AutonomicProvisioningandApplicationMappingonSpotCloudResources

DanielJ.Dubois,Giuliano Casale2015IEEEInternationalConferenceonCloudandAutonomicComputing

(ICCAC),Cambridge,Massachusetts,September21-25,2015

SummarizedbyEniyeTebekaemi

Outline

• SpotInstancePricingModels• ProblemDescription• SystemModel• ProposedSolution• ExperimentalResults• Discussions

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SpotInstancePricingModel

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• Spot instances enable you to bid on unused EC2 instances, whichcan lower your Amazon EC2 costs significantly.

• The hourly price for a Spot instance (of each instance type in eachAvailability Zone) is set by Amazon EC2, and fluctuates dependingon the supply of and demand for Spot instances.

• Your Spot instance runs whenever your bid exceeds the currentmarket price.

• Spot instances are a cost-effective choice if you can be flexibleabout when your applications run and if your applications can beinterrupted.

http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-spot-instances.html

ProblemDescription

• To take maximum advantage fromspot instances users needs to makethe following decisions:• What type of virtual resources should berented for a given application?

• How to efficiently map the components ofan application (e.g., web server VMs, adatabase VMs) to the rented resources?

• What is the lowest bid price that stillallows to fulfill quality of servicerequirements?

• Users maybe SaaS providers ororganizations running theirapplications and web services onthe cloud.

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MajorContributions• Polynomial-time heuristic to jointly solve the bidding and allocationproblem, which are in general NP-hard;

• Bidding in extended queueing network models that include a model ofthe operational environment, which captures the stochastic nature ofthe operational environment, in which VMs can be suddenly lost as aresult of spot price fluctuations.

• The use of advanced fluid analysis techniques to accuratelyapproximate response time percentiles, which are commonly used toconstraint performance in service- level agreements.

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SystemModel – Application

Application:• Closed queuing networkQN of M software servers.

• A delay node representinguser think time

• K classes of requests• Set of constraints on theresponse time defined asthe SLO

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The Proposed systemmodel consist of 2 parts: application and resources.

Parameters

SystemModel – Application

Resources:

• R + 1 available resourcetypes. Type 0 is a specialvirtual type used torepresent unallocatedresources that have zeroprice and zero rate• Each resource ischaracterized by a certainrate (processing speed)and a certain number ofprocessors

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The Proposed systemmodel consist of 2 parts: application and resources.Parameters

SystemModel – DecisionVariableandProblemStatement

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A formalization of the optimization problem is the following:

Our goal is to:• Determine the rental and allocation

policies, which consist in the amount ofcomputational resources to be rentedfrom a cloud provider, the mapping ofthe various application components tothese resources, and finally the bid pricefor each resource.

• Decide a resource assignment vector 𝑡and an allocation matrix (𝑑#,% ) thatminimize the sum of the prices of allrented resources

ProposedSolution

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StateDiagramshowingallthestepsinthedecision-makingapproach

1. Choosetheminimumcomputationalrequirementsforeachapplicationcomponent.

2. Choosetheresourcestorent– calculatethebiddingpricethatguaranteesaminimumavailabilitylevelfortheresources.

3. Choosetheallocationoftheapplicationcomponentstotheresources

4. Analyzetheoverallsystemandpossiblescaling-upofbottlenecks.

ProposedSolution – Finding theoptimalrateforeachsoftwareserver

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• The minimum computational requirements interms of resource rates that are needed byeach software server to satisfy the SLO.

• Assume that each software server is deployedon a dedicated hypothetical resource

• Each resource provides minimum rate toprocess request such that the SLO is satisfied.

• The goal of this optimization problem is todecide the minimal rates 𝜇'# such:

ProposedSolution – Findingtherealresourcestorent

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• Decide which real resources to rent toprovide the required computationalneeds at minimal expense.

• We consider for each real resource 𝑦 amean price equal to �̂�%, that can beobtained from historical traces given acertain level of desired availability

• The goal is to minimize the sum of thesecosts while ensuring that the rates of allrented resources are large enough toallocate the rates found as the solution ofthe previous problem.

ProposedSolution – Findingtheallocationoftherateforeachsoftwareservertotherealresources

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• Find a good allocation of each softwareserver to the rented resources based ontheir rates

• Can allocate multiple software server toa single resource, or replicate a singlesoftware server over multiple resources.

• The goal is to minimize the overheaddue to load balancing by minimizing thenumber of associations 𝑎#,% betweensoftware servers such that

ProposedSolution – Systemanalysisandscaling-upofthebottleneckserver

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• Check if the SLO constraints still hold whenconsidering the system allocated using theresource assignment vector 𝑡 and theallocation matrix 𝐷 found in the previoussteps

• If SLO constraints still holds then applysolution

• If the SLO constraints do not hold anymorethen find the bottleneck server𝑚∗

• Scale up the bottleneck software serverrates by a factor𝛼

• Go back to step 2

Evaluation – Settings

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HardwareandSoftware

• 2.5GHzIntelCorei7quad-coreprocessorwith16GBofRAMrunningOSX10.10.3andMATLABR2015a.

• LINEsoftwaretopredicttheresponsetimesofourqueueingnetwork• LINEcancompute

percentilesofresponsetimes,whichareimportantforSLAassessment.

ApplicationModel

• Applicationmodelbasedonpreviousmeasurementsofanindustrialenterpriseresourceplanning(ERP)application,SAPERP.

• Representedasaqueueingnetworkwithexponentiallydistributedservicetimes,

• M=2softwareservers

• K=3classesofrequests

ResourceModel

• AmazonEC2historicalspotpricetracesforeachtypeofresources

• Computesresourceratesaselasticcomputingunit(ECU)

• 1ECU=65.1requests/sec

Evaluation – Parameters

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ExampleofAmazonSpotinstancetraceforWindowsm2.xlargeVM

SAPERPParameters

SpotPricesOfAmazonEC2

Results

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Experiment results when varying the number of users.

Results

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Experiment results when varying SLO. The SLO is a maximum limit on the mean value of the response time calculated for a rental time period 𝑻.

Results

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Experimentresultswhenvaryingtheavailability

Conclusion

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• This paper shows that their approach is able to outperform an exact algorithmthat is based on the MATLAB fmincon interior-point solver.

• Approximate a very complex problem using simple greedy algorithms that arelightweight enough to be used at run-time to support pro- active and reactivesystem adaptation.

• Predict and make decisions also when we have a representation of randomenvironmental parameters such as the possibility for spot resources to be lost