Energy Aware Consolidation Energy Aware Consolidation for Cloud Computingfor Cloud Computing

Srikanaiah, Kansal, ZhaoSrikanaiah, Kansal, Zhao

Usenix HotPower 2008Usenix HotPower 2008

Power & Consolidation IssuesPower & Consolidation Issues

High power consumption even at low load (at High power consumption even at low load (at 10% CPU util, 50% of peak power consumed)10% CPU util, 50% of peak power consumed)

Consolidation is not just bin-packingConsolidation is not just bin-packing– Packing too much might increase “energy used per Packing too much might increase “energy used per

unit service provided”unit service provided”– Consolidation can lead to performance degradationConsolidation can lead to performance degradation– There exists an “optimal” performance vs energy There exists an “optimal” performance vs energy

operating pointoperating point

Experiments for Understanding Experiments for Understanding ConsolidationConsolidation

Experimental Set up

Understanding Consolidation: Understanding Consolidation: Experimental ResultsExperimental Results

•Started with app using 10% CPU, 10% disk•Added workloads with varying CPU-disk utils•Numbers show CPU-disk utilization mix

•Point made: consolidation results in performance degradation

Energy Consumption Energy Consumption per transactionper transaction

U-shaped curve:•At low utilizations idle power is not “amortized effectively”•At high utilizations, energy consumption increases, but throughput degrades hence per tran energy consumption increases

•Observation: There is an optimal combination of CPU and disk utils for this setup (70% CPU, 50% disk)

Consolidation ProblemConsolidation Problem

Why not straightforward multi-dimensional Why not straightforward multi-dimensional bin-packingbin-packing– Performance degradation: resource utilizations Performance degradation: resource utilizations

are additive, but performance measures are not are additive, but performance measures are not modeled at all in bin-packing. Minimizing modeled at all in bin-packing. Minimizing number of bins not equal to minimizing energy number of bins not equal to minimizing energy (implied to mean energy per transaction)(implied to mean energy per transaction)

– Power variation: even if minimum number of Power variation: even if minimum number of severs is used, the allocation of workloads will severs is used, the allocation of workloads will result in varying power usageresult in varying power usage

Consolidation AlgorithmConsolidation Algorithm

Algorithm has to do followingAlgorithm has to do following– Workload “arrives” to host clusterWorkload “arrives” to host cluster– Arriving workload has known CPU-disk Arriving workload has known CPU-disk

utilizationutilization– Arriving workload has to be “assigned” to a host Arriving workload has to be “assigned” to a host

Consolidation AlgorithmConsolidation Algorithm

1.1. Optimal point for each host should be known (details not Optimal point for each host should be known (details not specified!!)specified!!)

Assumes that optimal point is a host characteristic - not Assumes that optimal point is a host characteristic - not dependent on application dependent on application

2.2. For a particular allocation, the “Euclidean distance” from For a particular allocation, the “Euclidean distance” from the optimal point is calculatedthe optimal point is calculated

3.3. Pick allocation which Pick allocation which maximizesmaximizes sum of such Euclidiean sum of such Euclidiean distances of each serverdistances of each server

In authors’ words “This heuristic is based on the intuition that we In authors’ words “This heuristic is based on the intuition that we can use both dimensions of a bin to the fullest (where “full” is can use both dimensions of a bin to the fullest (where “full” is defined as the optimal utilization point) after the current allocation defined as the optimal utilization point) after the current allocation is done, if we are left with maximum empty space in each is done, if we are left with maximum empty space in each dimension after the allocation.”dimension after the allocation.”

Consolidation HeuristicConsolidation Heuristic

EvaluationEvaluation

……EvaluationEvaluation