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OpticsOptikOptikOptik 121 (2010) 793–799

0030-4026/$ - se

doi:10.1016/j.ijl

�CorrespondE-mail addr

www.elsevier.de/ijleo

An efficient scheme for optimizing channel utilization in OBS networks

Amit Kumar Garga,�, R.S. Kalerb

aSchool of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Jammu and Kashmir, IndiabSchool of Electronics and Communication Engineering, Thapar University, Patiala, Punjab, India

Received 13 May 2008; accepted 10 September 2008

Abstract

Optical burst switching (OBS) is an emerging technology that allows variable size data bursts to be transporteddirectly over DWDM links. In order to make OBS a viable solution, the wavelength scheduling algorithms needto be able to utilize the available wavelengths efficiently, while being able to operate fast enough to keep up withthe burst incoming rate. Unfortunately, horizon scheduling cannot utilize the voids created by previously scheduledbursts, resulting in low bandwidth utilization. To date, Min-SV is the fastest scheduling algorithm that canschedule wavelengths efficiently. However, its complexity is O (logm) and it requires 10 log (m) memory accesses toschedule a single burst. This means that it can take upto several microseconds for each burst request, which is stilltoo slow to make it a practical solution for OBS deployment. In this paper, an efficient scheme has been proposedfor optimizing channel utilization in OBS networks. In the proposed approach, a burst is represented by an intervalof time. The process of scheduling a number of bursts, thus, turns to be a process of fitting a set of thecorresponding time intervals on a channel time line that represents a channel-time resource. By doing so,the scheduling process can be formulated as a combinatorial optimization problem. Then, graph theory isapplied to schedule as many non-overlapping intervals as possible onto the channel time line. The underlyingconcept of the proposed scheduling scheme is that of briefly delaying the scheduling of a burst so that a muchbetter decision can be made about a number of bursts all-together. This scheme is shown, through simulations,to improve performance in terms of burst loss probability, channel utilization, fairness-control and data throughputover existing schemes. Thus the proposed scheme is well suited for high performance networks in terms ofreliability.r 2009 Elsevier GmbH. All rights reserved.

Keywords: Optical burst switching; Burst scheduling-algorithms; Scheduling system model; Combinatorial optimization

1. Introduction

To meet the increasing bandwidth demands andreduce costs, several optical network paradigms havebeen under intensive research. Of all these paradigms,

e front matter r 2009 Elsevier GmbH. All rights reserved.

eo.2008.09.024

ing author.

ess: garg_amit03@yahoo.co.in (A.K. Garg).

optical circuit switching (e.g., wavelength routing) isrelatively easy to implement but lacks efficiency to copewith the fluctuating traffic and the changing link state;optical packet switching (OPS) is a natural choice, butthe required optical technologies such as optical bufferand optical logic are too immature for it to happenanytime soon. A new approach called optical burstswitching (OBS) that combines the best of optical circuit

ARTICLE IN PRESSA.K. Garg, R.S. Kaler / Optik 121 (2010) 793–799794

switching and optical packet switching was proposed in[1,2]. OBS has been receiving an increasing attention asa potentially bandwidth-efficient approach for futureoptical core networks. In OBS networks, an ingressnode assembles incoming packets into data bursts(DBs). Along with each of these created DBs, theingress node also generates a burst header packet (BHP)that contains control information like channel identifi-cation, destination node identification and the DBlength and DB arrival time. The DBs will later bedisassembled into the original packets at an egress node.Unlike packet switched networks, OBS networksseparate each DB from its control information(i.e., BHP) and transmit them on different channels.A channel carrying DBs is referred to as a data channel;while, a channel carrying BHPs is referred to as acontrol channel. Upon receiving the BHP, intermediatenodes allocate resources based on the informationit carries. While a BHP is sent out to reserve networkresources along a path, transmission of the DB isdeferred for some time period, which is known as the‘‘offset-time’’. In OBS networks, a key problem is thusto design efficient algorithms for scheduling bursts(or more precisely their bandwidth reservation). Anideal scheduling algorithm should be able to process acontrol packet fast enough before the burst arrives,and yet be able to find a suitable void interval (or asuitable combination of a fiber delay line (FDL) and avoid interval) for the burst as long as there existsone. Otherwise, a burst may be unnecessarily discardedeither because a reservation cannot be completed beforethe burst arrives or simply because the schedulingalgorithm is not smart enough to make the reservation.Given the fact that OBS uses one-way reservationprotocols such as just-enough-time (JET) and that aburst cannot be buffered at any intermediate nodedue to the lack of optical RAM (a FDL, if availableat all, can only provide a limited delay and contentionresolution capability), burst loss performance is amajor concern in OBS networks. Hence, an efficientscheduling algorithm that can reduce burst loss byscheduling bursts fast and in a bandwidth efficientway is of paramount concern in OBS network design.So far, two well known scheduling algorithms havebeen proposed. Horizon [3] does not utilize any voidintervals and thus is fast but not bandwidth efficient.On the other hand, latest available unused channelwith void filling (LAUC-VF) [4] can schedule a burstas long as it is possible but has a slow running time. Inthis paper, a novel scheduling technique has beenproposed that relies on BHP batching to gain additionalinformation and prior details about a batch of incomingDBs before commencing to schedule them. Theproposed scheduling is a novel scheme in whichthe task of serving the scheduling needs of arrivingrequests is not carried out immediately on one-by-one

