining round decreases By considering the jobrsquosurgency the probability gets to 1 as the deadline gets toend and the jobs with impending deadlines are likely to bedetermined as winners
Figure 3 shows the success rate of job completion foreach user The success rate of job completion indicates thenumber of jobs completed before the deadline to the numberof jobs submitted by user 119906
119895in the total simulation time The
success rate of ours is about 274 higher than that of CA-provision In Figure 4 the deadline of a job is determined bymultiplying a deadline factor 120591
119895with 119870
119895 thus the deadline
is 119889119895= 120591119895119870119895 Here 120591
119895is chosen from 15 20 25 and 30
The figure shows the success rate of job completion withvarying the deadline factors As you can see the success rateincreases as the deadline increases The success rate of ourmechanism is about 2 133 198 and 274 higher thanthat of CA-provision respectively Figures 3 and 4 show thatourmechanism effectively reduces the deadline violation andincreases the success rate of job completion
Our CA
Our
CA
Our
CA
Our
CA
Deadline factors
OurCA-provision
00
02
04
06
08
Succ
ess r
ate o
f job
com
plet
ion
10
times15 times20 times25 times30
Figure 4 The success rate of job completion with varying thedeadline factors
OurCA
OurCA
OurCA
OurCA
0
5000
10000
15000
20000Pr
ofit o
f clo
ud p
rovi
der
Deadline factors
OurCA-provision
times15 times20 times25 times30
Figure 5 The providerrsquos profit with varying the deadline factors
Figure 5 shows the profit of a cloud provider with varyingthe deadline factors The profit of our mechanism is about105 12 10 and 13 higher than that of CA-provisionrespectively This figure shows that through the winnerdetermination by considering the jobrsquos urgency deadlineviolation decreases and the profit of the provider increases
To evaluate the performance in different system scenar-ios we use eight workload logs from the Parallel WorkloadArchive [17] In Table 1 we show a brief description ofthe workload files The table describes the log file namethe average execution time of the jobs (119879wl) the averagenumber of jobs submitted for a unit of time (119869wl) theaverage number of processors required per job (119875wl) andthe total number of processors in the system (119872wl) Fromthe real workload data we use several data such as jobnumber execution time the number of allocated processorsaverage CPU time used and user ID Some records in a logfile are not specified because the original files had missinginformation So if a record data is missing we randomly
International Journal of Distributed Sensor Networks 5
Table 1 Real workload data
Log file 119879wl (hours) 119869wl (jobshr) 119875wl 119872wl
SDSC-DS 13 1026 6236 8192LLNL-Thunder 5 3362 4236 4008LLNL-Atlas 8 76 401 9216RICC 5 11987 1645 4096PIK-IPLEX 40 2046 3449 2560LCG 1 26116 3452 4096LLNL-uBGL 7 2234 576 2048UniLu-Gaia 3 2406 997 64
Our
CAOur
CA
Our
CA
OurCAOur
CAOur
CA
Our
CA
Our
CA
Succ
ess r
ate o
f job
com
plet
ion
00
02
04
06
08
10
per p
roce
ssor
-rou
nd
OurCA-provision
Workload file (normalized load)
SDSC
-DS(021
)
LLN
L-Th
unde
r(053
)
LLN
L-At
las(082
)
PIK-
IPLE
X(237
)
LCG
(765
)
LLN
L-uB
GL(785
)
Uni
Lu-G
aia998400 (1
49
)
RICC
998400(158
)
Figure 6 The success rate of job completion with varying theworkloads
generate the record data within the average range of theother records using a uniform distribution In RICC andUniLu-Gaia of the eight workloads we decrease119872wl by about70 to make competitive environment Thus we mark thetwo workloads with 119877119868119862119862
1015840 and 119880119899119894119871119906-1198661198861198941198861015840 in the figures
Since theworkloads are heterogeneous in several dimensionswe need normalization for workload logs To normalizeworkload logs we use normalized load in a workload wl120578wl = (119869wltimes119879wltimes119875wl)119872wlThe normalized loadmeasures theaverage amount of load per processor When analyzing theresults we use the normalized load to rank the heterogeneouslog files
Figure 6 shows the success rate of job completion indifferent workloads In the experiments we set 120591
119895to 20 Our
mechanism has 182 more success rate than CA-provisionFigure 7 shows the profit of a cloud provider Since theworkloads are generated for different durations of time forsystems with different number of processors we scale theprofit with respect to the total simulation hours and the
Our
CA
Our
CA Our
CA
Our
CA
Our
CA
Our
CAOur
CA OurCA
Profi
t of c
loud
pro
vide
r
OurCA-provision
Workload file (normalized load)
00
02
04
06
08
10
12
14
per p
roce
ssor
-rou
nd
SDSC
-DS(021
)
LLN
L-Th
unde
r(053
)
LLN
L-At
las(082
)
PIK-
IPLE
X(237
)
LCG
(765
)
LLN
L-uB
GL(785
)
Uni
Lu-G
aia998400 (1
49
)
RICC
998400(158
)
Figure 7 The providerrsquos profit with varying the workloads
number of processors We define the profit per processor-hour as Π
prwl = Πwl(119872wl times 119877wl) where Πwl is the sum
of profits in all bidding round and 119877wl is the number ofbiddings provided in each workload [13] As you can see ourmechanism has about 268 more profit than CA-provisionAs a result our mechanism shows the better performancewith respect to the success rate and the profit than CA-provision in various workload scenarios
5 Conclusion
To increase the providerrsquos profit the penalty cost for SLAviolations needs to be considered To reduce the cost weconsider the jobrsquos urgency based on the deadline constraintwhen winners are determined in the combinatorial auctionTaking the urgency into consideration we calculate theprobability of deadline violation for each jobThen using theprobability we calculate the expected value of the providerrsquosprofit when the corresponding user is selected as a winnerat a bidding round The user with the larger expected valueis likely to be determined as a winner Thus the penaltycost decreases by the decrease of SLA violation and theproviderrsquos profit increasesThe experimental results show thatour mechanism has higher profit and success rate of jobcompletion than these of the conventional mechanism Welook forward to compare the other winner determinationmechanisms in the combinatorial auction and demonstrateeffectiveness of our mechanism
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
6 International Journal of Distributed Sensor Networks
Acknowledgment
This research was supported by Basic Science ResearchProgram through the National Research Foundation ofKorea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A09057141)
References
[1] H-L Truong and S Dustdar ldquoPrinciples for engineering IoTcloud systemsrdquo IEEE Cloud Computing vol 2 no 2 pp 68ndash762015
[2] U Lampe M Siebenhaar A Papageorgiou D Schuller and RSteinmetz ldquoMaximizing cloud provider profit from equilibriumprice auctionsrdquo in Proceedings of the IEEE 5th InternationalConference on Cloud Computing (CLOUD rsquo12) pp 83ndash90Honolulu Hawaii USA June 2012
[3] S R Shirley and P Karthikeyan ldquoA survey on auction basedresource allocation in cloud environmentrdquo International Journalof Research in Computer Applications and Robotics vol 1 no 9pp 96ndash102 2013
[4] S Son G Jung and S C Jun ldquoAn SLA-based cloud computingthat facilitates resource allocation in the distributed data centersof a cloud providerrdquo Journal of Supercomputing vol 64 no 2pp 606ndash637 2013
[5] H J Moon Y Chi and H Hacıgumus ldquoSLA-aware profitoptimization in cloud services via resource schedulingrdquo in Pro-ceedings of the 6th International Conference on World Congresson Services (SERVICES rsquo10) pp 152ndash153 IEEEMiami Fla USAJuly 2010
[6] M Alrokayan A V Dastjerdi and R Buyya ldquoSLA-awareprovisioning and scheduling of cloud resources for big data ana-lyticsrdquo in Proceedings of the 3rd IEEE International Conferenceon Cloud Computing for Emerging Markets (CCEM rsquo14) pp 1ndash8Bangalore India October 2014
[7] D Minarolli and B Freisleben ldquoUtility-based resource alloca-tion for virtual machines in cloud computingrdquo in Proceeding ofthe 16th IEEE Symposium on Computers and Communications(ISCC rsquo11) pp 410ndash417 Kerkyra Greece July 2011
[8] H N Van F D Tran and J-M Menaud ldquoAutonomic virtualresource management for service hosting platformsrdquo in Pro-ceedings of the ICSE Workshop on Software Engineering Chal-lenges of Cloud Computing (CLOUD rsquo09) pp 1ndash8 VancouverCanada May 2009
[9] J-G Park J-M KimH Choi and Y-CWoo ldquoVirtualmachinemigration in self-managing virtualized server environmentsrdquoin Proceeding of the 11th IEEE International Conference onAdvanced Communication Technology (ICACT rsquo09) pp 2077ndash2083 February 2009
[10] A Verma P Ahuja and A Neogi ldquopMapper power andmigration cost aware application placement in virtualizedsystemsrdquo in Middleware 2008 ACMIFIPUSENIX 9th Inter-national Middleware Conference Leuven Belgium December 1ndash5 2008 Proceedings vol 5346 of Lecture Notes in ComputerScience pp 243ndash264 Springer Berlin Germany 2008
