Name
Abstract | ||
|---|---|---|
It has been widely known that various benefits can be achieved by reducing energy consumption for high end computing. This paper aims to develop power aware scheduling heuristics for parallel tasks in a cluster with the DVFS technique. In this paper, formal models are presented for precedenceconstrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task’s execution time as a whole. This paper develops a power aware task clustering algorithm for parallel task scheduling Simulation results justify the design and implementation of proposed energy aware scheduling heuristics in the paper. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPADS.2010.128 | ICPADS |
Keywords | Field | DocType |
parallel tasks,proposed energy,execution time,energy consumption,paper study,parallel task,task clustering,slack time,power aware task,precedenceconstrained parallel task,power aware scheduling heuristics,aware scheduling heuristics,power aware scheduling,simulation,schedules,scheduling,parallel processing,task analysis,clustering algorithms | Task analysis,Computer science,Scheduling (computing),Real-time computing,Schedule,Least slack time scheduling,Execution time,Cluster analysis,Energy consumption,High end computing,Distributed computing | Conference |
Citations | PageRank | References |
2 | 0.37 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lizhe Wang | 1 | 2973 | 191.46 |
| Jie Tao | 2 | 916 | 61.29 |
| Gregor von Laszewski | 3 | 1795 | 157.85 |
| Dan Chen | 4 | 1096 | 59.02 |