basis. Instead, scheduling is delayed for certain periodof time during which BHP arrivals are gathered.Then, the scheduler processes all these requests atonce with the goal of optimizing channel utilization.The proposed scheduling technique could be regarded asan approach, among others, that attempts to pushscheduling efficiency to some limit with the goal ofidentifying the extents of OBS benefits in a networkenvironment.

2. Prior solutions and their limits

Several scheduling techniques have been proposedfor OBS networks. Earlier proposals were mainlybased on the concept of Horizon scheduling [3], wherethe scheduler attempts to reserve channels for a databurst immediately after the arrival of a header cell.If the attempt is successful, resources will be reservedright away until a burst transmission is due to complete.The only information need to be kept is the latesttime the channel was utilized. This scheme, however,cannot utilize a transmission channel during thetime gaps between scheduled transmissions. LAUC-VF[4] can produce efficient channel schedules but it takesO (m) time to schedule a burst, which is too slow tobe practical. The minimum starting void (Min-SV)algorithm [5,6] can produce the same wavelengthschedule as LAUC-VF and its complexity is O (logm)where m is the number of voids per channel. Thisis a significant improvement over LAUC-VF. However,Min-SV still requires 10 log (m) memory accessesfor each burst request. It is not unusual that a systemwill have to keep track of 100K to a million voids.This means that Min-SV takes up to a few microsecondsto schedule a single burst, which is still too slow tomeet the stringent burst scheduling requirement.Later, the concept of delayed reservation was intro-duced where an offset time is maintained betweenDB and BHP transmissions. Protocols such as JETand just-in-time (JIT) were based on this concept[2–7] and aimed at allocating transmission resourcesonly upon arrival of the DB and for the time periodspecified in its BHP. This made it possible to fit afuture DB with the right size into a vacant transmissiongap between the already scheduled DBs. Delayedreservation based with an offset time scheduling ismore resource efficient than horizon-based schedulingand results in lower burst dropping probability.Yet, it reserves resource immediately after the BHP isreceived and processed. Since the scheduler processesone request at a time, delayed reservation resembles anon-line processing mechanism, in which no futureinformation is available to support the process ofdecision making.

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BHP Collector (Batcher)

Classifier With

Channel Assignment

Datachannel

Scheduler

Collected BHPs

Classified BHPs

ChannelSchedule

Incoming

BHPs

Fig. 1. Proposed scheduling system model.

BHP

Buffer

Basket N

Basket 2

Basket 1

Incoming BHPs

Fig. 2. BHP batcher model.

A.K. Garg, R.S. Kaler / Optik 121 (2010) 793–799 795

3. Proposed scheduling system model

In the proposed scheduling system model, a channel isrepresented by a time line. The proposed schedulingscheme partitions this channel time line into small timewindows where a channel scheduling decision can takeplace on a per-window basis. Only those DBs thatintend to utilize channel during a certain channelwindow, not the others, are scheduled together. ABHP, in this scheme, thus serves as a DB agent inrequesting channel resources for a particular channelwindow. To accomplish such a goal, the proposedscheduling system requires three basic building blocks: aBHP collector (batcher) module, a classifier withchannel assignment module and a channel scheduler,as illustrated in Fig. 1.

