Journal of Parallel and Distributed Computing
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
A Power-Aware Run-Time System for High-Performance Computing
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Energy-aware server provisioning and load dispatching for connection-intensive internet services
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
Scheduling Parallel Task Graphs on (Almost) Homogeneous Multicluster Platforms
IEEE Transactions on Parallel and Distributed Systems
Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Energy aware scheduling for DAG structured applications on heterogeneous and DVS enabled processors
GREENCOMP '10 Proceedings of the International Conference on Green Computing
Energy Conscious Scheduling for Distributed Computing Systems under Different Operating Conditions
IEEE Transactions on Parallel and Distributed Systems
A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems
Journal of Parallel and Distributed Computing
Low power scheduling of DAGs to minimize finish times
HiPC'06 Proceedings of the 13th international conference on High Performance Computing
Energy efficient scheduling of parallel tasks on multiprocessor computers
The Journal of Supercomputing
Energy-aware I/O optimization for checkpoint and restart on a NAND flash memory system
Proceedings of the 3rd Workshop on Fault-tolerance for HPC at extreme scale
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Energy consumption has become a major concern to the widespread deployment of cloud data centers. The growing importance for parallel applications in the cloud introduces significant challenges in reducing the power consumption drawn by the hosted servers. In this paper, we propose an enhanced energy-efficient scheduling (EES) algorithm to reduce energy consumption while meeting the performance-based service level agreement (SLA). Since slacking non-critical jobs can achieve significant power saving, we exploit the slack room and allocate them in a global manner in our schedule. Using random generated and real-life application workflows, our results demonstrate that EES is able to reduce considerable energy consumption while still meeting SLA.