agent-based federated hybrid cloud prof. yue-shan chang distributed & mobile computing lab....
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Agent-based Federated Hybrid Cloud
Prof. Yue-Shan ChangDistributed & Mobile Computing Lab.
Dept. of Computer Science & Information EngineeringNational Taipei University
• Cloud computing: – The evolution and convergence of computing trends– Layers
• SaaS: Software As A Service• PaaS: Platform As A Service• IaaS: Infrastructure As A Service
Introduction
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Introduction
• Types– Private Cloud
• each enterprise’s IT platform has their own network, servers and storage hardware (Data Centers)
– Public Cloud• User can obtain any service and resource from service
provider• pay-per-use charging model
Introduction
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• Which one is suitable? – Considering Issues
• Cost (Construction, Operation, Maintenance, Tax …)• Security (Data, Network, …)• Flexibility & Convenience (Operation, Maintenance, Management, …)• Reliability & Availability• Performance
• Main benefits of using a public cloud service: – Easy and inexpensive set-up because hardware,
application and bandwidth costs are covered by the provider.
– Scalability to meet needs. – No wasted resources because you pay for what
you use.
Introduction
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Introduction
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What kind of cloud do I
need?Private?
Public?
• A hybrid cloud – is a cloud computing
environment in which an organization provides and manages some resources in-house and has others provided externally.
Introduction
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HiCloudHiCloud
Amazon Amazon
GoogleGoogle
Public CloudPrivate Cloud
• Effectively utilize public cloud resource is an important issue while adopting hybrid cloud– what kind of jobs need to be dispatched or be
migrated to public cloud? – When does a job be dispatched to public cloud? – And how will a job be dispatched to public cloud?
Introduction
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• Hybrid Cloud Project– ITRI Cloud Center– f5 Hybrid Cloud Architecture
• http://www.f5.com/pdf/solution-center/vmware-vcloud-director.pdf
– Fujitsu Hybrid Cloud• Mikio Funahashi, Shigeo Yoshikawa “Fujitsu’s Approach to
Hybrid Cloud Systems,” Fujitsu Sci. Tech. J., Jul. 2011, Vol. 47, No.3, pp. 285-292
– IBM Hybrid Cloud • IBM Service Management Extensions for Hybrid Cloud• http://public.dhe.ibm.com/common/ssi/ecm/en/
ibd03004usen/IBD03004USEN.PDF
Introduction
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• ITRI Hybrid Cloud Architecture
Introduction
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Public CloudPrivate Cloud
• f5 Hybrid Cloud Architecture – http://www.f5.com/pdf/solution-center/vmware-vcloud-director.pdf
Introduction
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• Fujitsu’s Approach to Hybrid Cloud Systems– Mikio Funahashi, Shigeo Yoshikawa “Fujitsu’s Approach to Hybrid Cloud Systems,” Fujitsu Sci. Tech. J.,
Jul. 2011, Vol. 47, No.3, pp. 285-292
Introduction
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• Agent & Grid Computing– Ian Foster addressed that agent technology and
grid computing need each other because agent technology can enhance the ability of problem solving of grid.
• Agent & Cloud computing– More and more research adopting agent
technology to solve problems faced in the cloud
Introduction
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• Propose an automatic, intelligent framework based on agent technology.
• A federated layer to tie private and public cloud.
• Mobile agent technique is exploited – manage all resources, – monitor system behaviour, – negotiate all actions
Introduction
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• Objective– For performance issue
• Load balance
– For cost issue• utilize private cloud as much as possible• if private cloud cannot complete user’s job before
deadline (Deadline-constraint Job)– dispatch the job to public cloud
» minimize the required resource of the VM
Introduction
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Agent-based Federated Broker
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Agent-based Federated Broker
• Five major components– System Monitoring Agent (SMA)
• Collects the system information
– Reconfiguration Decision Agent (RDA)• Reconfigure and adjust the cloud environment.
– Service Dispatching Agent (SDA) • assign a location in the cloud that allows the job to be
executed on. • if some clusters are overloading, SDA will notify some JAs to
migrate to some other cluster, to balance the load.
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Agent-based Federated Broker
– Cluster Management Agent (CMA) • schedules jobs locally in a FCFS fashion, so that there is only
one job is executing on the cluster. • reports the status of the cluster• collects the information and send it via heartbeats to SeMA.
– Job Agent (JA) • encapsulates a job, the job can be migrated along with the
JA. • executes and monitors the job on the cluster. • reports the job status to the CMA periodically. • brings the results back to the private cloud.
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• Job dispatching – Pack a job into Job Agent(JA)– dispatching JA to destination– Unpack the JA
Agent-based Federated Broker
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• Job Count (JC), –
• Tc: Job count threshold
• the SMA will pick up the (JCPU +TC+1)th job from job queue of private cloud, and trigger it to be migrated.
• For example, if the JCPR is equal to 10, the JCPU is equal to 4, and the TC is equal to 2. Therefore, the 7th job will be migrated to public cloud.
Policy of Job Dispatching to Public Cloud
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CPUPR T-JCJC
• total Size of Job (SJ),–
• the SMA will pick up the th job from the job queue of private cloud, and trigger it to be migrated.
• For example, if the total size of job in public cloud is 10Mbytes, the TS is equal to 2Mbytes, and the size of jobs in private cloud are 3, 4, 3, 3, 2, 3, 4 Mbytes respectively. The 5th job (2Mbytes) will be migrated to public cloud because the (3+3+4+3); so that the 5th job will be migrated.