(A) BHP collector (batcher) module: In order to beeligible for scheduling in a certain time window, a BHPmust arrive prior to the closing time of this window. TheBHP batcher scrutinizes the BHPs, by looking at theirDBs arrival times and durations to determine thechannel window the DBs are supposed to be transmittedwithin. The BHP batcher then puts the BHP in anappropriate basket as shown in Fig. 2; each basketrepresents each channel time window (i.e., it is a one-to-one mapping between a channel time window and abasket.). For each basket, BHPs may be sorted, e.g., bytheir data burst arrival times. This results in assistingBHP classification and channel assignment for multiple-channel scheduling systems. Once the closing time ofeach channel window is reached, the correspondingbasket will be removed from the BHP grouper and noother BHPs will be eligible for scheduling considerationin that window.

(B) Classifier with channel assignment module: A sche-matic diagram of the classifier with channel assignmentmodule is depicted in Fig. 3. It performs channel (orwavelength) allocation and/or service differentiation. Ser-vice differentiation can be provided, for examples, byemploying a priority scheme, a weighted round robindiscipline, a weighted fair queue, or a pre-assignedproportion of BHPs from each class. After being grouped,BHPs are classified into classes and placed in theappropriate queues. In case of a single class, there is only

a single queue. The channel (wavelength) assignmentmodule then hands over BHPs to the appropriate channelschedulers. This is the place where channel managementleads to efficient resource utilization and service differentia-tion. In the single channel case there is only one channelscheduler and the channel assignment task is reduced toallocation of bursts, of various classes, on this channel.

(C) Channel scheduler module: The channel schedulermodule schedules BHPs (based on a specification of thestart and end times of their corresponding DBs). Theprimary objective is to maximize the number of BHPs(and, accordingly, DBs) to be scheduled in the channeltime window. For a set of BHPs/DBs, the scheduler firstestablishes interval representation profile, as shown by theexample in Fig. 4. The figure shows seven DBs withvarious start times and durations. Once the intervalrepresentation profile is created, it is transformed to aninterval graph. Fig. 5 depicts the interval graph for theexample of Fig. 4. Each vertex in the graph represents aburst. There exists an edge to connect two vertices if, andonly if, their corresponding bursts are overlapped,otherwise there is no edge between these vertices. Forexample, DBs 1 and 2 are overlapped, there is thus anedge connecting vertices 1 and 2 in Fig. 5. On the otherhand, there is no edge between vertices 4 and 5 since theseDBs do not overlap. Based on the interval graph, thechannel scheduler applies an appropriate algorithm toobtain the set of maximum number of non-overlappingintervals (under whatever desired constraints). Forexample, there are two possible sets of maximum non-overlapping DBs. The first set is {3, 4, 5} and the other setis {1, 3, 4}. The channel scheduler can choose either batchdepending on the criteria deployed in the algorithm.

4. Scheduling system operation

As mentioned earlier, the channel time (as the channelresource) is partitioned into a sequence of equal time

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BHPBasket

(collection

Wavelength Assignment

Module

Data Scheduler 2

Data Scheduler N

QueueClass 1

QueueClass 2

QueueClass N

Data Scheduler 1

Fig. 3. Classifier with channel assignment model.

1234567

Fig. 4. Interval representation.

1

4

2

7

6

5

3

Fig. 5. Interval graph.

A.K. Garg, R.S. Kaler / Optik 121 (2010) 793–799796

windows. During each window, a core node (the BHPbatcher therein) keeps collecting BHPs arriving overthe control channel. To be eligible for being scheduled inthis time window, the BHP must arrive before itsclosing time. Typically, the window closing time is wellbefore of the actual channel time during which DBswould be transmitted (taking into considerationother processing times such as scheduling time). Thesequence of processes as seen by a core node for a single-channel scheduling window involves: First, BHPs arebatched. Then, classification with channel assignmenttakes place. Once the data channel and a class of serviceare identified, BHPs are scheduled and a set of newBHPs are forwarded to the downstream. Finally, whenDBs arrive, they are forwarded according to theschedule plan (The scheduling must finish by someamount of time before the DBs forwarding processactually starts).

