Name
Abstract | ||
|---|---|---|
Nowadays, how to improve energy efficiency has become a challenging problem in cloud computing. However, most existing efforts in improving the energy efficiency of a cloud system only focus on resource allocation at the system level like managing physical nodes or virtual machines. This paper tries to address the energy efficiency problem of a heterogeneous cloud system at the task scheduling level. A novel task scheduling algorithm is proposed to reduce the energy consumption of the system while maintaining its performance, without closing or consolidating any system resources such as virtual machines or storage systems. The algorithm dynamically monitors CPU and memory load information of participating nodes on a heterogeneous Hadoop platform with Ganglia, then selects and submits an appropriate task to the node with relatively low workload to avoid excessive energy consumption on some nodes. Experimental results show that this novel scheduling algorithm can effectively improve the energy-saving ratio of a heterogeneous cloud platform while maintaining a high system performance. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.4018/IJGHPC.2014100101 | International Journal of Grid and High Performance Computing |
Keywords | Field | DocType |
Cloud Computing, Energy Efficiency, Heterogeneous Environments, Task Scheduling | Virtual machine,Fair-share scheduling,Efficient energy use,Workload,Scheduling (computing),Computer science,Computer network,Resource allocation,Energy consumption,Cloud computing,Distributed computing | Journal |
Volume | Issue | ISSN |
6 | 4 | 1938-0259 |
Citations | PageRank | References |
4 | 0.41 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Lin | 1 | 143 | 12.22 |
| C. Yang | 2 | 296 | 43.66 |
| Chaoyue Zhu | 3 | 9 | 0.83 |
| James Z. Wang | 4 | 66 | 6.18 |
| zhiping peng | 5 | 28 | 1.62 |