An adaptive model-free resource and power management approach for multi-tier cloud environments
Journal of Systems and Software
Enhanced Energy-Efficient Scheduling for Parallel Applications in Cloud
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Reducing Operational Costs through Consolidation with Resource Prediction in the Cloud
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
The Journal of Supercomputing
Multi-objective firefly algorithm for energy optimization in grid environments
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
DS-RT '12 Proceedings of the 2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications
Energy-Aware Scheduling Algorithm with Duplication on Heterogeneous Computing Systems
GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
Virtual machine power measuring technique with bounded error in cloud environments
Journal of Network and Computer Applications
Heterogeneity-Aware optimal power allocation in data center environments
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Energy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems
Journal of Grid Computing
Hi-index | 0.00 |
Traditionally, the primary performance goal of computer systems has focused on reducing the execution time of applications while increasing throughput. This performance goal has been mostly achieved by the development of high-density computer systems. As witnessed recently, these systems provide very powerful processing capability and capacity. They often consist of tens or hundreds of thousands of processors and other resource-hungry devices. The energy consumption of these systems has become a major concern. In this paper, we address the problem of scheduling precedence-constrained parallel applications on multiprocessor computer systems and present two energy-conscious scheduling algorithms using dynamic voltage scaling (DVS). A number of recent commodity processors are capable of DVS, which enables processors to operate at different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. To effectively balance these two performance goals, we have devised a novel objective function and a variant from that. The main difference between the two algorithms is in their measurement of energy consumption. The extensive comparative evaluations conducted as part of this work show that the performance of our algorithms is very compelling in terms of both application completion time and energy consumption.