4.1. Proposed scheduling algorithm

An efficient scheduling algorithm should be able to fit a

new reservation period into an existing void interval

whenever possible to increase the bandwidth utilization

and decrease the data loss rate. The interval-scheduling isan algorithm to be carried out by the channel schedulerto schedule DBs onto a channel. The basic task of thisalgorithm is to create an interval graph out of the set ofDBs delivered by the classification and channel assign-ment module. The channel scheduler then uses thisgraph to obtain the maximum number of bursts that canbe scheduled. To achieve the goal, the schedulerleverages the unique properties of an interval graph inorder to find a clique of maximum size in graph i.e.,maximum stable set of the graph. A clique in anundirected graph G ¼ (V, E) is a subset V 0 � V ofvertices, each pair of which is connected by an edge in E.The clique problem is an optimization problem offinding a clique of maximum size in graph. Themaximum clique problem (MCP) is a hard combinator-ial problem, classified as NP-Complete. The MCP hasmany practical applications in science and engineering.These include project selection, classification, faulttolerance, coding, computer vision, economics, informa-tion retrieval and signal transmission. A major applica-tion of the MCP occurs in the area of coding theory[8–10]. A maximum clique is a maximal clique that hasthe maximum cardinality. No polynomial-time algo-rithm has yet been discovered for an NP-Completeproblem, nor has any one yet been able to prove a superpolynomial-time lower bound for any of them. Themain goal of this algorithm is to find a clique ofmaximum size in graph using a verification andelimination method. If the algorithm gives an outputthen it will be the maximum size clique in that givengraph, but it is not possible to provide an upper boundfor the time it will take. Hence the algorithm is heuristic.The Heuristic interval scheduling algorithm is usedto find a clique of maximum size in graph (as shownin Fig. 6). In a graph of size N, there are exactly NCK

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Fig. 6. Heuristic interval scheduling algorithm.

A.K. Garg, R.S. Kaler / Optik 121 (2010) 793–799 797

sub-graphs of size K. So the total number of sub-graphsin graph G is given by:

G ¼XN

K¼1

NCK ¼ 2N (1)

This is exponential. So verifying all of the sub-graphswill take a long time, because number of verificationsrequired is not a polynomial in N. For decreasing thetime required, some of the verifications can be avoided.The proposed algorithm uses the following importantproperties of clique.

�

Every graph contains at least one clique. � In a clique of size M, all the vertices have the degree

M�1.

�

If the maximum size of any vertex is M, there cannotbe a clique of size 4M+1. � If there is a clique of size K there are cliques of any

sizeoK in the same graph.

� Conversely, if there is no clique of size K, there will

not be a clique of size 4K.

4.2. Description of the algorithm

The number of vertices in the clique is known as thesize, N of the clique. There are three procedures knownas FINDCLIQUE, SELECT and ISCLIQUE areused in the algorithm. First one calls the secondalgorithm and second in turn calls the third. The outputwill be the maximum clique. The different steps of theFINDCLIQUE algorithm are summarized as follows:

(1)

It finds the maximum degree m, sets lb to 0, ub to mand (lb+ub)/2 to mid.

(2)

Check whether there is a clique of size mid+1� If it is there, no need to verify the sub-graphs of

sizepmid and hence sets lb to mid+1.� If it is not there no need to consider the sub-

graphs with size 4 mid.Hence sets ub to mid�1.

(3)

If lbpub, sets (lb+ub)/2 to mid and repeats thestep (2).

(4)

The clique, which is found just before when lbbecomes 4 ub is the clique of maximum size in thegiven graph G.

(5)

Print the size and vertices in that clique.

The checking of the existence of the clique of size mid+1 (step (2)) is made by the SELECT algorithm. Thisalgorithm finds the different combinations of theselected vertices and calls the algorithm ISCLIQUE

for checking whether that combination of the verticesform a clique or not. If the degree of every vertex, in theinduced sub-graph by that combination is equal to mid,ISCLIQUE returns TRUE.

4.3. Time complexity of the algorithm

The algorithm FINDCLIQUE works similar to thatof binary search. For maximum degree M, the timecomplexity of the algorithm FINDCLIQUE is O (log2(M)), which is polynomial. If the size of the sub-graph tobe verified is K, the time complexity of the algorithmISCLIQUE is O (K2), which is again polynomial. Thealgorithm SELECT is based on combinations. In worstcase, all the NCK combinations may have to be verified.But whenever a clique is found, the algorithm willreturn, neglecting the remaining combinations. So,average time complexity of this algorithm is practicallypolynomial.

