Cloud Computing Energy efficient cloud computing Keke Chen.

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<ul><li><p>Cloud Computing</p><p>Energy efficient cloud computing</p><p>Keke Chen</p></li><li><p>OutlineImpacts of data centers energy consumptionenergy-efficient cloud computingFocus on cloud-sideFocus on scheduling of virtual machines/workloadsDifferent from client-side problems</p></li><li><p>Environment and Energy probleme-wasteCoal is used to generate ~41% of global electricity, ~44% in 2030Coal CO2 environment Computing and cooling system61 billion kWh (kilowatt hours) in 2006, 1.5 percent of total U.S. electricity consumption that yearDoubled from 2000 to 2006</p></li><li><p>Economical impact of energy consumptionPCs electricity bill $7 billion per year + several billions more for displays$18.5 billions for data centers in 2005Increasing trendsServers growing rate: 14% per year in USIncrease per server consumption 16% per yearIncrease in electricity cost 12% per yearPredict: $250 billions worldwide for 2012</p></li><li><p>Existing approachesHardware improvementCircuit design low-power CPUsSleep modeCooling system Power distributionWorkload distribution</p></li><li><p>Major factorsEnergy savingGuaranteed Performance (QoS)TimeMoney </p></li><li><p>Some approaches in detailVM schedulingVM consolidationJob scheduling</p></li><li><p>Power-aware scheduling of VMsPhysical machines have different processor speedAdjustableType of work Monitor VM status to adjust processor speedAllocate new VMs to servers having the required speed, according to the performance requirementweakness: the correlation between performance and energy reduction is not certain</p></li><li><p>VM consolidationDetermine the VMs to be migrated Sorting all VMs in decreasing order of current utilizationAllocate each VM to a host based on a policy of least increase of power consumptionReducing performance degradationMinimizaiton of migrationsHighest potential growthRandom choice</p></li><li><p>Application of machine learning techniqueFor the VM consolidation problemUse ML techniques to reduce the performance degradationPredict SLA/customer satisfaction level of each job before moving them across serversIn general, predictors can be learned for optimizing server power and reducing performance impact</p></li><li><p>Scheduling compute-intensive jobs with unknown service timesProcessor profiles in the clusterSome for performance criticalSome for energy savingTwo queuesEnergy-efficient priority: Energy efficient processors are preferred in schedulingHigh performance priority: performance is preferredScheduling considers energy-efficient queue first </p></li><li><p>Some Research TopicsHeterogeneous workloadsHeterogeneous nodesMatching workloads to nodesResource monitoringLive migration policy</p></li><li><p>Types of workloadWorkload CPU, I/O, Memory, network,Allocating same type of workloads to one node might not be appropriateBetter to mix different types of workloads Need methods for characterizing the workload types</p></li><li><p>Types of nodesNodes in the data center are possibly heterogeneous CPU, disk, memory, network.Different energy profileMatching workloads and nodes</p></li><li><p>Machine learning techniquesConsidering many types of workloads, and types of nodesFinding optimal matching is not trivial </p></li><li><p>Resource monitoringEnergy consumptionNode performance </p><p>Important measures for real-time decisions</p></li><li><p>Overhead of live migrationMigration process consumes a large amount of energyData center may span multiple physical locationsShould avoid continuous workload movements smarter policies are needed</p><p>**</p></li></ul>

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