Adaptive regressive holt–winters workload prediction and firefly optimized lottery scheduling for load balancing in cloud-Reference-Cited by-同舟云学术

Adaptive regressive holt–winters workload prediction and firefly optimized lottery scheduling for load balancing in cloud

Published:2019-07-15 Issue:8 Volume:27 Page:5597-5615
ISSN:1022-0038
Container-title:Wireless Networks
language:en
Short-container-title:Wireless Netw

Author:

Prassanna J.^ORCID,Venkataraman Neelanarayanan

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s11276-019-02090-8.pdf

Reference34 articles.

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2. Panda, S. K., & Jana, P. K. (2019). Load balanced task scheduling for cloud computing: A probabilistic approach. Knowledge and Information Systems. https://doi.org/10.1007/s10115-019-01327-4.

3. 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. https://doi.org/10.1109/tase.2017.2693688.

4. Hussain, A., Aleem, M., Khan, A., Iqbal, M. A., & Islam, M. A. (2018). RALBA: A computation-aware load balancing scheduler for cloud computing. Cluster Computing, 21(3), 1667–1680. https://doi.org/10.1007/s10586-018-2414-6.

5. Ali, H. M., & Lee, Daniel C. (2015). Virtual machine placement using biogeography-based optimization. Future Generation Computing Systems, 54, 95–122.

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