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2

1

3

4

5

6

78

10

9

11 12

13

14

Fig. 7. NSFNET with 14 nodes.

Mean Offered Load0.2

Bur

st L

oss

Pro

babi

lity

0.1

0.2

0.3

0.4

0.5JIT-SchedulingHeuristic Interval Scheduling

0.4 0.6 0.8 1.0

Fig. 8. Burst loss probability for exponential inter-arrival time

and burst length.

Mean Offered Load0.2

Cha

nnel

Util

izat

ion

0.0

0.1

0.2

0.3

0.4

0.5Heuristic Interval SchedulingJIT-Scheduling

0.4 0.6 0.8 1.0

Fig. 9. Channel utilization for exponential inter-arrival time

and burst length.

x 0.8

1.0

Heuristic Interval SchedulingJIT-Scheduling

A.K. Garg, R.S. Kaler / Optik 121 (2010) 793–799798

5. Performance evaluation

The performance of the proposed scheme has beenevaluated using NS-2 simulator [11] on the NSF14-Nodes network (as shown in Fig. 7). Assume that eachnode is composed of both an edge router and a corerouter and each link has some data channel and onecontrol channel and the transmission rate on eachchannel is 10Gbps. At each edge router, the aggregatepacket arrival process is superimposed by independentON/OFF source. ON and OFF periods are exponen-tially distributed and the minimum length of ON periodis 1 and that of OFF period is 0. The length of bursts is afixed value of 15 000 bytes. An immediate schedulingscheme (JIT/JET-like scheduling with a single channel[7]), the counterpart, is used as a baseline schemerepresenting one of the best currently known schemes. Achannel bit-rate is 10Gb/s with scheduling time windowto 200 ms. Performance metrics such as: burst lossprobability, channel utilization, fairness-index and datathroughput have been studied.

5.1. Simulation parameters

Inde

�

- 0.6 Wavelengths ¼ 3–12 per fiber

ss

� e Control burst processing time ¼ 2.5–4 ms

irn 0.4

� Switching time ¼ 12 ms

Fa

� 0.2 Propagation delay on a link ¼ 0.2–1ms

Input Load0.0

0.00.2 0.4 0.6 0.8 1.0

Fig. 10. Fairness index vs. load.

6. Results

Fig. 8 compares the burst-loss probability of theproposed scheduling with that of the JIT-scheduling.Both probability distributions of the source ‘‘on’’ and‘‘off’’ states are exponentially distributed. The resultsshow that the proposed scheduling outperforms the JITscheduling over the entire range of the mean offeredload. The gain increases slightly with load. Upto 9%improvement in burst loss probability can be achieved,particularly in the full load to overload region.

Fig. 9 shows a comparison between the two schemesin regards to channel utilization. Both the ‘‘on’’ and‘‘off’’ states are exponential distributed. OBS proposed

scheduling performs better also in terms of channelutilization. The improvement is close to 8.9% over theentire range of mean offered load from 0.1 to 0.8. Anadvantage of the proposed OBS proposed schedulingover the JIT-scheduling is shown, specifically at highloads, i.e., from 0.8 to 1.2.

Fig. 10 shows that the proposed scheme can keepFairness Index at about 1.0, vise versa. Through Fig. 11,it is clear that better data throughput performance hasbeen obtained by the proposed scheme.

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Input Load0.0

Thro

ughp

ut

0.0

0.2

0.4

0.6

0.8

1.0Heuristic Interval SchedulingJIT-Scheduling

0.2 0.4 0.6 0.8 1.0

Fig. 11. Throughput vs. load.

A.K. Garg, R.S. Kaler / Optik 121 (2010) 793–799 799

7. Conclusions

A novel burst scheduling approach has been proposedfor optimizing channel utilization in OBS networks. Inthis approach, DBs are scheduled in batches. Theproblem of DB scheduling is mapped to a combinatorialoptimization problem of scheduling DB time intervalson a channel time line. The heuristic interval schedulingalgorithm is utilized to obtain the maximum number ofnon-overlapping bursts. Performance comparison ofproposed scheme with existing scheduling schemes hasbeen performed. The results showed the advantages ofproposed scheme in terms of burst loss probability,channel utilization, fairness-control and data through-put. In future, the impact of network dimensioning andadaptive flow of bursts shall be performed with theproposed approach.

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