Energy Efficient Web Server Cluster Andrew Krioukov, Sara Alspaugh, Laura Keys, David Culler, Randy Katz.

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Presentation transcript:

Energy Efficient Web Server Cluster Andrew Krioukov, Sara Alspaugh, Laura Keys, David Culler, Randy Katz

Doubling in 5 years (EPA Report on Server and Data Center Energy Efficiency, 2007) $7.2 billion Energy consumption in data centers

Web Applications Database / SAN Web App Web Server Frontend /Load Balancer Web Server Web App Clients

Core i7 50% Idle Power

Atom 80% Idle Power

Server energy consumption Idle Sleep / Off Active

Server energy efficiency Percent Efficiency Energy Efficiency = Work / Energy

Power Proportional Server

Problem Servers are energy efficient at high utilization Typical server utilization is low – Google: average server utilization 30%

Google CPU Utilization The Case for Energy-Proportional Computing Luiz Barroso, Urs Holzle ,000 servers at Google during a six- month period

Solutions Make servers power proportional – Requires fixing hardware & software Make power proportional cluster – Run nodes at high utilization or “off” – Consolidate workload

Web Servers Stateless Short requests Requests can be served by multiple machines Large variation in load

Web Server Load ISP web server trace from Internet Traffic Archive

Cluster Architecture

Atom Nodes Intel Atom 330 with 945CG chipset 1.6 GHz, 2 cores CPU spec sheet TDP: 8W Chipset spec sheet TDP: 22.2W

Atom Nodes Power states: – Active – Idle: CPU enters C-states – Sleep: Suspend to RAM – Off Power (Watts)Time to Resume (seconds) Active22 – 24 W - Idle22.08 W0 s Sleep1.6 W2.5 s Off0 W61 s

Node Performance Max request rate

Scheduler Algorithm Keep awake desired_servers Put servers to sleep after a timeout

Evaluation Httperf workload generator Synthetic workload – Request files in Zipf distribution – Ramp request rate up and down Working on using real web server traces

Throughput

Energy Savings Simple Load BalancerPower Aware Cluster Manager

Load per Server

Future Work Heterogeneous hardware – Small nodes for low utilization Adjust to changes in request types – Dynamic vs. static requests – Adjust max requests per server

Questions

Adjust to request types

Power vs. server cost In the data center, power and cooling costs more than the IT equipment it supports Christian L. Belady, HP 2007

Saving Energy Turn off unused resources – Use lower states Improve power in states Active Idle Sleep Power Off