The Markov-modulated Poisson process (MMPP) cookbook
Performance Evaluation
Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Power provisioning for a warehouse-sized computer
Proceedings of the 34th annual international symposium on Computer architecture
netWorker - Cloud computing: PC functions move onto the web
Profiling and modeling resource usage of virtualized applications
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Injecting realistic burstiness to a traditional client-server benchmark
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
A comparison of high-level full-system power models
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Coordinating Power Control and Performance Management for Virtualized Server Clusters
IEEE Transactions on Parallel and Distributed Systems
Economical and Robust Provisioning of N-Tier Cloud Workloads: A Multi-level Control Approach
ICDCS '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems
Energy-Efficient Resource Management for Cloud Computing Infrastructures
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
Minimizing data center SLA violations and power consumption via hybrid resource provisioning
IGCC '11 Proceedings of the 2011 International Green Computing Conference and Workshops
A Pareto-modulated Poisson process (PMPP) model for long-range dependent traffic
Computer Communications
Concurrency and Computation: Practice & Experience
Hi-index | 0.00 |
Cloud computing is an emerging computing paradigm in which "Everything is as a Service", including the provision of virtualized computing infrastructures (known as Infrastructure-as-a-Service modality) hosted on the physical infrastructure, owned by an infrastructure provider. The goal of this infrastructure provider is to maximize its profit by minimizing the amount of violations of Quality-of-Service (QoS) levels agreed with its customers and, at the same time, by lowering infrastructure costs among which energy consumption plays a major role. In this paper, we propose a framework able to automatically manage resources of cloud infrastructures in order to simultaneously achieve suitable QoS levels and to reduce as much as possible the amount of energy used for providing services. We show, through simulation, that our approach is able to dynamically adapt to time-varying workloads (without any prior knowledge) and to significantly reduce QoS violations and energy consumption with respect to traditional static approaches.