[11] W Shi and B Hong ldquoTowards profitable virtual machineplacement in the data centerrdquo in Proceedings of the 4th IEEEInternational Conference on Cloud and Utility Computing (UCCrsquo11) pp 138ndash145 IEEE Victoria Australia December 2011
[12] D Breitgand and A Epstein ldquoSLA-aware placement of multi-virtual machine elastic services in compute cloudsrdquo in Proceed-ings of the IFIPIEEE International Symposium on Integrated
NetworkManagement (IM rsquo11) pp 161ndash168Dublin IrelandMay2011
[13] S Zaman and D Grosu ldquoA combinatorial auction-based mech-anism for dynamic VM provisioning and allocation in cloudsrdquoIEEETransactions onCloudComputing vol 1 no 2 pp 129ndash1412013
[14] A H Ozer and C Ozturan ldquoAn auction based mathematicalmodel and heuristics for resource co-allocation problem ingrids and cloudsrdquo in Proceedings of the 5th International Confer-ence on Soft Computing Computing with Words and Perceptionsin SystemAnalysis Decision and Control (ICSCCW rsquo09) pp 1ndash4Famagusta Cyprus September 2009
[15] Amazon EC2 Spot Instances httpawsamazoncomec2spot-instances
[16] Y Choi andY Lim ldquoResourcemanagementmechanism for SLAprovisioning on cloud computing for IoTrdquo in Proceedings of theInternational Conference on Information and CommunicationTechnology Convergence (ICTC rsquo15) pp 500ndash502 IEEE JejuIsland South Korea October 2015
[17] D G Feitelson ldquoParallel Workloads Archiverdquo httpwwwcshujiacillabsparallelworkloadlogshtml
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
2 International Journal of Distributed Sensor Networks
important from QoS guarantees perspective It causes lossof profit for a provider due to incompletion to execute thecertain jobs and SLA penalty cost [6]
Considering SLA guarantee we propose a resourceallocation mechanism in combinatorial auction system forcloud computing For efficient resource allocation winnerdetermination problem in the auction should be solvedThe conventional winner determination mechanisms areproposed to maximize resource utilization and a providerrsquosprofit for each time interval However the profit can bemaximized by reducing the penalty cost for SLA violationTo reduce SLA violations the deadline constraints need tobe considered when winner is determined in the auctionThus we propose a winner determination mechanism withconsideration for the deadline constraints to maximize theproviderrsquos profit
The rest of the paper is organized as follows In Section 2we discuss the related work In Section 3 we present ourwinner determination mechanism In Sections 4 and 5 weevaluate the performance of ourmechanism and conclude thepaper with the future research plan
2 Related Work
We investigate resource allocation strategies for the SLA-based cloud computing framework Several proposals inthe literature are based on utility functions to dynamicallyallocate resources In [7] a two-tier resource managementapproach based on utility functions is presented It defines theVM (Virtual Machine) utility function as a linear function ofthe CPU resources In [8] an autonomic resource manageris presented to control the virtualized environment whichdecouples the provisioning of resources from the dynamicplacement of VMs It finds an optimal number and sizeof VMs to allocate CPU resources to applications whilemaximizing the utility function
There are several proposals that dynamically manageVMs by optimizing objective functions Reference [9] deter-mines dynamic placement of VMs based on minimizing anobjective cost function using linear programming The func-tion is to provide an economic model between infrastructureproviders and users The pMapper [10] system includes apower-aware application placement controller to optimize acost-performance function It uses the bin-packing algorithmbased on a modified version of FFD (First-Fit Decreasing) toplace the VMs on the servers while trying to meet the targetutilization
Researchers have investigated maximizing the profitunder SLA constraints In [11] a multilevel generalizedassignment problem is formulated To solve the problem thefirst-fit heuristic is used for maximizing the profit under SLAand the power budget constraint In [12] a combinatorialoptimization problem is defined to maximize the providerrsquosprofit from SLA compliant placement The problem is pre-sented as a multiunit combinatorial auction To solve theproblem a column generation method is presented to obtainnear optimal solutions
A combinatorial auction allows bidders to bid for acombination of multiple resources In other words bids aresubmitted for the whole bundle as a single unit After the auc-tion each bidder receives either the whole bundle or nothingIn the auction the goal of a cloud provider is to efficientlyallocate the available resources to users and to maximallygenerate its revenue In [13] CA-provision is proposed as anallocation mechanism to maximize the revenue for a cloudprovider as well as maximize the resource utilization Theproviderrsquos revenue has three elements the revenue of theresource the cost of the VM instances that are allocated tothe users and the cost of keeping the remaining resourcesidle The purpose of considering the two costs is to reducethe cloud providerrsquos losses Reference [14] proposes a modelcalled ABRA (Auction-Based Resource Co-Allocation) tosolve the resource coallocation problem It imposes penaltycosts on unallocated resources after an auction in order toimprove the resource utilization First a new neighborhoodstructure is proposed to define an ordering as a permutationof the bids in the auctionThen the search space is defined asthe set of all possible orderings to find the optimum solutionFinally various neighbor selection methods are used to findthe solution
The conventional resource allocation mechanisms incombinatorial auction system present winner determinationalgorithms to reduce the penalty cost in each bidding roundHowever since they do not consider the probability of SLAviolation when deadline gets to end the performance islimited We present a new winner determination algorithmwith consideration for deadline constraints of jobs (ieurgency) to reduce the penalty cost for SLA violation andmaximize the profit of the provider
3 Proposed Mechanism
A cloud provider offers computing services to users through119898 different types of VM instances VM
1VM2 VM
119898
The computing power of a VM instance of type VM119894 119894 =
1 119898 is 119908119894 where 119908
1= 1 119908
1lt 1199082
lt sdot sdot sdot lt 119908119898 and
119908 = (1199081 1199082 119908
119898) [13] We consider that 119908 is determined
mainly by the number of cores like Amazon EC2 [15] Let 119896119894
be the number of VM119894instances provisioned by the provider
Let119872 be themaximumnumber of VM instances provisionedby the provider The provider provisions a combination ofinstances given by the vector (119896
1 1198962 119896
119898) as long as
sum119898
119894=1119908119894119896119894le 119872 We consider 119899 users 119906
1 119906
119899who request
computing resources from the provider specified as bundlesof VM instances A user 119906
119895requests VM instances for its job
job119895by submitting a bid 119861
119895= (119903119895
1 119903
119895
119898 V119895) to the provider
where 119903119895
119894is the number of requested instances of type VM
119894
and V119895is the price user 119906
119895is willing to pay to use the requested
bundle of VMs for a unit of time In addition let us denote by119901119895the amount paid by user 119906
119895for using its requested bundle
of VMs and 119901119895and V119895can be different (usually 119901
119895le V119895) We
assume that the users are single minded which means a userbids for only one bundle
The provider runs auction mechanism periodically toallocate the VM instances Thus users bid for obtaining
International Journal of Distributed Sensor Networks 3
the VM bundles for a unit of time If the userrsquos job requiresa bundle for more than one unit of time the user has tobid again in the next round of the auction separately Auser bids until its application is completed or its deadline isexceeded Conventional winner determination mechanismsdo not consider that each job has different urgency In otherwords the job which has impending deadline needs to havea weight to the job with the time left before the deadline inthe competitive bidding To maximize the providerrsquos profitby reducing the penalty cost for SLA violations we use theprobability of deadline violations by considering the jobrsquosurgency when winners are determined [16]
In our paper SLA is defined in terms of completiondeadline 119889
119895for job job
119895of user 119906
119895 For simplicityrsquos sake we
assume that a user has one job at a time Our problem is that119870119895 119870119895le 119889119895biddings should be succeeded to complete job
119895
before 119889119895gets to endThe current bidding round 120579
119895is the sum
of the number of successful biddings 119896119895 119896119895
lt 119870119895and the
number of bidding failures 119891119895 in other words 120579
119895= 119896119895+ 119891119895