Policy of Job Dispatching to Public Cloud
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• Estimated Finish Time (EFT)–
• the SMA will pick the th job in the queue of private cloud, and trigger it to be migrated.
• For example, if the total finish time of jobs in public cloud is 100s, the TT is equal to 20s, and the finish time of jobs in private cloud are 33, 24, 45, 43, 22, 37, 24 second respectively. The 5th job (22s of finish time) will be migrated to public cloud
– Rough Set Theory
Policy of Job Migration
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• Agent Platform for the hybrid cloud
Prototyping and evaluation
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• Job migrated
Prototyping and evaluation
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• Comparison between with migration and without migration
Evaluation
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• Comparison between job count and total size of job
Evaluation
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• an agent-based automatic intelligent job migration framework on a hybrid cloud is proposed.– built a prototype that integrating our private cloud
with public cloud.
• We demonstrate the job migration mechanism on Hadoop platform– it shows that the framework can be applied to
hybrid cloud and work well.
Summary
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Execution Time Prediction Using Rough Set Theory in
Hybrid Cloud
Introduction
• Resource utilization is important issue in cloud computing– Could the remaining resource in private cloud serve
the incoming task and complete the task before deadline?
– If not, the incoming task need to be dispatched to public cloud.
• How much resource we need to preserve to serve the deadline-constraint task in public cloud?
• For the remaining resource , the execution time prediction of a task becomes an important issue in hybrid cloud.
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Introduction
• Exploit Rough Set Theory (RST) to predict job's execution time in the hybrid cloud environment. – RST is a well-known prediction technique that uses
the historical data to predict the attribute value of an object.
– We propose an execution time prediction algorithm based on RST to schedule jobs
• The evaluation show that the RST can be utilized to accurately predict the execution time while historical data is increasingly.
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RST-based Prediction
• Rough Set Theory (RST)– have been witnessed that is a useful prediction technique
based on historical data in a variety of applications,• such as quantitative structure–activity relationship in the
Chemistry and data mining . – It provides an appropriate theory for identifying good
“similarity templates”. • The primary objective of similarity templates is to identify
characteristics of applications that define similarity.
• Two prediction phases– Inference rule deducing phase – Estimation phase
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RST-based Prediction
• Inference rule deducing phase – Steps (detailed methodology of RST can refer to [2])
• Define all attributes; including condition attributes (CA) and decision attributes (DA).
• Discretize the properties of historical records for diversified attributes.
• Calculate D-Reducts– Utilize discernibility matrix to list all properties, – apply discernibility function to formulate the relation of the
properties, – and then simplify the formulation using boolean algebra.
• Derive the inference rule of DA. .
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RST-based Prediction
• Define all attributes
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Decision AttributeConditional Attributes
RST-based Prediction
• Discretize the properties of historical records
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RST-based Prediction
• Calculate D-Reducts and D-Core– Generate discernibility matrix
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RST-based Prediction
• Calculate D-Reducts and D-Core– Formulate discernibility function: fA(D)
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Both {a1, a3} and {a2,a3 } are D-Reducts, {a3} is D-core
RST-based Prediction
• Calculate D-Reducts and D-Core– formulate the relation of the properties, and
simplify the formulation
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f2(D)=a1, f3(D)=a1+a3 , f4(D)=a1+a3 , …
RST-based Prediction
• Deduce Inference Rule (- : means don’t care)– a1=2 -> d=2
– a1=3-> d=1
– a3=4 -> d=4
– a1=1 and a3=2
-> d=2
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RST-based Prediction
• Estimation Phase– Apply simple mathematical operation, such as arithmetic
average of the value of DA, to obtain the final value of the DA.» Estimated time = (job3+job5+job6)/3
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Element Processor Speed
Input size Execution time
3 5 2 480
5 5 2 500
6 5 2 505
The new job 5 2 ?
Prototyping and Evaluation
• Prototype the system using the agent platform JADE v4.0
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Prototyping and Evaluation
• two jobs are submitted to the system– Compute π– Area Approximation
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Prototyping and Evaluation
• The Error Rate– Positive->over
prediction, – Negative->under
predicted. • Vibration during the
first 25 jobs. • lack of the historical data
that can be used to predict the job.
– The more the historical data are stored, the more accurate the prediction will be.
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-0.5
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Compute π
Area Approximation
Job #
Err
or R
ate
Prototyping and Evaluation
• Absolute Error Rate. – shows how much
improvement has the prediction made.
• The higher the absolute error is, the more improvement is needed.
– for 2 kinds of jobs with 200 submissions are 0.2008 and 0.0615.
– the accuracy is very impressive if remove the first 25 predictions
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Compute π
Area Approximation
Job #
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olut
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rror
Rat
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Prototyping and Evaluation
• The largest prediction latency is 642.91 ms with 190 jobs– is acceptable.
• no new record to be updated, the prediction time taken can be less than 1 ms.
• generating the decision rule needs much more time than just predicting the value.
• To reduce the time of predicting, – periodically updating the
decision rules can be considered.
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No. of job in history
estimated time
Estimation #
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Summary
• we utilized the RST to predict the execution time in hybrid cloud.
• The result shows that RST-based predictor can predict the execution time of a job – error rate under 0.1 when the number of historical job is
over 50. – When more records available, the error rate can drop
under 0.03. • Latency is reasonable,
– less than 1 second with 190 historical records to perform a full prediction. The system can aid users to schedule their jobs faster and more accurate.
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