Voltage scheduling problem for dynamically variable voltage processors
ISLPED '98 Proceedings of the 1998 international symposium on Low power electronics and design
The Vision of Autonomic Computing
Computer
QoS Management in Web-based Real-Time Data Services
WECWIS '02 Proceedings of the Fourth IEEE International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems (WECWIS'02)
Power-aware QoS Management in Web Servers
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
Energy-Efficient Real-Time Heterogeneous Server Clusters
RTAS '06 Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium
Control of large scale computing systems
ACM SIGBED Review
Feedback Control Architecture and Design Methodology for Service Delay Guarantees in Web Servers
IEEE Transactions on Parallel and Distributed Systems
Statistical QoS Guarantee and Energy-Efficiency in Web Server Clusters
ECRTS '07 Proceedings of the 19th Euromicro Conference on Real-Time Systems
Power and Performance Management of Virtualized Computing Environments Via Lookahead Control
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
Generalized Tardiness Quantile Metric: Distributed DVS for Soft Real-Time Web Clusters
ECRTS '09 Proceedings of the 2009 21st Euromicro Conference on Real-Time Systems
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This paper presents a novel way to control power consumption and performance in a multi-tier server cluster designed for e-commerce applications. The requests submitted to these server systems have a soft real-time constraint, given that although some can miss a pre-defined deadline, the system can still meet an agreed upon performance level. Clusters of servers are extensively used nowadays and, with the steep increase in the total power consumption in these systems, economic and environmental problems have been raised. We present ways of decreasing power expenditure, and show the implementation of a SISO (Single Input Single Output) controller that acts on the speed of all server nodes capable of dynamic voltage and frequency scaling (DVFS), with QoS (Quality of Service) being the reference setpoint. For QoS, we use the request tardiness, defined as the ratio of the end-to-end response time to the deadline, rather than the usual metric that counts missed deadlines. We adjust the servers operating frequencies to guarantee that a pre-defined p-quantile of the tardiness probability distribution of the requests meet their deadlines. Doing so we can guarantee that the QoS will be statistically p. We test this technique in a prototype multi-tier cluster, using open software, commodity hardware, and a standardized e-commerce application to generate a workload close to that of the real world. The main contribution of this paper is to empirically show the robustness of the SISO controller, presenting a sensibility analysis of its parameters. Experimental results show that our implementation outperforms other published state-of-the-art cluster implementations.