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 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.

Test Machine Configuration

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

CPU Usage vs. Power

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

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

Performance vs. Energy

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

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

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 block size of 128KB

Power Consumption of Disks

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.

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

Analyzing Power Consumption

Memory bus utilization

Hashjoin Operator

Sort Operator

Scan Operator

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

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

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

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

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

Questions?