The case for power management in web servers
Power aware computing
Making a Case for Efficient Supercomputing
Queue - Power Management
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Scheduling for reduced CPU energy
OSDI '94 Proceedings of the 1st USENIX conference on Operating Systems Design and Implementation
Power capping: a prelude to power shifting
Cluster Computing
Overview of the Blue Gene/L system architecture
IBM Journal of Research and Development
Towards Adaptive Power-Aware Scheduling for Real-Time Tasks on DVS-Enabled Heterogeneous Clusters
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters
Journal of Parallel and Distributed Computing
Energy-efficient deadline scheduling for heterogeneous systems
Journal of Parallel and Distributed Computing
Parallel job scheduling for power constrained HPC systems
Parallel Computing
DS-RT '12 Proceedings of the 2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications
Deadline and energy constrained dynamic resource allocation in a heterogeneous computing environment
The Journal of Supercomputing
Energy-aware parallel task scheduling in a cluster
Future Generation Computer Systems
| Hi-index | 0.00 |
Energy consumption of computing systems and especially of large-scale systems has a growing concern nowadays due to economic and ecological reasons. To reduce energy consumption, a variety of mechanisms are proposed in the literature. One of them is Dynamic Voltage Scaling (DVS). Many systems impose energy consumption restrictions when tasks are executed. We propose an energy saving mechanism based on DVS that takes into account energy consumption restrictions imposed by the system during the execution of tasks. The proposed DVS mechanism maximizes performance without violating energy consumption restrictions. Furthermore, it achieves great energy savings by lowering processors, frequency and executes tasks under a lower power budget than the one imposed by the system. Simulation experiments show promising results when hard real-time CPU-intensive tasks are executed. Success ratio is kept at satisfactory levels (97%-99.2%) while energy consumption is reduced from 13.7% up to 21%.