A Greener Heterogeneous Scheduling Algorithm via Blending Pattern of Particle Swarm Computing Intelligence and Geometric Brownian Motion

This paper focuses on the algorithms design of heterogeneous green scheduling for energy conservation and emission reduction in cloud computing. In essence, the real time, dynamic and complexity of heterogeneous scheduling require higher algorithm performance; however, the swarm intelligent algorith...

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Autores principales:	Shaohui Li, Hong Liu, Bin Gong, Jinglian Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Heterogeneous green scheduling swarm intelligence particle swarm optimization (PSO) algorithm standard Brownian motion G-Brownian motion (geometric Brownian motion) blending pattern Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/3c800159940e44fe93bb723ceae9ff2d
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spelling	oai:doaj.org-article:3c800159940e44fe93bb723ceae9ff2d2021-11-18T00:07:23ZA Greener Heterogeneous Scheduling Algorithm via Blending Pattern of Particle Swarm Computing Intelligence and Geometric Brownian Motion2169-353610.1109/ACCESS.2021.3123628https://doaj.org/article/3c800159940e44fe93bb723ceae9ff2d2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591647/https://doaj.org/toc/2169-3536This paper focuses on the algorithms design of heterogeneous green scheduling for energy conservation and emission reduction in cloud computing. In essence, the real time, dynamic and complexity of heterogeneous scheduling require higher algorithm performance; however, the swarm intelligent algorithms although with some improvements, still exist big imbalances between local exploration and macro development or between route (solution) diversity and faster convergence. In this paper, a greener heterogeneous scheduling algorithm via blending pattern of particle swarm computing intelligence and geometric Brownian motion, is proposed, based on our earlier theoretical breakthroughs on G-Brownian motion and through a series of mathematical derivations or proofs; furthermore, in order for suitable for the hybrid processor architecture of the scheduling management server, the algorithm is designed in parallel with deep fusion of coarse-grained and master-slave models. A large number of experimental results are given. Compared with most newly published scheduling algorithms, there are significant advantages of the proposed algorithm on the dynamic optimization performance for consistent or semiconsistent and large inconsistent scheduling instances, although with lower improvement factors for small inconsistent instances.Shaohui LiHong LiuBin GongJinglian WangIEEEarticleHeterogeneous green schedulingswarm intelligenceparticle swarm optimization (PSO) algorithmstandard Brownian motionG-Brownian motion (geometric Brownian motion)blending patternElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147397-147405 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Heterogeneous green scheduling swarm intelligence particle swarm optimization (PSO) algorithm standard Brownian motion G-Brownian motion (geometric Brownian motion) blending pattern Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Heterogeneous green scheduling swarm intelligence particle swarm optimization (PSO) algorithm standard Brownian motion G-Brownian motion (geometric Brownian motion) blending pattern Electrical engineering. Electronics. Nuclear engineering TK1-9971 Shaohui Li Hong Liu Bin Gong Jinglian Wang A Greener Heterogeneous Scheduling Algorithm via Blending Pattern of Particle Swarm Computing Intelligence and Geometric Brownian Motion
description	This paper focuses on the algorithms design of heterogeneous green scheduling for energy conservation and emission reduction in cloud computing. In essence, the real time, dynamic and complexity of heterogeneous scheduling require higher algorithm performance; however, the swarm intelligent algorithms although with some improvements, still exist big imbalances between local exploration and macro development or between route (solution) diversity and faster convergence. In this paper, a greener heterogeneous scheduling algorithm via blending pattern of particle swarm computing intelligence and geometric Brownian motion, is proposed, based on our earlier theoretical breakthroughs on G-Brownian motion and through a series of mathematical derivations or proofs; furthermore, in order for suitable for the hybrid processor architecture of the scheduling management server, the algorithm is designed in parallel with deep fusion of coarse-grained and master-slave models. A large number of experimental results are given. Compared with most newly published scheduling algorithms, there are significant advantages of the proposed algorithm on the dynamic optimization performance for consistent or semiconsistent and large inconsistent scheduling instances, although with lower improvement factors for small inconsistent instances.
format	article
author	Shaohui Li Hong Liu Bin Gong Jinglian Wang
author_facet	Shaohui Li Hong Liu Bin Gong Jinglian Wang
author_sort	Shaohui Li
title	A Greener Heterogeneous Scheduling Algorithm via Blending Pattern of Particle Swarm Computing Intelligence and Geometric Brownian Motion
title_short	A Greener Heterogeneous Scheduling Algorithm via Blending Pattern of Particle Swarm Computing Intelligence and Geometric Brownian Motion
title_full	A Greener Heterogeneous Scheduling Algorithm via Blending Pattern of Particle Swarm Computing Intelligence and Geometric Brownian Motion
title_fullStr	A Greener Heterogeneous Scheduling Algorithm via Blending Pattern of Particle Swarm Computing Intelligence and Geometric Brownian Motion
title_full_unstemmed	A Greener Heterogeneous Scheduling Algorithm via Blending Pattern of Particle Swarm Computing Intelligence and Geometric Brownian Motion
title_sort	greener heterogeneous scheduling algorithm via blending pattern of particle swarm computing intelligence and geometric brownian motion
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/3c800159940e44fe93bb723ceae9ff2d
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A Greener Heterogeneous Scheduling Algorithm via Blending Pattern of Particle Swarm Computing Intelligence and Geometric Brownian Motion

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