and 120579119895le 119889119895 We define the problem as the combination of 119889
119895
taken119870119895
119889119895119862119870119895
(1)
subject to119889119895ge 119870119895 (2)
The success probability of119870119895biddings before 119889
119895is as follows
1
119889119895119862119870119895
(3)
We identify that the probability of successful job completionneeds to be increased as 119891
119895or 120579119895increases At the current
round 120579119895with 119896
119895successful bidding to complete the job
before 119889119895 119870119895minus 119896119895successful biddings need more The
remaining rounds before 119889119895are 119889119895minus120579119895= 119889119895minus119891119895minus119896119895 Thus to
get the probability of job completion before119889119895 (3) is rewritten
as follows
Pr119895=
1
(119889119895minus119891119895minus119896119895)119862
(119870119895minus119896119895)
=
(119870119895minus 119896119895)
(119889119895minus119891119895minus119896119895)119875
(119870119895minus119896119895)
(4)
Using (4) we calculate the expected value of the providerrsquosprofit for each user to determine the winners at a biddinground The profit is divided into two cases When the jobis completed before deadline the profit is the differencebetween revenue and running cost 119862
119877of the VM instances
to be allocated to the user When the job is not completedbefore deadline and SLA is violated the provider should paythe penalty cost119862penalty
119895With the probability of SLA violation
in (4) we define 119864profit119895
the expected value of the providerrsquosprofit for 119906
119895as follows
119864profit119895
= Pr119895(V119895minus 119862119877
119898
sum
119894=1
119908119894119903119895
119894)
+ (1 minus Pr119895)(V119895minus 119862119877
119898
sum
119894=1
119908119894119903119895
119894minus 119862
penalty119895
)
(5)
0 2 4 6 8 10Number of biddings
Number of bidding successes (kj)Number of bidding failures (fj)
00
02
04
06
08
10
Prj
Figure 1 Pr119895with varying the number of biddings
In (6) we normalize 119864profit119895
with the maximum value of 119864profit119895
to remove the dependence on V119895 The maximum value of
119864profit119895
indicates the expected value when there is no SLAviolation
119864profit119895
V119895minus 119862119877sum119898
119894=1119908119894119903119895
119894
(6)
Using (6) winners are determined at each roundFigure 1 shows that Pr
119895increases as 119896
119895or 119891119895increases in
our mechanism We set 119889119895and 119870
119895to 15 and 10 respectively
Thus when 119891119895gt 5 job
119895is not completed before the deadline
In the figure Pr119895gets to 1 in order to complete the job before
the deadline when 119891119895= 5 Besides as you can see 119891
119895has
the bigger impact than 119896119895on Pr119895 Because 119864profit
119895increases as
Pr119895increases the probability that 119906
119895is to be a winner also
increases Thus the jobs with impending deadlines are likelyto be determined as winners Through the determinationdeadline violation can be reduced and the profit of theprovider can be improved
4 Performance Analysis
To evaluate the performance of our mechanism we con-duct simulation with real workload data and compare theperformance of the conventional mechanism (CA-provision[13]) In preliminary experiments we use UniLu-Gaia-2014workload logs from the ParallelWorkloadArchive [17] In thelogs the average number of submitted jobs per round is set toabout 24 and the average number of processors required perjob is 997 In the experiments we set 119862
119877to 05 and 119862
penalty119895
to10 percent of V
119895 119870119895is randomly selected from (1 20) and the
average execution time of a job is multiplying bidding timeinterval Δ119905 with 119870
119895
Figure 2 shows the probability of bidding success atcurrent round 120579
119895when the remaining bidding rounds before
119889119895vary In CA-provision the probability does not show
4 International Journal of Distributed Sensor Networks
OurCA-provision
0 5 10 15 20 2500
02
04
06
08
10
Prob
abili
ty o
f bid
ding
succ
ess
at cu
rren
t rou
nd120579j
Remaining bidding rounds before dj
Figure 2 The probability of bidding success at a current round
OurCA-provision
00
02
04
Succ
ess r
ate o
f job
com
plet
ion
06
08
10
10 20 30 400User ID
Figure 3 The success rate of job completion for users
the big change In our mechanism the probability increasesas the remaining round decreases By considering the jobrsquosurgency the probability gets to 1 as the deadline gets toend and the jobs with impending deadlines are likely to bedetermined as winners
Figure 3 shows the success rate of job completion foreach user The success rate of job completion indicates thenumber of jobs completed before the deadline to the numberof jobs submitted by user 119906
119895in the total simulation time The
success rate of ours is about 274 higher than that of CA-provision In Figure 4 the deadline of a job is determined bymultiplying a deadline factor 120591
119895with 119870
119895 thus the deadline
is 119889119895= 120591119895119870119895 Here 120591
119895is chosen from 15 20 25 and 30
The figure shows the success rate of job completion withvarying the deadline factors As you can see the success rateincreases as the deadline increases The success rate of ourmechanism is about 2 133 198 and 274 higher thanthat of CA-provision respectively Figures 3 and 4 show thatourmechanism effectively reduces the deadline violation andincreases the success rate of job completion
Our CA
Our
CA
Our
CA
Our
CA
Deadline factors
OurCA-provision
00
02
04
06
08
Succ
ess r
ate o
f job
com
plet
ion
10
times15 times20 times25 times30
Figure 4 The success rate of job completion with varying thedeadline factors
OurCA
OurCA
OurCA
OurCA
0
5000
10000
15000
20000Pr
ofit o
f clo
ud p
rovi
der
Deadline factors
OurCA-provision
times15 times20 times25 times30
Figure 5 The providerrsquos profit with varying the deadline factors
Figure 5 shows the profit of a cloud provider with varyingthe deadline factors The profit of our mechanism is about105 12 10 and 13 higher than that of CA-provisionrespectively This figure shows that through the winnerdetermination by considering the jobrsquos urgency deadlineviolation decreases and the profit of the provider increases
To evaluate the performance in different system scenar-ios we use eight workload logs from the Parallel WorkloadArchive [17] In Table 1 we show a brief description ofthe workload files The table describes the log file namethe average execution time of the jobs (119879wl) the averagenumber of jobs submitted for a unit of time (119869wl) theaverage number of processors required per job (119875wl) andthe total number of processors in the system (119872wl) Fromthe real workload data we use several data such as jobnumber execution time the number of allocated processorsaverage CPU time used and user ID Some records in a logfile are not specified because the original files had missinginformation So if a record data is missing we randomly
International Journal of Distributed Sensor Networks 5
Table 1 Real workload data
Log file 119879wl (hours) 119869wl (jobshr) 119875wl 119872wl
SDSC-DS 13 1026 6236 8192LLNL-Thunder 5 3362 4236 4008LLNL-Atlas 8 76 401 9216RICC 5 11987 1645 4096PIK-IPLEX 40 2046 3449 2560LCG 1 26116 3452 4096LLNL-uBGL 7 2234 576 2048UniLu-Gaia 3 2406 997 64
Our
CAOur
CA
Our
CA
OurCAOur
CAOur
CA
Our
CA
Our
CA
Succ
ess r
ate o
f job
com
plet
ion
00
02
04
06
08
10
per p
roce
ssor
-rou
nd
OurCA-provision
Workload file (normalized load)
SDSC
-DS(021
)
LLN
L-Th
unde
r(053
)
LLN
L-At
las(082
)
PIK-
IPLE
X(237
)
LCG
(765
)
LLN
L-uB
GL(785
)
Uni
Lu-G
aia998400 (1
49
)
RICC
998400(158
)
Figure 6 The success rate of job completion with varying theworkloads
generate the record data within the average range of theother records using a uniform distribution In RICC andUniLu-Gaia of the eight workloads we decrease119872wl by about70 to make competitive environment Thus we mark thetwo workloads with 119877119868119862119862
1015840 and 119880119899119894119871119906-1198661198861198941198861015840 in the figures
Since theworkloads are heterogeneous in several dimensionswe need normalization for workload logs To normalizeworkload logs we use normalized load in a workload wl120578wl = (119869wltimes119879wltimes119875wl)119872wlThe normalized loadmeasures theaverage amount of load per processor When analyzing theresults we use the normalized load to rank the heterogeneouslog files
Figure 6 shows the success rate of job completion indifferent workloads In the experiments we set 120591
119895to 20 Our
mechanism has 182 more success rate than CA-provisionFigure 7 shows the profit of a cloud provider Since theworkloads are generated for different durations of time forsystems with different number of processors we scale theprofit with respect to the total simulation hours and the
Our
CA
Our
CA Our
CA
Our
CA
Our
CA
Our
CAOur
CA OurCA
Profi
t of c
loud
pro
vide
r
OurCA-provision
Workload file (normalized load)
00
02
04
06
08
10
12
14
per p
roce
ssor
-rou
nd
