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

2. Article Analyzing the Energy Efficiency of a Database Server – Dimitris Tsirogiannis – 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

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?