Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Minimizing execution time in MPI programs on an energy-constrained, power-scalable cluster
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Scientific Programming - Scientific Workflows
Just-in-time dynamic voltage scaling: Exploiting inter-node slack to save energy in MPI programs
Journal of Parallel and Distributed Computing
ICPADS '08 Proceedings of the 2008 14th IEEE International Conference on Parallel and Distributed Systems
Cost-aware scheduling for heterogeneous enterprise machines (CASH'EM)
CLUSTER '07 Proceedings of the 2007 IEEE International Conference on Cluster Computing
Minimizing Energy Consumption for Precedence-Constrained Applications Using Dynamic Voltage Scaling
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Profit-Driven Service Request Scheduling in Clouds
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Profile-based optimization of power performance by using dynamic voltage scaling on a PC cluster
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers
Journal of Parallel and Distributed Computing
A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems
Journal of Parallel and Distributed Computing
Energy efficient utilization of resources in cloud computing systems
The Journal of Supercomputing
Parallel genetic algorithms for DVS scheduling of distributed embedded systems
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with High Performance Computing (HPC). Minimizing energy consumption can significantly reduce the amount of energy bills and then increase the provider's profit. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to make HPC applications consuming less energy. In this paper, we present a multi-objective genetic algorithm (MO-GA) that optimizes the energy consumption, CO2 emissions and the generated profit of a geographically distributed cloud computing infrastructure. We also propose a greedy heuristic that aims to maximize the number of scheduled applications in order to compare it with the MO-GA. The two approaches have been experimented using realistic workload traces from Feitelson's PWA Parallel Workload Archive. The results show that MO-GA outperforms the greedy heuristic by a significant margin in terms of energy consumption and CO2 emissions. In addition, MO-GA is also proved to be slightly better in terms of profit while scheduling more applications.