SDSC
-DS(021
)
LLN
L-Th
unde
r(053
)
LLN
L-At
las(082
)
PIK-
IPLE
X(237
)
LCG
(765
)
LLN
L-uB
GL(785
)
Uni
Lu-G
aia998400 (1
49
)
RICC
998400(158
)
Figure 7 The providerrsquos profit with varying the workloads
number of processors We define the profit per processor-hour as Π
prwl = Πwl(119872wl times 119877wl) where Πwl is the sum
of profits in all bidding round and 119877wl is the number ofbiddings provided in each workload [13] As you can see ourmechanism has about 268 more profit than CA-provisionAs a result our mechanism shows the better performancewith respect to the success rate and the profit than CA-provision in various workload scenarios
5 Conclusion
To increase the providerrsquos profit the penalty cost for SLAviolations needs to be considered To reduce the cost weconsider the jobrsquos urgency based on the deadline constraintwhen winners are determined in the combinatorial auctionTaking the urgency into consideration we calculate theprobability of deadline violation for each jobThen using theprobability we calculate the expected value of the providerrsquosprofit when the corresponding user is selected as a winnerat a bidding round The user with the larger expected valueis likely to be determined as a winner Thus the penaltycost decreases by the decrease of SLA violation and theproviderrsquos profit increasesThe experimental results show thatour mechanism has higher profit and success rate of jobcompletion than these of the conventional mechanism Welook forward to compare the other winner determinationmechanisms in the combinatorial auction and demonstrateeffectiveness of our mechanism
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
6 International Journal of Distributed Sensor Networks
Acknowledgment
This research was supported by Basic Science ResearchProgram through the National Research Foundation ofKorea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A09057141)
References
[1] H-L Truong and S Dustdar ldquoPrinciples for engineering IoTcloud systemsrdquo IEEE Cloud Computing vol 2 no 2 pp 68ndash762015
[2] U Lampe M Siebenhaar A Papageorgiou D Schuller and RSteinmetz ldquoMaximizing cloud provider profit from equilibriumprice auctionsrdquo in Proceedings of the IEEE 5th InternationalConference on Cloud Computing (CLOUD rsquo12) pp 83ndash90Honolulu Hawaii USA June 2012
[3] S R Shirley and P Karthikeyan ldquoA survey on auction basedresource allocation in cloud environmentrdquo International Journalof Research in Computer Applications and Robotics vol 1 no 9pp 96ndash102 2013
[4] S Son G Jung and S C Jun ldquoAn SLA-based cloud computingthat facilitates resource allocation in the distributed data centersof a cloud providerrdquo Journal of Supercomputing vol 64 no 2pp 606ndash637 2013
[5] H J Moon Y Chi and H Hacıgumus ldquoSLA-aware profitoptimization in cloud services via resource schedulingrdquo in Pro-ceedings of the 6th International Conference on World Congresson Services (SERVICES rsquo10) pp 152ndash153 IEEEMiami Fla USAJuly 2010
[6] M Alrokayan A V Dastjerdi and R Buyya ldquoSLA-awareprovisioning and scheduling of cloud resources for big data ana-lyticsrdquo in Proceedings of the 3rd IEEE International Conferenceon Cloud Computing for Emerging Markets (CCEM rsquo14) pp 1ndash8Bangalore India October 2014
[7] D Minarolli and B Freisleben ldquoUtility-based resource alloca-tion for virtual machines in cloud computingrdquo in Proceeding ofthe 16th IEEE Symposium on Computers and Communications(ISCC rsquo11) pp 410ndash417 Kerkyra Greece July 2011
[8] H N Van F D Tran and J-M Menaud ldquoAutonomic virtualresource management for service hosting platformsrdquo in Pro-ceedings of the ICSE Workshop on Software Engineering Chal-lenges of Cloud Computing (CLOUD rsquo09) pp 1ndash8 VancouverCanada May 2009
[9] J-G Park J-M KimH Choi and Y-CWoo ldquoVirtualmachinemigration in self-managing virtualized server environmentsrdquoin Proceeding of the 11th IEEE International Conference onAdvanced Communication Technology (ICACT rsquo09) pp 2077ndash2083 February 2009
[10] A Verma P Ahuja and A Neogi ldquopMapper power andmigration cost aware application placement in virtualizedsystemsrdquo in Middleware 2008 ACMIFIPUSENIX 9th Inter-national Middleware Conference Leuven Belgium December 1ndash5 2008 Proceedings vol 5346 of Lecture Notes in ComputerScience pp 243ndash264 Springer Berlin Germany 2008
[11] W Shi and B Hong ldquoTowards profitable virtual machineplacement in the data centerrdquo in Proceedings of the 4th IEEEInternational Conference on Cloud and Utility Computing (UCCrsquo11) pp 138ndash145 IEEE Victoria Australia December 2011
[12] D Breitgand and A Epstein ldquoSLA-aware placement of multi-virtual machine elastic services in compute cloudsrdquo in Proceed-ings of the IFIPIEEE International Symposium on Integrated
NetworkManagement (IM rsquo11) pp 161ndash168Dublin IrelandMay2011
[13] S Zaman and D Grosu ldquoA combinatorial auction-based mech-anism for dynamic VM provisioning and allocation in cloudsrdquoIEEETransactions onCloudComputing vol 1 no 2 pp 129ndash1412013
[14] A H Ozer and C Ozturan ldquoAn auction based mathematicalmodel and heuristics for resource co-allocation problem ingrids and cloudsrdquo in Proceedings of the 5th International Confer-ence on Soft Computing Computing with Words and Perceptionsin SystemAnalysis Decision and Control (ICSCCW rsquo09) pp 1ndash4Famagusta Cyprus September 2009
[15] Amazon EC2 Spot Instances httpawsamazoncomec2spot-instances
[16] Y Choi andY Lim ldquoResourcemanagementmechanism for SLAprovisioning on cloud computing for IoTrdquo in Proceedings of theInternational Conference on Information and CommunicationTechnology Convergence (ICTC rsquo15) pp 500ndash502 IEEE JejuIsland South Korea October 2015
[17] D G Feitelson ldquoParallel Workloads Archiverdquo httpwwwcshujiacillabsparallelworkloadlogshtml
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
International Journal of Distributed Sensor Networks 3
the VM bundles for a unit of time If the userrsquos job requiresa bundle for more than one unit of time the user has tobid again in the next round of the auction separately Auser bids until its application is completed or its deadline isexceeded Conventional winner determination mechanismsdo not consider that each job has different urgency In otherwords the job which has impending deadline needs to havea weight to the job with the time left before the deadline inthe competitive bidding To maximize the providerrsquos profitby reducing the penalty cost for SLA violations we use theprobability of deadline violations by considering the jobrsquosurgency when winners are determined [16]
In our paper SLA is defined in terms of completiondeadline 119889
119895for job job
119895of user 119906
119895 For simplicityrsquos sake we
assume that a user has one job at a time Our problem is that119870119895 119870119895le 119889119895biddings should be succeeded to complete job
119895
before 119889119895gets to endThe current bidding round 120579
119895is the sum
of the number of successful biddings 119896119895 119896119895
lt 119870119895and the
number of bidding failures 119891119895 in other words 120579
119895= 119896119895+ 119891119895
and 120579119895le 119889119895 We define the problem as the combination of 119889
119895
taken119870119895
119889119895119862119870119895
(1)
subject to119889119895ge 119870119895 (2)
The success probability of119870119895biddings before 119889
119895is as follows
1
119889119895119862119870119895
(3)
We identify that the probability of successful job completionneeds to be increased as 119891
119895or 120579119895increases At the current
round 120579119895with 119896
119895successful bidding to complete the job
before 119889119895 119870119895minus 119896119895successful biddings need more The
remaining rounds before 119889119895are 119889119895minus120579119895= 119889119895minus119891119895minus119896119895 Thus to
get the probability of job completion before119889119895 (3) is rewritten
as follows
Pr119895=
1
(119889119895minus119891119895minus119896119895)119862
(119870119895minus119896119895)
=
(119870119895minus 119896119895)
(119889119895minus119891119895minus119896119895)119875
(119870119895minus119896119895)
(4)
Using (4) we calculate the expected value of the providerrsquosprofit for each user to determine the winners at a biddinground The profit is divided into two cases When the jobis completed before deadline the profit is the differencebetween revenue and running cost 119862
119877of the VM instances
to be allocated to the user When the job is not completedbefore deadline and SLA is violated the provider should paythe penalty cost119862penalty
119895With the probability of SLA violation
in (4) we define 119864profit119895
the expected value of the providerrsquosprofit for 119906
119895as follows
119864profit119895
= Pr119895(V119895minus 119862119877
119898
sum
119894=1
119908119894119903119895
119894)
+ (1 minus Pr119895)(V119895minus 119862119877
119898
sum
119894=1
119908119894119903119895
119894minus 119862
penalty119895
)
(5)
