Adaptive performance control of computing systems PowerPoint Presentation, PPT - DocSlides

Adaptive performance control of computing systems via distributed cooperative control: application to power management in computing clusters

Authors:

Mianyu

Wang,

Nagarajan

Kandasamy

,

Allon

Guez

, and Moshe

Kam

Proceedings of the 3

rd

International Conference on Autonomic Computing, ICAC 2006, Dublin, Ireland

Presenter:

Ramya

Pradhan

,

Fall 2012, UCF.

OutlineResearch problemProposed solutionEvaluation of proposed solutionStrengths

Limitations

Proposed extensions

Research Problem

Server cluster

Clients

Power

Consumption

How to balance power consumption with

time-varying workload and

QoS

?

Proposed solutionFully decentralized and

cooperative control framework

using optimal control theory

balance

cluster operating frequency

and

average response time

scalable due to problem decompositionfault-tolerant due to cooperative controlno intra-cluster communication

Proposed solution using optimal controlOptimal control

“uses

predictive approach

that generates

sequence of control inputs

over a specified

lookahead horizon

while estimating changes in operating conditions.”System ModelSystem state: queue sizeConstrained control input: operating frequency

Output: average response time

Distributed control framework

Server cluster

Global request buffer

Clients

Dynamic

Controllers

EvaluationSystem settingse-commerceVirtual store consisting of 10000 objects

response time uniformly chosen between (4,11)

ms

request distribution

popularity

temporal locality

cluster of four servers

Evaluation

Adaptive power consumption

Evaluation

Adaptive power consumption during processors’ failure

StrengthsDevelopment of a communication-less framework for distributed optimization

Implementation of the framework

of

power consumption

and

guarantee

QoS

Usage of distributed frameworkautonomous controllersno single point of failurecapable of self-* properties

LimitationsMain concept: decomposing power management into optimal control problems for each server, based on the assumption

that resource provisioning and allocation can also be decomposed into such problems; this may not always be possible.

Adding new servers adds to the overhead in predicting its behavior by all other servers. Results for adding servers is

not

presented.

Possible extensionsStudy the system under dynamic

adding and removing of servers

Experiment with perturbations when servers are

optimally performing

remove servers that

almost always

guanrantee

QoS and see how other servers respondadd more servers to observe how estimating

the other servers’ behavior affects guarantee of QoS

Thank You!