A parallel multi-objective imperialist competitive algorithm to solve the load offloading problem in mobile cloud computing

Cloud computing is a modern architecture for performing complex and immense processes. It consists of configurable computational resource sets that communicate with each other through communication networks. With the advent of the cloud computing architecture and increasing its applications for mobi...

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Vydané v:	Neural computing & applications Ročník 35; číslo 26; s. 18905 - 18932
Hlavní autori:	Alipour, Sara, Saadatfar, Hamid, Poor, Mahdi Khazaie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.09.2023 Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Cloud computing Communication networks Computation offloading Computational Biology/Bioinformatics Computational Science and Engineering Computer architecture Computer Science Data Mining and Knowledge Discovery Electronic devices Energy consumption Evolutionary algorithms Image Processing and Computer Vision Mobile computing Multiple objective analysis Original Article Parallel processing Power consumption Power management Probability and Statistics in Computer Science Task scheduling Mobile cloud computing Task scheduling Cloud computing Parallel algorithm Imperialist competitive algorithm Load offloading
ISSN:	0941-0643, 1433-3058
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Abstract	Cloud computing is a modern architecture for performing complex and immense processes. It consists of configurable computational resource sets that communicate with each other through communication networks. With the advent of the cloud computing architecture and increasing its applications for mobile devices, the growth rate of mobile data has proliferated exponentially. Consequently, processing the tasks of mobile users has become difficult due to the limitations of these devices, such as low computing power and low capacity. Therefore, the idea of mobile cloud computing (MCC) for mobile devices using cloud-based storage and computing resources was introduced. In MCC, processing information is transferred from the user ' s mobile devices to the cloud servers. This process is known as the tasks offloading and scheduling of mobile users. In this case, the task execution time, CPU power consumption, network bandwidth, and task allocation time must be specified. Due to many tasks and different resources, the process of task offloading and scheduling is considered a challenging subject in the field of MCC. Therefore, in this paper, a multi-objective parallel imperialist competitive algorithm (MPICA) is proposed. The main objective of this parallel algorithm is to reduce the algorithm ' s execution time for searching the problem space, reducing processing time, reducing energy consumption, and improving load balance. The simulation results of the proposed algorithm represent that the parallelization of the imperialist competitive algorithm (ICA) has a significant effect on reducing the execution time of the algorithm. In general, the proposed algorithm performs better than the state-of-the-art algorithms based on the proposed criteria.
AbstractList	Cloud computing is a modern architecture for performing complex and immense processes. It consists of configurable computational resource sets that communicate with each other through communication networks. With the advent of the cloud computing architecture and increasing its applications for mobile devices, the growth rate of mobile data has proliferated exponentially. Consequently, processing the tasks of mobile users has become difficult due to the limitations of these devices, such as low computing power and low capacity. Therefore, the idea of mobile cloud computing (MCC) for mobile devices using cloud-based storage and computing resources was introduced. In MCC, processing information is transferred from the user's mobile devices to the cloud servers. This process is known as the tasks offloading and scheduling of mobile users. In this case, the task execution time, CPU power consumption, network bandwidth, and task allocation time must be specified. Due to many tasks and different resources, the process of task offloading and scheduling is considered a challenging subject in the field of MCC. Therefore, in this paper, a multi-objective parallel imperialist competitive algorithm (MPICA) is proposed. The main objective of this parallel algorithm is to reduce the algorithm's execution time for searching the problem space, reducing processing time, reducing energy consumption, and improving load balance. The simulation results of the proposed algorithm represent that the parallelization of the imperialist competitive algorithm (ICA) has a significant effect on reducing the execution time of the algorithm. In general, the proposed algorithm performs better than the state-of-the-art algorithms based on the proposed criteria. Cloud computing is a modern architecture for performing complex and immense processes. It consists of configurable computational resource sets that communicate with each other through communication networks. With the advent of the cloud computing architecture and increasing its applications for mobile devices, the growth rate of mobile data has proliferated exponentially. Consequently, processing the tasks of mobile users has become difficult due to the limitations of these devices, such as low computing power and low capacity. Therefore, the idea of mobile cloud computing (MCC) for mobile devices using cloud-based storage and computing resources was introduced. In MCC, processing information is transferred from the user ' s mobile devices to the cloud servers. This process is known as the tasks offloading and scheduling of mobile users. In this case, the task execution time, CPU power consumption, network bandwidth, and task allocation time must be specified. Due to many tasks and different resources, the process of task offloading and scheduling is considered a challenging subject in the field of MCC. Therefore, in this paper, a multi-objective parallel imperialist competitive algorithm (MPICA) is proposed. The main objective of this parallel algorithm is to reduce the algorithm ' s execution time for searching the problem space, reducing processing time, reducing energy consumption, and improving load balance. The simulation results of the proposed algorithm represent that the parallelization of the imperialist competitive algorithm (ICA) has a significant effect on reducing the execution time of the algorithm. In general, the proposed algorithm performs better than the state-of-the-art algorithms based on the proposed criteria.
Author	Poor, Mahdi Khazaie Saadatfar, Hamid Alipour, Sara
Author_xml	– sequence: 1 givenname: Sara surname: Alipour fullname: Alipour, Sara organization: Computer Engineering Department, Birjand Branch, Islamic Azad University – sequence: 2 givenname: Hamid orcidid: 0000-0002-6130-8450 surname: Saadatfar fullname: Saadatfar, Hamid email: saadatfar@birjand.ac.ir organization: Department of Computer Engineering, University of Birjand – sequence: 3 givenname: Mahdi Khazaie surname: Poor fullname: Poor, Mahdi Khazaie organization: Computer Engineering Department, Birjand Branch, Islamic Azad University
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(2021) A new optimization approach for task scheduling problem using water cycle algorithm in mobile cloud computing. In: 2021 IEEE congress on evolutionary computation (CEC). IEEE – reference: DinhHTA survey of mobile cloud computing: architecture, applications, and approachesWirel Commun Mob Comput201313181587161110.1002/wcm.1203 – reference: TarafdarAEnergy and makespan aware scheduling of deadline sensitive tasks in the cloud environmentJ Grid Comput20211912510.1007/s10723-021-09548-0 – reference: HanPCost and makespan scheduling of workflows in clouds using list multiobjective optimization techniqueJ Syst Architect202111210.1016/j.sysarc.2020.101837 – reference: ShiTAn energy-efficient scheduling scheme for time-constrained tasks in local mobile cloudsPervasive Mob Comput2016279010510.1016/j.pmcj.2015.07.005 – reference: AkherfiKGerndtMHarroudHMobile cloud computing for computation offloading: Issues and challengesAppl Comput Inform201814111610.1016/j.aci.2016.11.002 – reference: LinJC-WScalable mining of high-utility sequential patterns with three-tier MapReduce modelACM Trans Knowl Discov Data (TKDD)2021163126 – reference: Shao Y, et al. 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SubjectTerms	Algorithms Artificial Intelligence Cloud computing Communication networks Computation offloading Computational Biology/Bioinformatics Computational Science and Engineering Computer architecture Computer Science Data Mining and Knowledge Discovery Electronic devices Energy consumption Evolutionary algorithms Image Processing and Computer Vision Mobile computing Multiple objective analysis Original Article Parallel processing Power consumption Power management Probability and Statistics in Computer Science Task scheduling
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Title	A parallel multi-objective imperialist competitive algorithm to solve the load offloading problem in mobile cloud computing
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