0 2 4 6 8 10Number of biddings
Number of bidding successes (kj)Number of bidding failures (fj)
00
02
04
06
08
10
Prj
Figure 1 Pr119895with varying the number of biddings
In (6) we normalize 119864profit119895
with the maximum value of 119864profit119895
to remove the dependence on V119895 The maximum value of
119864profit119895
indicates the expected value when there is no SLAviolation
119864profit119895
V119895minus 119862119877sum119898
119894=1119908119894119903119895
119894
(6)
Using (6) winners are determined at each roundFigure 1 shows that Pr
119895increases as 119896
119895or 119891119895increases in
our mechanism We set 119889119895and 119870
119895to 15 and 10 respectively
Thus when 119891119895gt 5 job
119895is not completed before the deadline
In the figure Pr119895gets to 1 in order to complete the job before
the deadline when 119891119895= 5 Besides as you can see 119891
119895has
the bigger impact than 119896119895on Pr119895 Because 119864profit
119895increases as
Pr119895increases the probability that 119906
119895is to be a winner also
increases Thus the jobs with impending deadlines are likelyto be determined as winners Through the determinationdeadline violation can be reduced and the profit of theprovider can be improved
4 Performance Analysis
To evaluate the performance of our mechanism we con-duct simulation with real workload data and compare theperformance of the conventional mechanism (CA-provision[13]) In preliminary experiments we use UniLu-Gaia-2014workload logs from the ParallelWorkloadArchive [17] In thelogs the average number of submitted jobs per round is set toabout 24 and the average number of processors required perjob is 997 In the experiments we set 119862
119877to 05 and 119862
penalty119895
to10 percent of V
119895 119870119895is randomly selected from (1 20) and the
average execution time of a job is multiplying bidding timeinterval Δ119905 with 119870
119895
Figure 2 shows the probability of bidding success atcurrent round 120579
119895when the remaining bidding rounds before
119889119895vary In CA-provision the probability does not show
4 International Journal of Distributed Sensor Networks
OurCA-provision
0 5 10 15 20 2500
02
04
06
08
10
Prob
abili
ty o
f bid
ding
succ
ess
at cu
rren
t rou
nd120579j
Remaining bidding rounds before dj
Figure 2 The probability of bidding success at a current round
OurCA-provision
00
02
04
Succ
ess r
ate o
f job
com
plet
ion
06
08
10
10 20 30 400User ID
Figure 3 The success rate of job completion for users
the big change In our mechanism the probability increasesas the remaining round decreases By considering the jobrsquosurgency the probability gets to 1 as the deadline gets toend and the jobs with impending deadlines are likely to bedetermined as winners
Figure 3 shows the success rate of job completion foreach user The success rate of job completion indicates thenumber of jobs completed before the deadline to the numberof jobs submitted by user 119906
119895in the total simulation time The
success rate of ours is about 274 higher than that of CA-provision In Figure 4 the deadline of a job is determined bymultiplying a deadline factor 120591
119895with 119870
119895 thus the deadline
is 119889119895= 120591119895119870119895 Here 120591
119895is chosen from 15 20 25 and 30
The figure shows the success rate of job completion withvarying the deadline factors As you can see the success rateincreases as the deadline increases The success rate of ourmechanism is about 2 133 198 and 274 higher thanthat of CA-provision respectively Figures 3 and 4 show thatourmechanism effectively reduces the deadline violation andincreases the success rate of job completion
Our CA
Our
CA
Our
CA
Our
CA
Deadline factors
OurCA-provision
00
02
04
06
08
Succ
ess r
ate o
f job
com
plet
ion
10
times15 times20 times25 times30
Figure 4 The success rate of job completion with varying thedeadline factors
OurCA
OurCA
OurCA
OurCA
0
5000
10000
15000
20000Pr
ofit o
f clo
ud p
rovi
der
Deadline factors
OurCA-provision
times15 times20 times25 times30
Figure 5 The providerrsquos profit with varying the deadline factors
Figure 5 shows the profit of a cloud provider with varyingthe deadline factors The profit of our mechanism is about105 12 10 and 13 higher than that of CA-provisionrespectively This figure shows that through the winnerdetermination by considering the jobrsquos urgency deadlineviolation decreases and the profit of the provider increases
To evaluate the performance in different system scenar-ios we use eight workload logs from the Parallel WorkloadArchive [17] In Table 1 we show a brief description ofthe workload files The table describes the log file namethe average execution time of the jobs (119879wl) the averagenumber of jobs submitted for a unit of time (119869wl) theaverage number of processors required per job (119875wl) andthe total number of processors in the system (119872wl) Fromthe real workload data we use several data such as jobnumber execution time the number of allocated processorsaverage CPU time used and user ID Some records in a logfile are not specified because the original files had missinginformation So if a record data is missing we randomly
International Journal of Distributed Sensor Networks 5
Table 1 Real workload data
Log file 119879wl (hours) 119869wl (jobshr) 119875wl 119872wl
SDSC-DS 13 1026 6236 8192LLNL-Thunder 5 3362 4236 4008LLNL-Atlas 8 76 401 9216RICC 5 11987 1645 4096PIK-IPLEX 40 2046 3449 2560LCG 1 26116 3452 4096LLNL-uBGL 7 2234 576 2048UniLu-Gaia 3 2406 997 64
Our
CAOur
CA
Our
CA
OurCAOur
CAOur
CA
Our
CA
Our
CA
Succ
ess r
ate o
f job
com
plet
ion
00
02
04
06
08
10
per p
roce
ssor
-rou
nd
OurCA-provision
Workload file (normalized load)
SDSC
-DS(021
)
LLN
L-Th
unde
r(053
)
LLN
L-At
las(082
)
PIK-
IPLE
X(237
)
LCG
(765
)
LLN
L-uB
GL(785
)
Uni
Lu-G
aia998400 (1
49
)
RICC
998400(158
)
Figure 6 The success rate of job completion with varying theworkloads
generate the record data within the average range of theother records using a uniform distribution In RICC andUniLu-Gaia of the eight workloads we decrease119872wl by about70 to make competitive environment Thus we mark thetwo workloads with 119877119868119862119862
1015840 and 119880119899119894119871119906-1198661198861198941198861015840 in the figures
Since theworkloads are heterogeneous in several dimensionswe need normalization for workload logs To normalizeworkload logs we use normalized load in a workload wl120578wl = (119869wltimes119879wltimes119875wl)119872wlThe normalized loadmeasures theaverage amount of load per processor When analyzing theresults we use the normalized load to rank the heterogeneouslog files
Figure 6 shows the success rate of job completion indifferent workloads In the experiments we set 120591
119895to 20 Our
mechanism has 182 more success rate than CA-provisionFigure 7 shows the profit of a cloud provider Since theworkloads are generated for different durations of time forsystems with different number of processors we scale theprofit with respect to the total simulation hours and the
Our
CA
Our
CA Our
CA
Our
CA
Our
CA
Our
CAOur
CA OurCA
Profi
t of c
loud
pro
vide
r
OurCA-provision
Workload file (normalized load)
00
02
04
06
08
10
12
14
per p
roce
ssor
-rou
nd
SDSC
-DS(021
)
LLN
L-Th
unde
r(053
)
LLN
L-At
las(082
)
PIK-
IPLE
X(237
)
LCG
(765
)
LLN
L-uB
GL(785
)
Uni
Lu-G
aia998400 (1
49
)
RICC
998400(158
)
Figure 7 The providerrsquos profit with varying the workloads
number of processors We define the profit per processor-hour as Π
prwl = Πwl(119872wl times 119877wl) where Πwl is the sum
of profits in all bidding round and 119877wl is the number ofbiddings provided in each workload [13] As you can see ourmechanism has about 268 more profit than CA-provisionAs a result our mechanism shows the better performancewith respect to the success rate and the profit than CA-provision in various workload scenarios
5 Conclusion
To increase the providerrsquos profit the penalty cost for SLAviolations needs to be considered To reduce the cost weconsider the jobrsquos urgency based on the deadline constraintwhen winners are determined in the combinatorial auctionTaking the urgency into consideration we calculate theprobability of deadline violation for each jobThen using theprobability we calculate the expected value of the providerrsquosprofit when the corresponding user is selected as a winnerat a bidding round The user with the larger expected valueis likely to be determined as a winner Thus the penaltycost decreases by the decrease of SLA violation and theproviderrsquos profit increasesThe experimental results show thatour mechanism has higher profit and success rate of jobcompletion than these of the conventional mechanism Welook forward to compare the other winner determinationmechanisms in the combinatorial auction and demonstrateeffectiveness of our mechanism
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
6 International Journal of Distributed Sensor Networks
Acknowledgment
This research was supported by Basic Science ResearchProgram through the National Research Foundation ofKorea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A09057141)
