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Automatic Energy-based Scheduling

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Course: Execution Environments for Distributed Computing
Final Presentation (20min): Automatic Energy-based Scheduling

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Automatic Energy-based Scheduling

  1. 1. EEDC 34330 AutomaticExecutionEnvironments for Energy-AwareDistributed SchedulingComputingEuropean Master in Distributed A GREEN ProjectComputing – EMDC Group members: Maria Stylianou – marsty5@gmail.com Georgia Christodoulidou – geochris71@gmail.com
  2. 2. Outline● Problem Statement● Green500 List● Automatic Energy-Aware Scheduling● Conclusions 2
  3. 3. Problem Statement Energy-costs dominate!Performance = Speed ReliabilityBad Effects: Availability Usability → Huge increase in total cost for maintaining a data center 3
  4. 4. The Green500 List● Description● Top10 supercomputers● Trends for energy consumption decrease 4
  5. 5. Description● Started in April 2005● Ranking of the most energy-efficient supercomputers in the world● Aim → Raise awareness to other performance metrics ● Performance per watt ● Energy efficiency for improved reliability → Encourage “greener” supercomputers 5
  6. 6. Top10 SupercomputersRetrieved from http://www.green500.org/lists/2011/11/top/list.php 6
  7. 7. Trends for energy consumption decrease● Aggregate many low power processors● Use energy-efficient accelerators from gaming market No use of automatic energy-based scheduling! 7
  8. 8. Automatic Energy-Aware Scheduling● Problem Restatement● Energy Management Technologies ● How to address the problem ● Server Virtualization ● Additional Help● Whats in the market 8
  9. 9. Problem Restatement● Previously said: Energy-costs dominate!● Peaks are fronted by adding servers → Servers are underutilized “the average server utilization varies between 11% and 50% for workloads from sports, e-commerce, financial, and Internet proxy clusters.” 9
  10. 10. Energy Management Technologies● Awareness ● Energy consumption in data centers ● Substantial carbon footprint Solutions Hardware Level System Level Build energy Manage power efficiency into consumption of components & servers & systems systems design adapting to changing conditions in the workload 10
  11. 11. How to address the problem Power-aware dynamic app placement! This is... Automatic Energy-aware scheduling! 11
  12. 12. Server Virtualization● Appeared in 1960s● Disruptive business model● Aim: Workload consolidation → Reduce the energy costs 12
  13. 13. Server Virtualization● P1: Servers are heavily underutilized → Static consolidation of workloads → Reduction of servers Reference [1] 13
  14. 14. Server Virtualization● P2: Servers are underutilized for long periods/day → Consolidation of workloads → Servers in a low power state Reference [1] 14
  15. 15. Server Virtualization● P3: Low resource utilization of applications● P4: Applications have a complementary resource behavior → Dynamic consolidation of workloads 15
  16. 16. Server Virtualization Scheduling policies ● Random: assigns the tasks randomly → only if the task can fit into a server ● Round Robin: assigns a task to each available node → implies a maximization of the # of resources to a task → implies a sparse usage of the resources ● Backfilling: fills in turned on machines before starting offline ones ● Dynamic Backfilling: able to move tasks between machines → provide a higher consolidation level. 16
  17. 17. Server Virtualization ● Benefits ● More efficient utilization of hardware ● Reduced floor space ● Reduced facilities management costs ● Hide the heterogeneity in server hardware ● Make apps more portable/resilient to hardware changes 17
  18. 18. Additional Help – Hardware Level Cooling ● Automatic Air Cooling ● Water Cooling “water as a coolant has the ability to capture heat about 4,000 times more efficiently than air” ~IBM → Aquasar Supercomputer – IBM Research Zurich Use of powerful chip watercoolers → no need of the water to be chilled in lower temperatures 18
  19. 19. Additional Help – System Level Machine Learning● Scheduling Information → use predictive methods not available to model missing information● Dynamic Backfilling Scheduling Policy 1st step 2nd step → Change static data by estimated data 19
  20. 20. Whats in the market● VMturbo ● Created in 2009 ● Aim: Intelligent Workload Management real-time solution for Cloud & Virtualized environments ● Overall strategy: ● replace manual partitioned management ● with scalable, automated, and unified resource & performance management ● Use of economic techniques for IT resource management ● Economic Scheduling Engine: Dynamically adjust resource allocation 20
  21. 21. Conclusions● Automatic Energy-based scheduling → is a recent area → should be considered more by researchers → should become the target for top10 supercomputers → even better results! → Server Virtualization is an efficient way for reducing energy-costs 21
  22. 22. References1. G. Dasgupta, A. Sharma, A. Verma, A. Neogi, R. Kothari, “Workload Management for Power Efficiency in Virtualized Data Centers”, Communication of the ACM, 54:7, July 2011.2. The Green500, retrieved on 9th May 2012, http://www.green500.org.3. J. Ll. Berral, Í. Goiri, R. Nou, F. Julià, J. Guitart, R. Gavaldà, J. Torres, “Towards energy-aware scheduling in data centers using machine learning”, In Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking, Germany, April 2010.4. IBM builds water-cooled processor for Zurich supercomputer, retrieved on 10th May 2012, http://www.computerweekly.com/feature/IBM-builds-water-cooled-processor-for- Zurich-supercomputer.5. IBMs Water-Cooled Aquasar Supercomputer Uses Waste Heat to Warm Dorms, retrieved on 10th May 2012, http://www.popsci.com/technology/article/2010-04/ibms- water-cooled-supercomputer-could-cut-energy-costs.6. VMturbo: Intelligent Workload Management for Cloud and Virtualized Environments, retrieved on 10th May 2012, http://www.vmturbo.com/.7. Operations Management in the Age of Virtualization, A Vmturbo Whitepaper. 22
  23. 23. EEDC 34330 AutomaticExecutionEnvironments for Energy-AwareDistributed SchedulingComputingEuropean Master in Distributed A GREEN ProjectComputing – EMDC Group members: Maria Stylianou – marsty5@gmail.com Georgia Christodoulidou – geochris71@gmail.com

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