Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization

Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of servic...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 23; číslo 13; s. 6155
Hlavní autori:	Mangalampalli, Sudheer, Swain, Sangram Keshari, Chakrabarti, Tulika, Chakrabarti, Prasun, Karri, Ganesh Reddy, Margala, Martin, Unhelkar, Bhuvan, Krishnan, Sivaneasan Bala
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 05.07.2023 MDPI
Predmet:	Algorithms Analysis Cloud Computing Customer services Energy consumption Literature reviews makespan Mathematical optimization OpenStack Optimization Quality of service Scheduling Simulation SLA violation task scheduling Workload Workloads India makespan OpenStack task scheduling cloud computing energy consumption SLA violation
ISSN:	1424-8220, 1424-8220
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Abstract	Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters.
AbstractList	Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters. Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters.Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters.
Audience	Academic
Author	Chakrabarti, Prasun Krishnan, Sivaneasan Bala Karri, Ganesh Reddy Margala, Martin Swain, Sangram Keshari Mangalampalli, Sudheer Unhelkar, Bhuvan Chakrabarti, Tulika
AuthorAffiliation	6 Muma College of Business, University of South Florida Sarasota-Manatee campus, Sarasota, FL 33620, USA; bunhelkar@usf.edu 3 Department of Basic Sciences, Sir Padampat Singhania University, Udaipur 313601, Rajasthan, India; tulika.chakrabarti@spsu.ac.in 5 School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA; martin.margala@louisiana.edu 1 School of Computer Science and Engineering, VIT-AP University, Amarvati 522237, Andhra Pradesh, India; ganesh.reddy@vitap.ac.in 2 School of Engineering and Technology, Centurion University of Technology and Management, Bhubaneswar 752050, Odisha, India 4 Department of Computer Science and Engineering, ITM SLS Baroda University, Vadodara 391510, Gujarat, India; drprasun.cse@gmail.com 7 Singapore Institute of Technology, Singapore 139660, Singapore; sivaneasan@singaporetech.edu.sg
AuthorAffiliation_xml	– name: 1 School of Computer Science and Engineering, VIT-AP University, Amarvati 522237, Andhra Pradesh, India; ganesh.reddy@vitap.ac.in – name: 3 Department of Basic Sciences, Sir Padampat Singhania University, Udaipur 313601, Rajasthan, India; tulika.chakrabarti@spsu.ac.in – name: 5 School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA; martin.margala@louisiana.edu – name: 7 Singapore Institute of Technology, Singapore 139660, Singapore; sivaneasan@singaporetech.edu.sg – name: 4 Department of Computer Science and Engineering, ITM SLS Baroda University, Vadodara 391510, Gujarat, India; drprasun.cse@gmail.com – name: 2 School of Engineering and Technology, Centurion University of Technology and Management, Bhubaneswar 752050, Odisha, India – name: 6 Muma College of Business, University of South Florida Sarasota-Manatee campus, Sarasota, FL 33620, USA; bunhelkar@usf.edu
Author_xml	– sequence: 1 givenname: Sudheer orcidid: 0000-0002-1485-8783 surname: Mangalampalli fullname: Mangalampalli, Sudheer – sequence: 2 givenname: Sangram Keshari surname: Swain fullname: Swain, Sangram Keshari – sequence: 3 givenname: Tulika surname: Chakrabarti fullname: Chakrabarti, Tulika – sequence: 4 givenname: Prasun surname: Chakrabarti fullname: Chakrabarti, Prasun – sequence: 5 givenname: Ganesh Reddy orcidid: 0000-0002-5177-8125 surname: Karri fullname: Karri, Ganesh Reddy – sequence: 6 givenname: Martin surname: Margala fullname: Margala, Martin – sequence: 7 givenname: Bhuvan surname: Unhelkar fullname: Unhelkar, Bhuvan – sequence: 8 givenname: Sivaneasan Bala orcidid: 0000-0002-4271-2677 surname: Krishnan fullname: Krishnan, Sivaneasan Bala
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Cites_doi	10.1007/s11227-021-03915-0 10.1007/s11277-019-06360-8 10.1016/j.fcij.2018.03.004 10.1002/spe.995 10.3390/s22031242 10.1007/s10586-018-1823-x 10.1109/TNET.2022.3193381 10.1007/s00521-019-04119-7 10.1007/s10462-020-09933-3 10.1155/2022/4525220 10.1016/j.matpr.2020.09.064 10.1145/3447993.3483274 10.3390/pr9091514 10.1016/j.ipm.2021.102676 10.1109/ICDCS.2019.00021 10.1109/MCC.2018.032591611 10.1016/j.future.2021.05.012 10.1007/s10586-019-02983-5 10.1049/iet-com.2019.1149 10.2298/CSIS220322038L 10.1080/17517575.2019.1605001 10.1007/s00521-019-04067-2 10.1002/dac.4379 10.1016/j.knosys.2019.01.023 10.1155/2020/4854895 10.1155/2019/6543957 10.1108/IJWIS-11-2020-0071 10.1109/ACCESS.2019.2946216 10.32604/cmc.2022.017504 10.1016/j.asej.2020.07.003 10.1016/j.eswa.2020.114230 10.1016/j.jnca.2017.11.016 10.7717/peerj-cs.539 10.1109/ACCESS.2021.3105727 10.1007/s00521-021-06002-w
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SubjectTerms	Algorithms Analysis Cloud Computing Customer services Energy consumption Literature reviews makespan Mathematical optimization OpenStack Optimization Quality of service Scheduling Simulation SLA violation task scheduling Workload Workloads
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Title	Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization
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