Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem

In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. T...
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Autor*in:	Xinying Chen [verfasserIn] Yuefeng Wu [verfasserIn] Siyi Xiao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Intelligent optimization algorithm PSO microservice container scheduling GWO pareto optimal

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 11(2023), Seite 16667-16682
Übergeordnetes Werk:	volume:11 ; year:2023 ; pages:16667-16682

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2023.3244881

Katalog-ID:	DOAJ079943403

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allfields	10.1109/ACCESS.2023.3244881 doi (DE-627)DOAJ079943403 (DE-599)DOAJ37295cd024fa40dd916981a8c5a92847 DE-627 ger DE-627 rakwb eng TK1-9971 Xinying Chen verfasserin aut Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm - Grey Wolf Cooperation Algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability. Intelligent optimization algorithm PSO microservice container scheduling GWO pareto optimal Electrical engineering. Electronics. Nuclear engineering Yuefeng Wu verfasserin aut Siyi Xiao verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 16667-16682 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:16667-16682 https://doi.org/10.1109/ACCESS.2023.3244881 kostenfrei https://doaj.org/article/37295cd024fa40dd916981a8c5a92847 kostenfrei https://ieeexplore.ieee.org/document/10044102/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 16667-16682
spelling	10.1109/ACCESS.2023.3244881 doi (DE-627)DOAJ079943403 (DE-599)DOAJ37295cd024fa40dd916981a8c5a92847 DE-627 ger DE-627 rakwb eng TK1-9971 Xinying Chen verfasserin aut Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm - Grey Wolf Cooperation Algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability. Intelligent optimization algorithm PSO microservice container scheduling GWO pareto optimal Electrical engineering. Electronics. Nuclear engineering Yuefeng Wu verfasserin aut Siyi Xiao verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 16667-16682 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:16667-16682 https://doi.org/10.1109/ACCESS.2023.3244881 kostenfrei https://doaj.org/article/37295cd024fa40dd916981a8c5a92847 kostenfrei https://ieeexplore.ieee.org/document/10044102/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 16667-16682
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allfieldsGer	10.1109/ACCESS.2023.3244881 doi (DE-627)DOAJ079943403 (DE-599)DOAJ37295cd024fa40dd916981a8c5a92847 DE-627 ger DE-627 rakwb eng TK1-9971 Xinying Chen verfasserin aut Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm - Grey Wolf Cooperation Algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability. Intelligent optimization algorithm PSO microservice container scheduling GWO pareto optimal Electrical engineering. Electronics. Nuclear engineering Yuefeng Wu verfasserin aut Siyi Xiao verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 16667-16682 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:16667-16682 https://doi.org/10.1109/ACCESS.2023.3244881 kostenfrei https://doaj.org/article/37295cd024fa40dd916981a8c5a92847 kostenfrei https://ieeexplore.ieee.org/document/10044102/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 16667-16682
allfieldsSound	10.1109/ACCESS.2023.3244881 doi (DE-627)DOAJ079943403 (DE-599)DOAJ37295cd024fa40dd916981a8c5a92847 DE-627 ger DE-627 rakwb eng TK1-9971 Xinying Chen verfasserin aut Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm - Grey Wolf Cooperation Algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability. Intelligent optimization algorithm PSO microservice container scheduling GWO pareto optimal Electrical engineering. Electronics. Nuclear engineering Yuefeng Wu verfasserin aut Siyi Xiao verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 16667-16682 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:16667-16682 https://doi.org/10.1109/ACCESS.2023.3244881 kostenfrei https://doaj.org/article/37295cd024fa40dd916981a8c5a92847 kostenfrei https://ieeexplore.ieee.org/document/10044102/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 16667-16682
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callnumber-first	T - Technology
author	Xinying Chen
spellingShingle	Xinying Chen misc TK1-9971 misc Intelligent optimization algorithm misc PSO misc microservice container scheduling misc GWO misc pareto optimal misc Electrical engineering. Electronics. Nuclear engineering Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem
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topic_title	TK1-9971 Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem Intelligent optimization algorithm PSO microservice container scheduling GWO pareto optimal
topic	misc TK1-9971 misc Intelligent optimization algorithm misc PSO misc microservice container scheduling misc GWO misc pareto optimal misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Intelligent optimization algorithm misc PSO misc microservice container scheduling misc GWO misc pareto optimal misc Electrical engineering. Electronics. Nuclear engineering
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title	Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem
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title_sort	particle swarm–grey wolf cooperation algorithm based on microservice container scheduling problem
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title_auth	Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem
abstract	In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm - Grey Wolf Cooperation Algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability.
abstractGer	In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm - Grey Wolf Cooperation Algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability.
abstract_unstemmed	In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm - Grey Wolf Cooperation Algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability.
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title_short	Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem
url	https://doi.org/10.1109/ACCESS.2023.3244881 https://doaj.org/article/37295cd024fa40dd916981a8c5a92847 https://ieeexplore.ieee.org/document/10044102/ https://doaj.org/toc/2169-3536
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