Author:
Mostafavi Seyedakbar,Hakami Vesal
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Reference43 articles.
1. Al Buhussain, A., Robson, E., & Boukerche, A. (2016). Performance analysis of bio-inspired scheduling algorithms for cloud environments. In 2016 IEEE international parallel and distributed processing symposium workshops (IPDPSW) (pp. 776–785). New York: IEEE.
2. Zhang, P., & Zhou, M. (2018). Dynamic cloud task scheduling based on a two-stage strategy. IEEE Transactions on Automation Science and Engineering, 15(2), 772–783.
3. Peng, Z., Cui, D., Zuo, J., Li, Q., Xu, B., & Lin, W. (2015). Random task scheduling scheme based on reinforcement learning in cloud computing. Cluster Computing, 18(4), 1595–1607.
4. Azad, P., Navimipour, N. J., & Hosseinzadeh, M. (2019). A fuzzy-based method for task scheduling in the cloud environments using inverted ant colony optimisation algorithm. International Journal of Bio-Inspired Computation, 14(2), 125–137.
5. Ebadifard, F., & Babamir, S. M. (2018). A PSO-based task scheduling algorithm improved using a load-balancing technique for the cloud computing environment. Concurrency and Computation: Practice and Experience, 30(12), e4368.
Cited by
17 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献