References
[1] H-L Truong and S Dustdar ldquoPrinciples for engineering IoTcloud systemsrdquo IEEE Cloud Computing vol 2 no 2 pp 68ndash762015
[2] U Lampe M Siebenhaar A Papageorgiou D Schuller and RSteinmetz ldquoMaximizing cloud provider profit from equilibriumprice auctionsrdquo in Proceedings of the IEEE 5th InternationalConference on Cloud Computing (CLOUD rsquo12) pp 83ndash90Honolulu Hawaii USA June 2012
[3] S R Shirley and P Karthikeyan ldquoA survey on auction basedresource allocation in cloud environmentrdquo International Journalof Research in Computer Applications and Robotics vol 1 no 9pp 96ndash102 2013
[4] S Son G Jung and S C Jun ldquoAn SLA-based cloud computingthat facilitates resource allocation in the distributed data centersof a cloud providerrdquo Journal of Supercomputing vol 64 no 2pp 606ndash637 2013
[5] H J Moon Y Chi and H Hacıgumus ldquoSLA-aware profitoptimization in cloud services via resource schedulingrdquo in Pro-ceedings of the 6th International Conference on World Congresson Services (SERVICES rsquo10) pp 152ndash153 IEEEMiami Fla USAJuly 2010
[6] M Alrokayan A V Dastjerdi and R Buyya ldquoSLA-awareprovisioning and scheduling of cloud resources for big data ana-lyticsrdquo in Proceedings of the 3rd IEEE International Conferenceon Cloud Computing for Emerging Markets (CCEM rsquo14) pp 1ndash8Bangalore India October 2014
[7] D Minarolli and B Freisleben ldquoUtility-based resource alloca-tion for virtual machines in cloud computingrdquo in Proceeding ofthe 16th IEEE Symposium on Computers and Communications(ISCC rsquo11) pp 410ndash417 Kerkyra Greece July 2011
[8] H N Van F D Tran and J-M Menaud ldquoAutonomic virtualresource management for service hosting platformsrdquo in Pro-ceedings of the ICSE Workshop on Software Engineering Chal-lenges of Cloud Computing (CLOUD rsquo09) pp 1ndash8 VancouverCanada May 2009
[9] J-G Park J-M KimH Choi and Y-CWoo ldquoVirtualmachinemigration in self-managing virtualized server environmentsrdquoin Proceeding of the 11th IEEE International Conference onAdvanced Communication Technology (ICACT rsquo09) pp 2077ndash2083 February 2009
[10] A Verma P Ahuja and A Neogi ldquopMapper power andmigration cost aware application placement in virtualizedsystemsrdquo in Middleware 2008 ACMIFIPUSENIX 9th Inter-national Middleware Conference Leuven Belgium December 1ndash5 2008 Proceedings vol 5346 of Lecture Notes in ComputerScience pp 243ndash264 Springer Berlin Germany 2008
[11] W Shi and B Hong ldquoTowards profitable virtual machineplacement in the data centerrdquo in Proceedings of the 4th IEEEInternational Conference on Cloud and Utility Computing (UCCrsquo11) pp 138ndash145 IEEE Victoria Australia December 2011
[12] D Breitgand and A Epstein ldquoSLA-aware placement of multi-virtual machine elastic services in compute cloudsrdquo in Proceed-ings of the IFIPIEEE International Symposium on Integrated
NetworkManagement (IM rsquo11) pp 161ndash168Dublin IrelandMay2011
[13] S Zaman and D Grosu ldquoA combinatorial auction-based mech-anism for dynamic VM provisioning and allocation in cloudsrdquoIEEETransactions onCloudComputing vol 1 no 2 pp 129ndash1412013
[14] A H Ozer and C Ozturan ldquoAn auction based mathematicalmodel and heuristics for resource co-allocation problem ingrids and cloudsrdquo in Proceedings of the 5th International Confer-ence on Soft Computing Computing with Words and Perceptionsin SystemAnalysis Decision and Control (ICSCCW rsquo09) pp 1ndash4Famagusta Cyprus September 2009
[15] Amazon EC2 Spot Instances httpawsamazoncomec2spot-instances
[16] Y Choi andY Lim ldquoResourcemanagementmechanism for SLAprovisioning on cloud computing for IoTrdquo in Proceedings of theInternational Conference on Information and CommunicationTechnology Convergence (ICTC rsquo15) pp 500ndash502 IEEE JejuIsland South Korea October 2015
[17] D G Feitelson ldquoParallel Workloads Archiverdquo httpwwwcshujiacillabsparallelworkloadlogshtml
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
4 International Journal of Distributed Sensor Networks
OurCA-provision
0 5 10 15 20 2500
02
04
06
08
10
Prob
abili
ty o
f bid
ding
succ
ess
at cu
rren
t rou
nd120579j
Remaining bidding rounds before dj
Figure 2 The probability of bidding success at a current round
OurCA-provision
00
02
04
Succ
ess r
ate o
f job
com
plet
ion
06
08
10
10 20 30 400User ID
Figure 3 The success rate of job completion for users
the big change In our mechanism the probability increasesas the remaining round decreases By considering the jobrsquosurgency the probability gets to 1 as the deadline gets toend and the jobs with impending deadlines are likely to bedetermined as winners
Figure 3 shows the success rate of job completion foreach user The success rate of job completion indicates thenumber of jobs completed before the deadline to the numberof jobs submitted by user 119906
119895in the total simulation time The
success rate of ours is about 274 higher than that of CA-provision In Figure 4 the deadline of a job is determined bymultiplying a deadline factor 120591
119895with 119870
119895 thus the deadline
is 119889119895= 120591119895119870119895 Here 120591
119895is chosen from 15 20 25 and 30
The figure shows the success rate of job completion withvarying the deadline factors As you can see the success rateincreases as the deadline increases The success rate of ourmechanism is about 2 133 198 and 274 higher thanthat of CA-provision respectively Figures 3 and 4 show thatourmechanism effectively reduces the deadline violation andincreases the success rate of job completion
Our CA
Our
CA
Our
CA
Our
CA
Deadline factors
OurCA-provision
00
02
04
06
08
Succ
ess r
ate o
f job
com
plet
ion
10
times15 times20 times25 times30
Figure 4 The success rate of job completion with varying thedeadline factors
OurCA
OurCA
OurCA
OurCA
0
5000
10000
15000
20000Pr
ofit o
f clo
ud p
rovi
der
Deadline factors
OurCA-provision
times15 times20 times25 times30
Figure 5 The providerrsquos profit with varying the deadline factors
Figure 5 shows the profit of a cloud provider with varyingthe deadline factors The profit of our mechanism is about105 12 10 and 13 higher than that of CA-provisionrespectively This figure shows that through the winnerdetermination by considering the jobrsquos urgency deadlineviolation decreases and the profit of the provider increases
To evaluate the performance in different system scenar-ios we use eight workload logs from the Parallel WorkloadArchive [17] In Table 1 we show a brief description ofthe workload files The table describes the log file namethe average execution time of the jobs (119879wl) the averagenumber of jobs submitted for a unit of time (119869wl) theaverage number of processors required per job (119875wl) andthe total number of processors in the system (119872wl) Fromthe real workload data we use several data such as jobnumber execution time the number of allocated processorsaverage CPU time used and user ID Some records in a logfile are not specified because the original files had missinginformation So if a record data is missing we randomly
International Journal of Distributed Sensor Networks 5
Table 1 Real workload data
Log file 119879wl (hours) 119869wl (jobshr) 119875wl 119872wl
SDSC-DS 13 1026 6236 8192LLNL-Thunder 5 3362 4236 4008LLNL-Atlas 8 76 401 9216RICC 5 11987 1645 4096PIK-IPLEX 40 2046 3449 2560LCG 1 26116 3452 4096LLNL-uBGL 7 2234 576 2048UniLu-Gaia 3 2406 997 64
Our
CAOur
CA
Our
CA
OurCAOur
CAOur
CA
Our
CA
Our
CA
Succ
ess r
ate o
f job
com
plet
ion
00
02
04
06
08
10
per p
roce
ssor
-rou
nd
OurCA-provision
Workload file (normalized load)
SDSC
-DS(021
)
LLN
L-Th
unde
r(053
)
LLN
L-At
las(082
)
PIK-
IPLE
X(237
)
LCG
(765
)
LLN
L-uB
GL(785
)
Uni
Lu-G
aia998400 (1
49
)
RICC
998400(158
)
Figure 6 The success rate of job completion with varying theworkloads
generate the record data within the average range of theother records using a uniform distribution In RICC andUniLu-Gaia of the eight workloads we decrease119872wl by about70 to make competitive environment Thus we mark thetwo workloads with 119877119868119862119862
1015840 and 119880119899119894119871119906-1198661198861198941198861015840 in the figures
Since theworkloads are heterogeneous in several dimensionswe need normalization for workload logs To normalizeworkload logs we use normalized load in a workload wl120578wl = (119869wltimes119879wltimes119875wl)119872wlThe normalized loadmeasures theaverage amount of load per processor When analyzing theresults we use the normalized load to rank the heterogeneouslog files
Figure 6 shows the success rate of job completion indifferent workloads In the experiments we set 120591
119895to 20 Our
mechanism has 182 more success rate than CA-provisionFigure 7 shows the profit of a cloud provider Since theworkloads are generated for different durations of time forsystems with different number of processors we scale theprofit with respect to the total simulation hours and the
Our
CA
Our
CA Our
CA
Our
CA
Our
CA
Our
CAOur
CA OurCA
Profi
t of c
loud
pro
vide
r
OurCA-provision
