Optimal container resource allocation in cloud architecture: A new hybrid model
Journal of King Saud University - Computer and Information Sciences, ISSN: 1319-1578, Vol: 34, Issue: 5, Page: 1906-1918
2022
- 18Citations
- 72Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
A huge variety of fields and industries depend upon cloud computing based microservice due to its high-performance capability. Also, the merit of container usage is enormous; it enable larger portability, easier and faster deployment and restricted overheads. However, the rapid evolution causes issues in terms of container automation and management, Till now, a number of research works has concentrated on solving the open issues in container automation and management. In fact, container resource allocation is the major key hole for cloud providers since it directly influences the resource consumption and system performance. In this manner, this paper introduces a new optimized container resource allocation model by proposing a new optimization concept. To make the possibility of optimal container resource allocation, a new hybridized algorithm is implanted; namely, Whale Random update assisted Lion Algorithm (WR-LA), which is the hybrid form of Lion Algorithm (LA) and Whale Optimization Algorithm (WOA) is introduced. Moreover, the solution of optimized resource allocation is made by considering objectives like Threshold Distance, Balanced Cluster Use, System Failure, and Total Network Distance, respectively. Finally, the performance of the proposed model is compared over other conventional models and proves its superiority.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1319157819307190; http://dx.doi.org/10.1016/j.jksuci.2019.10.009; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85075460338&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1319157819307190; https://dx.doi.org/10.1016/j.jksuci.2019.10.009
Springer Science and Business Media LLC
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