Open Access

ARTICLE

MCWOA Scheduler: Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Chirag Chandrashekar¹, Pradeep Krishnadoss^1,*, Vijayakumar Kedalu Poornachary¹, Balasundaram Ananthakrishnan^1,2

1 School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India
2 Center for Cyber Physical Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India

* Corresponding Author: Pradeep Krishnadoss. Email:

Computers, Materials & Continua 2024, 78(2), 2593-2616. https://doi.org/10.32604/cmc.2024.046304

Received 26 September 2023; Accepted 14 December 2023; Issue published 27 February 2024

Abstract

Cloud computing provides a diverse and adaptable resource pool over the internet, allowing users to tap into various resources as needed. It has been seen as a robust solution to relevant challenges. A significant delay can hamper the performance of IoT-enabled cloud platforms. However, efficient task scheduling can lower the cloud infrastructure’s energy consumption, thus maximizing the service provider’s revenue by decreasing user job processing times. The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm (MCWOA), combines elements of the Chimp Optimization Algorithm (COA) and the Whale Optimization Algorithm (WOA). To enhance MCWOA’s identification precision, the Sobol sequence is used in the population initialization phase, ensuring an even distribution of the population across the solution space. Moreover, the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process. This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model. Simulated outcomes reveal that the new method outperforms the original MCWOA, especially in multi-damage detection scenarios. MCWOA excels in avoiding false positives and enhancing computational speed, making it an optimal choice for structural damage detection. The efficiency of the proposed MCWOA is assessed against metrics such as energy usage, computational expense, task duration, and delay. The simulated data indicates that the new MCWOA outpaces other methods across all metrics. The study also references the Whale Optimization Algorithm (WOA), Chimp Algorithm (CA), Ant Lion Optimizer (ALO), Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO).

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Keywords

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