Name
Title | ||
|---|---|---|
Using genetic algorithm in profile-based assignment of applications to virtual machines for greener data centers |
Abstract | ||
|---|---|---|
The increase in data center dependent services has made energy optimization of data centers one of the most exigent challenges inï¾źtoday's Information Age. The necessity of green and energy-efficient measures is very high for reducing carbon footprint and exorbitant energy costs. However, inefficient application management of data centers results in high energy consumption and low resource utilization efficiency. Unfortunately, in most cases, deploying an energy-efficient application management solution inevitably degrades the resource utilization efficiency of the data centers. To address this problem, a Penalty-based Genetic Algorithm GA is presented in this paper to solve a defined profile-based application assignment problem whilst maintaining a trade-off between the power consumption performance and resource utilization performance. Case studies show that the penalty-based GA is highly scalable and provides 16ï¾ź% to 32ï¾ź% better solutions than a greedy algorithm. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26535-3_21 | ICONIP |
Keywords | DocType | Volume |
Data center,Energy efficiency,Application assignment,Resource scheduling,Genetic algorithm,Profiling | Conference | 9490 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.37 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meera Vasudevan | 1 | 2 | 0.37 |
| Yu-Chu Tian | 2 | 550 | 59.35 |
| Maolin Tang | 3 | 107 | 10.36 |
| Erhan Kozan | 4 | 315 | 32.28 |
| Jing Gao | 5 | 2 | 1.04 |