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.

Outline  Research problem  Proposed solution  Evaluation of proposed solution  Strengths  Limitations  Proposed extensions

Research Problem Server cluster Clients Power ConsumptionPower Consumption How to balance power consumption with time-varying workload and QoS?

Proposed solution  Fully decentralized and cooperative control framework using optimal control theory balance cluster operating frequency and average response time scalable due to problem decomposition fault-tolerant due to cooperative control no intra-cluster communication

Proposed solution using optimal control  Optimal control “uses predictive approach that generates sequence of control inputs over a specified lookahead horizon while estimating changes in operating conditions.”  System Model System state: queue size Constrained control input: operating frequency Output: average response time

Distributed control framework Server cluster Global request buffer Clients D y n a m i c C o n t r o ll e r s

Evaluation  System settings e-commerce ○ Virtual store consisting of 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

Strengths  Development of a communication-less framework for distributed optimization  Implementation of the framework of power consumption and guarantee QoS  Usage of distributed framework autonomous controllers no single point of failure capable of self-* properties

Limitations  Main 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 extensions  Study 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 respond add more servers to observe how estimating the other servers’ behavior affects guarantee of QoS

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