Name
Abstract | ||
|---|---|---|
Cloud Computing is becoming more and more popular for solving problems that need high concurrency and a lot of resources. Many traditional areas of research choose to solve their problems through the cloud, and workflow scheduling is one of them. Cloud computing brings many benefits, meanwhile, due to the almost ;infinite & x201D; amount of resources for users, it also brings new challenges for scheduling and optimization, in which cost and makespan are the most concerned issues for workflow scheduling. Users want to obtain a low cost and fast makespan solution. This paper focuses on how to find an optimized solution to achieve better cost-makespan at the same time under the constraint of deadline. In order to solve this problem, an immune particle swarm optimization algorithm (IMPSO) is proposed, which effectively improves the quality and speed of the optimization. The proposed IMPSO overcomes the problem of slow convergence of PSO, which is easy to fall into local optimization. Experiments show the efficiency and effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2972963 | IEEE ACCESS |
Keywords | DocType | Volume |
Cloud computing,workflow scheduling,immune mechanism,particle swarm algorithm | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 2 | 0.37 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengwei Wang | 1 | 142 | 16.01 |
| Yinghui Lei | 2 | 2 | 0.37 |
| Promise Ricardo Agbedanu | 3 | 2 | 0.37 |
| Zhaohui Zhang | 4 | 16 | 9.02 |