Workload file (normalized load)
00
02
04
06
08
10
12
14
per p
roce
ssor
-rou
nd
SDSC
-DS(021
)
LLN
L-Th
unde
r(053
)
LLN
L-At
las(082
)
PIK-
IPLE
X(237
)
LCG
(765
)
LLN
L-uB
GL(785
)
Uni
Lu-G
aia998400 (1
49
)
RICC
998400(158
)
Figure 7 The providerrsquos profit with varying the workloads
number of processors We define the profit per processor-hour as Π
prwl = Πwl(119872wl times 119877wl) where Πwl is the sum
of profits in all bidding round and 119877wl is the number ofbiddings provided in each workload [13] As you can see ourmechanism has about 268 more profit than CA-provisionAs a result our mechanism shows the better performancewith respect to the success rate and the profit than CA-provision in various workload scenarios
5 Conclusion
To increase the providerrsquos profit the penalty cost for SLAviolations needs to be considered To reduce the cost weconsider the jobrsquos urgency based on the deadline constraintwhen winners are determined in the combinatorial auctionTaking the urgency into consideration we calculate theprobability of deadline violation for each jobThen using theprobability we calculate the expected value of the providerrsquosprofit when the corresponding user is selected as a winnerat a bidding round The user with the larger expected valueis likely to be determined as a winner Thus the penaltycost decreases by the decrease of SLA violation and theproviderrsquos profit increasesThe experimental results show thatour mechanism has higher profit and success rate of jobcompletion than these of the conventional mechanism Welook forward to compare the other winner determinationmechanisms in the combinatorial auction and demonstrateeffectiveness of our mechanism
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
6 International Journal of Distributed Sensor Networks
Acknowledgment
This research was supported by Basic Science ResearchProgram through the National Research Foundation ofKorea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A09057141)
References
[1] H-L Truong and S Dustdar ldquoPrinciples for engineering IoTcloud systemsrdquo IEEE Cloud Computing vol 2 no 2 pp 68ndash762015
[2] U Lampe M Siebenhaar A Papageorgiou D Schuller and RSteinmetz ldquoMaximizing cloud provider profit from equilibriumprice auctionsrdquo in Proceedings of the IEEE 5th InternationalConference on Cloud Computing (CLOUD rsquo12) pp 83ndash90Honolulu Hawaii USA June 2012
[3] S R Shirley and P Karthikeyan ldquoA survey on auction basedresource allocation in cloud environmentrdquo International Journalof Research in Computer Applications and Robotics vol 1 no 9pp 96ndash102 2013
[4] S Son G Jung and S C Jun ldquoAn SLA-based cloud computingthat facilitates resource allocation in the distributed data centersof a cloud providerrdquo Journal of Supercomputing vol 64 no 2pp 606ndash637 2013
[5] H J Moon Y Chi and H Hacıgumus ldquoSLA-aware profitoptimization in cloud services via resource schedulingrdquo in Pro-ceedings of the 6th International Conference on World Congresson Services (SERVICES rsquo10) pp 152ndash153 IEEEMiami Fla USAJuly 2010
[6] M Alrokayan A V Dastjerdi and R Buyya ldquoSLA-awareprovisioning and scheduling of cloud resources for big data ana-lyticsrdquo in Proceedings of the 3rd IEEE International Conferenceon Cloud Computing for Emerging Markets (CCEM rsquo14) pp 1ndash8Bangalore India October 2014
[7] D Minarolli and B Freisleben ldquoUtility-based resource alloca-tion for virtual machines in cloud computingrdquo in Proceeding ofthe 16th IEEE Symposium on Computers and Communications(ISCC rsquo11) pp 410ndash417 Kerkyra Greece July 2011
[8] H N Van F D Tran and J-M Menaud ldquoAutonomic virtualresource management for service hosting platformsrdquo in Pro-ceedings of the ICSE Workshop on Software Engineering Chal-lenges of Cloud Computing (CLOUD rsquo09) pp 1ndash8 VancouverCanada May 2009
[9] J-G Park J-M KimH Choi and Y-CWoo ldquoVirtualmachinemigration in self-managing virtualized server environmentsrdquoin Proceeding of the 11th IEEE International Conference onAdvanced Communication Technology (ICACT rsquo09) pp 2077ndash2083 February 2009
[10] A Verma P Ahuja and A Neogi ldquopMapper power andmigration cost aware application placement in virtualizedsystemsrdquo in Middleware 2008 ACMIFIPUSENIX 9th Inter-national Middleware Conference Leuven Belgium December 1ndash5 2008 Proceedings vol 5346 of Lecture Notes in ComputerScience pp 243ndash264 Springer Berlin Germany 2008
[11] W Shi and B Hong ldquoTowards profitable virtual machineplacement in the data centerrdquo in Proceedings of the 4th IEEEInternational Conference on Cloud and Utility Computing (UCCrsquo11) pp 138ndash145 IEEE Victoria Australia December 2011
[12] D Breitgand and A Epstein ldquoSLA-aware placement of multi-virtual machine elastic services in compute cloudsrdquo in Proceed-ings of the IFIPIEEE International Symposium on Integrated
NetworkManagement (IM rsquo11) pp 161ndash168Dublin IrelandMay2011
[13] S Zaman and D Grosu ldquoA combinatorial auction-based mech-anism for dynamic VM provisioning and allocation in cloudsrdquoIEEETransactions onCloudComputing vol 1 no 2 pp 129ndash1412013
[14] A H Ozer and C Ozturan ldquoAn auction based mathematicalmodel and heuristics for resource co-allocation problem ingrids and cloudsrdquo in Proceedings of the 5th International Confer-ence on Soft Computing Computing with Words and Perceptionsin SystemAnalysis Decision and Control (ICSCCW rsquo09) pp 1ndash4Famagusta Cyprus September 2009
[15] Amazon EC2 Spot Instances httpawsamazoncomec2spot-instances
[16] Y Choi andY Lim ldquoResourcemanagementmechanism for SLAprovisioning on cloud computing for IoTrdquo in Proceedings of theInternational Conference on Information and CommunicationTechnology Convergence (ICTC rsquo15) pp 500ndash502 IEEE JejuIsland South Korea October 2015
[17] D G Feitelson ldquoParallel Workloads Archiverdquo httpwwwcshujiacillabsparallelworkloadlogshtml
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
International Journal of Distributed Sensor Networks 5
Table 1 Real workload data
Log file 119879wl (hours) 119869wl (jobshr) 119875wl 119872wl
SDSC-DS 13 1026 6236 8192LLNL-Thunder 5 3362 4236 4008LLNL-Atlas 8 76 401 9216RICC 5 11987 1645 4096PIK-IPLEX 40 2046 3449 2560LCG 1 26116 3452 4096LLNL-uBGL 7 2234 576 2048UniLu-Gaia 3 2406 997 64
Our
CAOur
CA
Our
CA
OurCAOur
CAOur
CA
Our
CA
Our
CA
Succ
ess r
ate o
f job
com
plet
ion
00
02
04
06
08
10
per p
roce
ssor
-rou
nd
OurCA-provision
Workload file (normalized load)
SDSC
-DS(021
)
LLN
L-Th
unde
r(053
)
LLN
L-At
las(082
)
PIK-
IPLE
X(237
)
LCG
(765
)
LLN
L-uB
GL(785
)
Uni
Lu-G
aia998400 (1
49
)
RICC
998400(158
)
Figure 6 The success rate of job completion with varying theworkloads
generate the record data within the average range of theother records using a uniform distribution In RICC andUniLu-Gaia of the eight workloads we decrease119872wl by about70 to make competitive environment Thus we mark thetwo workloads with 119877119868119862119862
1015840 and 119880119899119894119871119906-1198661198861198941198861015840 in the figures
Since theworkloads are heterogeneous in several dimensionswe need normalization for workload logs To normalizeworkload logs we use normalized load in a workload wl120578wl = (119869wltimes119879wltimes119875wl)119872wlThe normalized loadmeasures theaverage amount of load per processor When analyzing theresults we use the normalized load to rank the heterogeneouslog files
Figure 6 shows the success rate of job completion indifferent workloads In the experiments we set 120591
119895to 20 Our
mechanism has 182 more success rate than CA-provisionFigure 7 shows the profit of a cloud provider Since theworkloads are generated for different durations of time forsystems with different number of processors we scale theprofit with respect to the total simulation hours and the
Our
CA
Our
CA Our
CA
Our
CA
Our
CA
Our
CAOur
CA OurCA
Profi
t of c
loud
pro
vide
r
OurCA-provision
Workload file (normalized load)
00
02
04
06
08
10
12
14
per p
roce
ssor
-rou
nd
SDSC
-DS(021
)
LLN
L-Th
unde
r(053
)
LLN
L-At
las(082
)
PIK-
IPLE
X(237
)
LCG
(765
)
LLN
L-uB
GL(785
)
Uni
Lu-G
aia998400 (1
49
)
RICC
998400(158
)
Figure 7 The providerrsquos profit with varying the workloads
number of processors We define the profit per processor-hour as Π
prwl = Πwl(119872wl times 119877wl) where Πwl is the sum
of profits in all bidding round and 119877wl is the number ofbiddings provided in each workload [13] As you can see ourmechanism has about 268 more profit than CA-provisionAs a result our mechanism shows the better performancewith respect to the success rate and the profit than CA-provision in various workload scenarios
5 Conclusion
