"Collaborative Cloud Resource Management and Task Consolidation Using J" by Kaushik Mishra, Santosh Kumar Majhi et al.
 

Collaborative Cloud Resource Management and Task Consolidation Using JAYA Variants

Document Type

Article

Publication Title

IEEE Transactions on Network and Service Management

Abstract

In Cloud-based computing, job scheduling and load balancing are vital to ensure on-demand dynamic resource provisioning. However, reducing the scheduling parameters may affect datacenter performance due to the fluctuating on-demand requests. To deal with the aforementioned challenges, this research proposes a job scheduling algorithm, which is an improved version of a swarm intelligence algorithm. Two approaches, namely linear weight JAYA (LWJAYA) and chaotic JAYA (CJAYA), are implemented to improve the convergence speed for optimal results. Besides, a load-balancing technique is incorporated in line with job scheduling. Dynamically independent and non-pre-emptive jobs were considered for the simulations, which were simulated on two disparate test cases with homogeneous and heterogeneous VMs. The efficiency of the proposed technique was validated against a synthetic and real-world dataset from NASA, and evaluated against several top-of-the-line intelligent optimization techniques, based on the Holm's test and Friedman test. Findings of the experiment show that the suggested approach performs better than the alternative approaches.

First Page

6248

Last Page

6259

DOI

10.1109/TNSM.2024.3443285

Publication Date

1-1-2024

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