Trust-Driven and PSO-SFLA based job scheduling algorithm on Cloud
Article
Xie, X., Liu, R., Cheng, X., Hu, X. and Ni, J. 2016. Trust-Driven and PSO-SFLA based job scheduling algorithm on Cloud. Intelligent Automation & Soft Computing: An International Journal . 22 (4), pp. 561-566.
| Type | Article |
|---|---|
| Title | Trust-Driven and PSO-SFLA based job scheduling algorithm on Cloud |
| Authors | Xie, X., Liu, R., Cheng, X., Hu, X. and Ni, J. |
| Abstract | With the advent of big data era, Cloud Computing has drawn widespread interests from industrial and academia. Job scheduling algorithm plays a crucial role in the paradigm of Cloud Computing. The well-designed job scheduling algorithms can provide fast, high quality and safe services. However, the conventional job scheduling algorithms are focusing on the improvement of efficiency, these obscure the important issue of trustworthiness in Cloud. This paper proposes a job scheduling algorithm with the consideration of efficiency and trustworthiness in Cloud. The intuition of the proposed algorithm is based on Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA). In this way, the proposed algorithm can avoid to obtain local optimal results. Also, the trust model is introduced to improve the trust of resources. The comprehensive simulations have been conducted via CloudSim. The experimental results have demonstrated that the proposed algorithm improve the trustworthiness than that of two classical compared algorithms GA and TDMin-Min, respectively. |
| Keywords | Cloud service; job scheduling; trust mechanism in Cloud; Particle Swarm Optimization (PSO); Shuffled Frog Leaping Algorithm (SFLA) |
| Publisher | Taylor and Francis |
| Journal | Intelligent Automation & Soft Computing: An International Journal |
| ISSN | 1079-8587 |
| Electronic | 2326-005X |
| Publication dates | |
| Online | 02 Mar 2016 |
| 01 Oct 2016 | |
| Publication process dates | |
| Deposited | 09 Jul 2018 |
| Accepted | 01 Jan 2016 |
| Output status | Published |
| Web address (URL) | https://www.tandfonline.com/doi/full/10.1080/10798587.2016.1152770 |
| Web of Science identifier | WOS:000393797100005 |
| Language | English |
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