An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

PLOS ONE RESEARCH ARTICLE An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment D. Paulraj1, T. Sethukarasi1, S. Neelakandan1, M. Prakash ID2, E. Baburaj ID3* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Paulraj D, Sethukarasi T, Neelakandan S, Prakash M, Baburaj E (2023) An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment. PLoS ONE 18(3): e0282600. https://doi.org/ 10.1371/journal.pone.0282600 Editor: Shih-Wei Lin, Chang Gung University, TAIWAN Received: November 8, 2022 Accepted: February 21, 2023 Published: March 13, 2023 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0282600 Copyright: © 2023 Paulraj et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 1 Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India, 2 School of Computing Science and Engineering, VIT University, Chennai, India, 3 Department of Electrical and Computer Engineering, Bule Hora University, Bule Hora, Ethiopia * Abstract Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species’ obligate brood parasitism (laying eggs in other species’ nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior. Data Availability Statement: All relevant data are within the paper. PLOS ONE | https://doi.org/10.1371/journal.pone.0282600 March 13, 2023 1 / 18 PLOS ONE Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Hybrid Job Scheduling Optimization (EHJSO) resource search, job optimization for cloud environment 1. Introduction Cloud computing has completely revolutionised business since it enables the efficient pooling of computing resources. Cloud users can provision and distribute pay-per-use cloud computing resources using the Cloud Service Provider’s public interface [1]. Recent advancements in cloud computing enable numerous geographically distributed and interconnected cloud data centres to provide pay-per-use on-demand services to cloud customers more efficiently [2]. According to [28], cloud data centres will handle 94% of computing workload by 2021. Cloud computing’s novel concept has provided various benefits, including decreased infrastructure costs, execution time, and maintenance expenses, among others. However, the increased strain imposed by the execution of several cloud-based applications led to a decline in resource utilisation and a reduced return on investment [3]. Incorrect job scheduling among virtual machines is one of the key causes of a decline in cloud computing resource utilisation, resulting in a loss of processing performance. Therefore, task scheduling is essential in cloud computing to assure optimal resource use by providing acceptable performance under varying task restrictions, including execution deadlines. In cloud computing, a variety of tasks may need to be programmed on a large number of virtual machines [4] to save development time and enhance system performance. Therefore, work planning is essential for restoring the adaptability and dependability of cloud-based solutions. Scheduling tasks, on the other hand, has a broad scope of optimization and greatly contributes to the development of dependable and adaptable dynamic solutions. The majority of cloud computing work scheduling algorithms are rule-based because they are simple to build. Rule-based algorithms perform badly in the preparation of multidimensional jobs. Moreover, resource allocation and scheduling are not only associated with quality of service (QoS), but can also have a long-term effect on the revenue of cloud service providers. Researchers have access to a wide variety of alternatives for resource scheduling, and resource scheduling is currently recognised as one of the most important concerns in the field of cloud computing. Job scheduling assigns user-supplied tasks to the correct cloud virtual machine [5]. Cloud consumers must sign a service level agreement with the cloud provider to stipulate service quality, execution timetable, budget, and work security. The user may request the computer resources needed to finish his job in compliance with the SLA [6]. The performance of cloud computing is directly affected by task scheduling. With proper work scheduling, more money can be generated, performance can be enhanced, and SLA violations may be minimised. Due to the rising complexity of cloud computing, the scheduling problem has become increasingly difficult to solve. In a cloud computing contex (...truncated)