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Analyzing the Energy Efficiency of a Database Server

Analyzing the Energy Efficiency of a Database Server . Hanskamal Patel SE 521. Article. Analyzing the Energy Efficiency of a Database Server Dimitris Tsirogiannis – University of Toronto Stavros Harizopoulos – HP Labs Mehul A. Shah – HP Labs. Introduction .

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Analyzing the Energy Efficiency of a Database Server

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  1. Analyzing the Energy Efficiency of a Database Server Hanskamal Patel SE 521

  2. Article • Analyzing the Energy Efficiency of a Database Server • DimitrisTsirogiannis– University of Toronto • Stavros Harizopoulos– HP Labs • Mehul A. Shah – HP Labs

  3. Introduction • Evaluating database system in terms of performance is measured in task per second or queries per second. • Similarly, energy-efficiency is determined by the measure of completed task per energy/Queries per Joule. • Improving performance is hardware/platform oriented or workload-management oriented. • Exploring ways to improve energy efficiency of a single-machine database server.

  4. Test Machine Configuration

  5. Power Breakdown • About half of the peak power is idle system • Two CPU’s • Fixed RAM Power • Board components • SDD and HDD Minimal Power • Left side of the chart is active power consumption • CPU is dominant component • SSD and HDD draw similar power

  6. CPU Usage vs. Power

  7. What affects energy efficiency? • EE = Work/Energy = Performance/Power • Several options affect power-use and potentially affect energy efficiency • CPU cycles to fetch data from disk • Scans, record access, compressions, sorting, and joining • Energy efficiency can be improved but it may sacrifice performance

  8. Energy efficiency vs. Performance • Experimented with five different overhead kernels • Parallel performing, cache-conscious hash join, sorting, alphasort and parallel merging • High performance storage engine that supports column and row oriented database scans. • PostgreSQL and System-X DBMS

  9. Performance vs. Energy

  10. Performance vs. Energy

  11. Assembling data-management architectures • Scale-up • Shared memory and shared disk • Choosing the balance of components and power down unneeded resources • Scale-out • Share nothing • Single node configurations connected by scaled network • Choose energy efficient components for one node and performance optimized for another

  12. Power Profiles of Hardware Components • RAM • RAM is responsible for 20% of the power consumption and stays the same throughout • Only way to vary power usage by memory is to physically remove the modules from the board

  13. Power Profiles of Hardware Components • Disks • Both HDD and SSD in the configuration • Supports active and idle stages, consuming different amount of power – 15% in the active stage • Test Configuration • Raid-0 configuration for both HDD and HDD • Reading 100GB file @ block size of 128KB

  14. Power Consumption of Disks

  15. Power Profiles of Hardware Components • CPU • The two CPU’s are responsible for the 85% of power increase in the system while active • Interested in understanding: • How CPU power is affected by database operations and the efficacy of hardware and software power management • Developed a set of micro-benchmarks that performs three classes of database operations: hashing, sorting, and scans.

  16. Micro-benchmarks • Custom Join Kernel • Hash join algorithm for computing join of two relations in parallel. • Sort Kernel • Two in-memory parallel sorting algorithm • Scan kernel • Scan uncompressed rows in memory • Scan compressed column on disk

  17. Analyzing Power Consumption

  18. Memory bus utilization

  19. Hashjoin Operator

  20. Sort Operator

  21. Scan Operator

  22. Energy vs. Performance • Parameters that have greatest impact on energy • Algorithm/plan selection • Intra-operator parallelism • Inter-query parallelism

  23. Algorithm/Plan selection • Access Methods • Join Algorithms • Complex Queries and Join Ordering

  24. Intra-operator and Inter-query Parallelism • Intra-operator parallelism • Parallel hash join • Parallel Sorts • Inter-query parallelism • Executing multiple queries at the same time

  25. Implications for Database Computing • One size fits all • Collection of nodes, where each node is optimized for specific task • High parallelism, low-frequency, small cache, and simple design CPU • Solid state drives • Shared nothing, everything, or in-between • Shared nothing and shared disk • Controlling peak power

  26. Conclusion • CPU power usage by different operators can vary by up to 60% • The best performing system was the most energy efficient • Future investigations: • Improving resources across unutilized nodes to save power • Alternative energy efficient hardware for lower fixed-power cost

  27. Questions?

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