The Vision of Autonomic Computing
Computer
Energy Management for Server Clusters
HOTOS '01 Proceedings of the Eighth Workshop on Hot Topics in Operating Systems
From UML activity diagrams to Stochastic Petri nets: application to software performance engineering
WOSP '04 Proceedings of the 4th international workshop on Software and performance
Managing server energy and operational costs in hosting centers
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Self-Managed Systems: an Architectural Challenge
FOSE '07 2007 Future of Software Engineering
Software Engineering for Self-Adaptive Systems: A Research Roadmap
Software Engineering for Self-Adaptive Systems
Performance aware open-world software in a 3-layer architecture
Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
Recipe for efficiency: principles of power-aware computing
Communications of the ACM
Power optimization for dynamic configuration in heterogeneous web server clusters
Journal of Systems and Software
Energy-efficient server clusters
PACS'02 Proceedings of the 2nd international conference on Power-aware computer systems
Performance sensitive self-adaptive service-oriented software using hidden Markov models
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Analysis of bursty workload-aware self-adaptive systems
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
QoS and energy management with Petri nets: A self-adaptive framework
Journal of Systems and Software
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The energy use is becoming a key design consideration in computing infrastructures and services. In this paper we focus on service-based applications and we propose an adaptation process that can be used to reduce power consumption. This adaptation process is materialized in an adaptation plan which fits into a software architecture specifically designed for self-adaptive systems. The adaptation plan guarantees a trade-off between energy consumption and QoS offered, while maintaining suitable revenues for the service provider. The proposed approach is based on the principle of proportional energy consumption obtained by scaling down energy for unused resources, considering both the number of servers switched on and the operating frequencies of that servers.