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On the Impact of Energy-saving Strategies in Opportunistic Grids

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Opportunistic grids are distributed computing
infrastructures that harvest the idle computing cycles of computing resources geographically distributed. In these grids, the demand for resources is typically bursty. During bursts of
resource demand, many grid resources are required, but at other times they remain idle for long periods. If the resources are kept powered on even when they are neither processing
their owners workload nor grid jobs, their exploitation is not efficient in terms of energy consumption. One way to reduce the energy consumed in these idleness periods is to place the
computers that form the grid in a “sleeping” mode which consumes less energy. We evaluated two sleeping strategies, denoted: standby and hibernate. Resources that comprise an
opportunistic grid are normally very heterogeneous, and differ enormously on their processing power and energy consumption. It opens the possibility of implementing scheduling strategies that take energy-efficiency into account. We consider scheduling in two different levels. Firstly, how to choose which machine should be woken up, if several options are available. Secondly, how to decide which tasks to schedule to the available machines. In summary, our results presented a significant reduction in energy consumption, surpassing 80% in a scenario when the amount of resources in the grid was high. Moreover, this comes with limited impact on the response time of the applications.

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On the Impact of Energy-saving Strategies in Opportunistic Grids

  1. 1. On the Impact of Energy-savingOn the Impact of Energy-saving Strategies in Opportunistic GridsStrategies in Opportunistic Grids Lesandro Ponciano, Francisco Brasileiro Federal University of Campina Grande, Brazil Department of Systems and Computing Distributed Systems Lab
  2. 2. 2 OutlineOutline  Introduction  Context  Problem Statement  Related work  Energy Saving in Opportunistic Grids  Evaluation  Results  Conclusions
  3. 3. 3 Opportunistic GridsOpportunistic Grids Harvest the idle computing cycles Suitable for the execution Bag- of-Tasks applications User Interface GridGrid componentscomponents Site Manager Resources
  4. 4. 4 Resources and demand for resourcesResources and demand for resources Resources differ on their processing power and energy consumption – Energy-aware Scheduling Strategies During bursts of resource demand, many grid resources are required, but at other times they remain idle for long periods – Sleeping Strategies
  5. 5. 5 Problem StatementProblem Statement What the impact that energy-saving strategies have on energy- saving and job makespan in opportunistic grids?
  6. 6. 6 Related WorkRelated Work Energy-saving strategies in computers – Better design of hardware and software – Pinheiro et al. (2003), Irani et al. (2003), Augostine et al. (2004) Energy-aware scheduling and Sleeping strategies in grids or clusters – Energy saving and its associated impact on job's deadline in infrastructures with resources reservation – Garg and Buyya(2009), Kim(2007), Sharma and Aggarwal (2009), Orgerie et al. (2008), Lammie et al. (2009)
  7. 7. 7 Idle, Standby, and Hibernate StatesIdle, Standby, and Hibernate States Idle – The machine is powered on, and it is waiting a new instruction Standby – The memory is kept powered on and other components are powered off (e.g.: CPU, Disk) Hibernate – The memory status is saved on the disk, and then the memory and other components are powered off Both Standby and Hibernate allow to wake up the machine by LAN interface (wake-on-LAN)
  8. 8. 8 Idle, Standby, and Hibernate StatesIdle, Standby, and Hibernate States Decrease Power Consumption Increase Latency to wake-up Idle Standby Hibernate
  9. 9. 9 Strategies to save energy inStrategies to save energy in Opportunistic GridsOpportunistic Grids
  10. 10. 10 Two ways to save energyTwo ways to save energy Sleeping strategies save energy at resources idle times – Advanced Configuration and Power Interface (ACPI) Energy-aware scheduling strategies save energy on resources allocation – how to choose which machine should be woken up, if several options are available – how to decide which tasks to schedule to the available machines
  11. 11. 11 Sleeping StrategiesSleeping Strategies P = Power Consumption L = Latency to wake-up
  12. 12. 12 Sleeping StrategiesSleeping Strategies Ph < Ps < Pi e Lh > Ls > Li P = Power Consumption L = Latency to wake-up Sleeping strategies can provide energy savings, but can increase job makespan
  13. 13. 13 Wake-up StrategiesWake-up Strategies • Most Recently Sleeping (MRS) • Energy Aware (EA) Waking up User Interface
  14. 14. 14 Scheduling StrategiesScheduling Strategies • First Come First Served (FCFS) • Fastest Processor to Largest Task (FPLT) • Most Energy-Efficient First (MEEF) • Most Energy-Efficient to Largest Task (MEELT) Scheduling User Interface
  15. 15. 15 EvaluationEvaluation
  16. 16. 16 Recalling the problem statementroblem statement... What is the purpose of this experiment? – The purpose is to analyze the impact that the proposed sleeping, waking up, and scheduling strategies have on energy-saving and job makespan in Opportunistic Grid
  17. 17. 17 MetricsMetrics Job makespan (Mj ): where sj is the job submission time and cj the latest time of completion of any of its tasks Job slowdown (Sj ): where A and B are site configuration Energy Saving: where E is the energy consumed by all machines of the site M j =cj−sj Sj= mj A mj B ξA= EB−EA EB ∗100
  18. 18. 18 GridGrid SimulatorSimulator Simulator based on OurGrid P2P opportunistic Grid
  19. 19. 19 ExperimentalExperimental ConfigurationConfiguration Resources – Up to 360 machines – Power consumption based on TDP – Variations in the machines availability (Kondo et al., 2007) Bag-of-task applications – Tasks CPU-Bound – 11 months of OurGrid trace – Synthetic Workload (Iosup et al., 2008)
  20. 20. 20 ResultsResults
  21. 21. 21 Sleeping StrategiesSleeping Strategies Results – Sleeping strategies can provide substantial energy savings – Hibernate provides greater energy saving than standby when the number of resources provisioned grows Grid Configuration – Sleeping: * – Scheduling: FCFS – Wake-up: EA Relative error bars for a confidence level of 95%
  22. 22. 22 Wake-up StrategiesWake-up Strategies Hibernate Standby Relative error bars for a confidence level of 95% Grid Configuration – Sleeping: * – Scheduling: FCFS – Wake-up: * Results – EA performers better than MRS when the number of resources provisioned grows
  23. 23. 23 Scheduling StrategiesScheduling Strategies Hibernate Standby Relative error bars for a confidence level of 95% Grid Configuration – Sleeping: * – Scheduling: * – Wake-up: EA Results – They have not shown significant difference when compared with each other
  24. 24. 24 SlowdownSlowdown Hibernate Standby Relative error bars for a confidence level of 95% Grid Configuration – Sleeping: * – Scheduling: * – Wake-up: EA Results – Wake-up and scheduling strategies have not impacted significantly on slowdown – Hibernate has presented greater slowdown than standby in all scenarios
  25. 25. 25 ConclusionsConclusions Analyze different strategies considering several aspects of the grid Most of the energy savings comes from the sleeping strategies Energy saving surpass 80% in a scenario when the contention for resources in the grid was low Limited impact on the makespan of the applications
  26. 26. 26 FutureFuture WorkWork P2P Opportunistic Grid Sensitivity analysis of power consumption and latency of sleeping states New strategies to decide when and which machines can go to sleeping state over time
  27. 27. 27 Thank you!Thank you! Lesandro Ponciano lesandrop@lsd.ufcg.edu.br Francisco Brasileiro fubica@dsc.ufcg.edu.br

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