Download
1 / 13
130 likes | 245 Views
This paper presents a fully decentralized cooperative control framework designed to optimize power consumption in computing clusters, addressing the challenge of balancing power usage with time-varying workloads and Quality of Service (QoS) requirements. Using optimal control theory, our framework operates without internal communication between servers, ensuring fault tolerance and scalability. We evaluate its effectiveness in an e-commerce context, demonstrating strengths such as a communication-less framework, while acknowledging limitations in server addition predictions. Proposed extensions include dynamic server management and further experimental scenarios.
E N D
Authors: Mianyu Wang, NagarajanKandasamy, AllonGuez, and Moshe Kam Proceedings of the 3rd International Conference on Autonomic Computing, ICAC 2006, Dublin, Ireland Presenter: RamyaPradhan, Fall 2012, UCF. Adaptive performance control of computing systems via distributed cooperative control: application to power management in computing clusters
Outline • Research problem • Proposed solution • Evaluation of proposed solution • Strengths • Limitations • Proposed extensions
Research Problem Clients Server cluster Power 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 Global request buffer Server cluster Clients Dynamic Controllers
Evaluation • System settings • e-commerce • Virtual 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
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 guanranteeQoS and see how other servers respond • add more servers to observe how estimating the other servers’ behavior affects guarantee of QoS
