Presentations text content in Adaptive performance control of computing systems
Slide1
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.
Slide2OutlineResearch problemProposed solutionEvaluation of proposed solutionStrengths
Limitations
Proposed extensions
Slide3Research Problem
Server cluster
Clients
Power
Consumption
How to balance power consumption with
time-varying workload and
QoS
?
Slide4Proposed 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
Slide5Proposed 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
Slide6Distributed control framework
Server cluster
Global request buffer
Clients
Dynamic
Controllers
Slide7EvaluationSystem settingse-commerceVirtual store consisting of 10000 objects
response time uniformly chosen between (4,11)
ms
request distribution
popularity
temporal locality
cluster of four servers
Slide8Evaluation
Adaptive power consumption
Slide9Evaluation
Adaptive power consumption during processors’ failure
Slide10StrengthsDevelopment 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
Slide11LimitationsMain 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.
Slide12Possible 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
Slide13Thank You!
Adaptive performance control of computing systems
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