To increase the providerrsquos profit the penalty cost for SLAviolations needs to be considered To reduce the cost weconsider the jobrsquos urgency based on the deadline constraintwhen winners are determined in the combinatorial auctionTaking the urgency into consideration we calculate theprobability of deadline violation for each jobThen using theprobability we calculate the expected value of the providerrsquosprofit when the corresponding user is selected as a winnerat a bidding round The user with the larger expected valueis likely to be determined as a winner Thus the penaltycost decreases by the decrease of SLA violation and theproviderrsquos profit increasesThe experimental results show thatour mechanism has higher profit and success rate of jobcompletion than these of the conventional mechanism Welook forward to compare the other winner determinationmechanisms in the combinatorial auction and demonstrateeffectiveness of our mechanism
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
6 International Journal of Distributed Sensor Networks
Acknowledgment
This research was supported by Basic Science ResearchProgram through the National Research Foundation ofKorea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A09057141)
References
[1] H-L Truong and S Dustdar ldquoPrinciples for engineering IoTcloud systemsrdquo IEEE Cloud Computing vol 2 no 2 pp 68ndash762015
[2] U Lampe M Siebenhaar A Papageorgiou D Schuller and RSteinmetz ldquoMaximizing cloud provider profit from equilibriumprice auctionsrdquo in Proceedings of the IEEE 5th InternationalConference on Cloud Computing (CLOUD rsquo12) pp 83ndash90Honolulu Hawaii USA June 2012
[3] S R Shirley and P Karthikeyan ldquoA survey on auction basedresource allocation in cloud environmentrdquo International Journalof Research in Computer Applications and Robotics vol 1 no 9pp 96ndash102 2013
[4] S Son G Jung and S C Jun ldquoAn SLA-based cloud computingthat facilitates resource allocation in the distributed data centersof a cloud providerrdquo Journal of Supercomputing vol 64 no 2pp 606ndash637 2013
[5] H J Moon Y Chi and H Hacıgumus ldquoSLA-aware profitoptimization in cloud services via resource schedulingrdquo in Pro-ceedings of the 6th International Conference on World Congresson Services (SERVICES rsquo10) pp 152ndash153 IEEEMiami Fla USAJuly 2010
[6] M Alrokayan A V Dastjerdi and R Buyya ldquoSLA-awareprovisioning and scheduling of cloud resources for big data ana-lyticsrdquo in Proceedings of the 3rd IEEE International Conferenceon Cloud Computing for Emerging Markets (CCEM rsquo14) pp 1ndash8Bangalore India October 2014
[7] D Minarolli and B Freisleben ldquoUtility-based resource alloca-tion for virtual machines in cloud computingrdquo in Proceeding ofthe 16th IEEE Symposium on Computers and Communications(ISCC rsquo11) pp 410ndash417 Kerkyra Greece July 2011
[8] H N Van F D Tran and J-M Menaud ldquoAutonomic virtualresource management for service hosting platformsrdquo in Pro-ceedings of the ICSE Workshop on Software Engineering Chal-lenges of Cloud Computing (CLOUD rsquo09) pp 1ndash8 VancouverCanada May 2009
[9] J-G Park J-M KimH Choi and Y-CWoo ldquoVirtualmachinemigration in self-managing virtualized server environmentsrdquoin Proceeding of the 11th IEEE International Conference onAdvanced Communication Technology (ICACT rsquo09) pp 2077ndash2083 February 2009
[10] A Verma P Ahuja and A Neogi ldquopMapper power andmigration cost aware application placement in virtualizedsystemsrdquo in Middleware 2008 ACMIFIPUSENIX 9th Inter-national Middleware Conference Leuven Belgium December 1ndash5 2008 Proceedings vol 5346 of Lecture Notes in ComputerScience pp 243ndash264 Springer Berlin Germany 2008
[11] W Shi and B Hong ldquoTowards profitable virtual machineplacement in the data centerrdquo in Proceedings of the 4th IEEEInternational Conference on Cloud and Utility Computing (UCCrsquo11) pp 138ndash145 IEEE Victoria Australia December 2011
[12] D Breitgand and A Epstein ldquoSLA-aware placement of multi-virtual machine elastic services in compute cloudsrdquo in Proceed-ings of the IFIPIEEE International Symposium on Integrated
NetworkManagement (IM rsquo11) pp 161ndash168Dublin IrelandMay2011
[13] S Zaman and D Grosu ldquoA combinatorial auction-based mech-anism for dynamic VM provisioning and allocation in cloudsrdquoIEEETransactions onCloudComputing vol 1 no 2 pp 129ndash1412013
[14] A H Ozer and C Ozturan ldquoAn auction based mathematicalmodel and heuristics for resource co-allocation problem ingrids and cloudsrdquo in Proceedings of the 5th International Confer-ence on Soft Computing Computing with Words and Perceptionsin SystemAnalysis Decision and Control (ICSCCW rsquo09) pp 1ndash4Famagusta Cyprus September 2009
[15] Amazon EC2 Spot Instances httpawsamazoncomec2spot-instances
[16] Y Choi andY Lim ldquoResourcemanagementmechanism for SLAprovisioning on cloud computing for IoTrdquo in Proceedings of theInternational Conference on Information and CommunicationTechnology Convergence (ICTC rsquo15) pp 500ndash502 IEEE JejuIsland South Korea October 2015
[17] D G Feitelson ldquoParallel Workloads Archiverdquo httpwwwcshujiacillabsparallelworkloadlogshtml
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
6 International Journal of Distributed Sensor Networks
Acknowledgment
This research was supported by Basic Science ResearchProgram through the National Research Foundation ofKorea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A09057141)
References
[1] H-L Truong and S Dustdar ldquoPrinciples for engineering IoTcloud systemsrdquo IEEE Cloud Computing vol 2 no 2 pp 68ndash762015
[2] U Lampe M Siebenhaar A Papageorgiou D Schuller and RSteinmetz ldquoMaximizing cloud provider profit from equilibriumprice auctionsrdquo in Proceedings of the IEEE 5th InternationalConference on Cloud Computing (CLOUD rsquo12) pp 83ndash90Honolulu Hawaii USA June 2012
[3] S R Shirley and P Karthikeyan ldquoA survey on auction basedresource allocation in cloud environmentrdquo International Journalof Research in Computer Applications and Robotics vol 1 no 9pp 96ndash102 2013
[4] S Son G Jung and S C Jun ldquoAn SLA-based cloud computingthat facilitates resource allocation in the distributed data centersof a cloud providerrdquo Journal of Supercomputing vol 64 no 2pp 606ndash637 2013
[5] H J Moon Y Chi and H Hacıgumus ldquoSLA-aware profitoptimization in cloud services via resource schedulingrdquo in Pro-ceedings of the 6th International Conference on World Congresson Services (SERVICES rsquo10) pp 152ndash153 IEEEMiami Fla USAJuly 2010
[6] M Alrokayan A V Dastjerdi and R Buyya ldquoSLA-awareprovisioning and scheduling of cloud resources for big data ana-lyticsrdquo in Proceedings of the 3rd IEEE International Conferenceon Cloud Computing for Emerging Markets (CCEM rsquo14) pp 1ndash8Bangalore India October 2014
[7] D Minarolli and B Freisleben ldquoUtility-based resource alloca-tion for virtual machines in cloud computingrdquo in Proceeding ofthe 16th IEEE Symposium on Computers and Communications(ISCC rsquo11) pp 410ndash417 Kerkyra Greece July 2011
[8] H N Van F D Tran and J-M Menaud ldquoAutonomic virtualresource management for service hosting platformsrdquo in Pro-ceedings of the ICSE Workshop on Software Engineering Chal-lenges of Cloud Computing (CLOUD rsquo09) pp 1ndash8 VancouverCanada May 2009
[9] J-G Park J-M KimH Choi and Y-CWoo ldquoVirtualmachinemigration in self-managing virtualized server environmentsrdquoin Proceeding of the 11th IEEE International Conference onAdvanced Communication Technology (ICACT rsquo09) pp 2077ndash2083 February 2009
[10] A Verma P Ahuja and A Neogi ldquopMapper power andmigration cost aware application placement in virtualizedsystemsrdquo in Middleware 2008 ACMIFIPUSENIX 9th Inter-national Middleware Conference Leuven Belgium December 1ndash5 2008 Proceedings vol 5346 of Lecture Notes in ComputerScience pp 243ndash264 Springer Berlin Germany 2008
[11] W Shi and B Hong ldquoTowards profitable virtual machineplacement in the data centerrdquo in Proceedings of the 4th IEEEInternational Conference on Cloud and Utility Computing (UCCrsquo11) pp 138ndash145 IEEE Victoria Australia December 2011
[12] D Breitgand and A Epstein ldquoSLA-aware placement of multi-virtual machine elastic services in compute cloudsrdquo in Proceed-ings of the IFIPIEEE International Symposium on Integrated
NetworkManagement (IM rsquo11) pp 161ndash168Dublin IrelandMay2011
[13] S Zaman and D Grosu ldquoA combinatorial auction-based mech-anism for dynamic VM provisioning and allocation in cloudsrdquoIEEETransactions onCloudComputing vol 1 no 2 pp 129ndash1412013
[14] A H Ozer and C Ozturan ldquoAn auction based mathematicalmodel and heuristics for resource co-allocation problem ingrids and cloudsrdquo in Proceedings of the 5th International Confer-ence on Soft Computing Computing with Words and Perceptionsin SystemAnalysis Decision and Control (ICSCCW rsquo09) pp 1ndash4Famagusta Cyprus September 2009
[15] Amazon EC2 Spot Instances httpawsamazoncomec2spot-instances
[16] Y Choi andY Lim ldquoResourcemanagementmechanism for SLAprovisioning on cloud computing for IoTrdquo in Proceedings of theInternational Conference on Information and CommunicationTechnology Convergence (ICTC rsquo15) pp 500ndash502 IEEE JejuIsland South Korea October 2015
[17] D G Feitelson ldquoParallel Workloads Archiverdquo httpwwwcshujiacillabsparallelworkloadlogshtml
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of