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Improved PSO-based Task Scheduling Algorithm in Cloud Computing
Shaobin Zhan,Hongying Huo +1 more
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TLDR
The results show that this method can reduce the task average running time, and raises the rate availability of resources, in the resources scheduling strategy of the cloud computing.Abstract:
Job scheduling system problem is a core and challenging issue in cloud computing. How to use cloud computing resources efficiently and gain the maximum profits with job scheduling system is one of the cloud computing service providers’ ultimate goals. For characteristics of particle swarm optimization algorithm in solving the large-scale combination optimization problem easy to fall into the search speed slowly and partially the most superior, the global fast convergence of simulated annealing algorithm is utilized to combine particle swarm optimization algorithm in each iteration, which enhances the convergence rate and improves the efficiency. This paper proposed the improve particle swarm optimization algorithm in resources scheduling strategy of the cloud computing. Through experiments, the results show that this method can reduce the task average running time, and raises the rate availability of resources.read more
Citations
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Symbiotic Organism Search optimization based task scheduling in cloud computing environment
TL;DR: Results revealed that DSOS outperforms Particle Swarm Optimization which is one of the most popular heuristic optimization techniques used for task scheduling problems and performs significantly better than PSO for large search spaces.
Journal ArticleDOI
A comprehensive survey for scheduling techniques in cloud computing
TL;DR: A systematic review as well as classification of proposed scheduling techniques along with their advantages and limitations of cloud computing are provided.
Journal ArticleDOI
A Survey of PSO-Based Scheduling Algorithms in Cloud Computing
TL;DR: This paper presents an in-depth analysis of the Particle Swarm Optimization-based task and workflow scheduling schemes proposed for the cloud environment in the literature and provides a classification of the proposed scheduling schemes based on the type of the PSO algorithms which have been applied and illuminates their objectives, properties and limitations.
Journal ArticleDOI
CLOUDRB: A framework for scheduling and managing High-Performance Computing (HPC) applications in science cloud
TL;DR: A CLOUD Resource Broker (CLOUDRB) for efficiently managing cloud resources and completing jobs for scientific applications within a user-specified deadline is designed and integrated with a Deadline-based Job Scheduling and Particle Swarm Optimization-based Resource Allocation mechanism.
Journal ArticleDOI
A PSO‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment
TL;DR: This paper presents a static task scheduling method based on the particle swarm optimization (PSO) algorithm where the tasks are assumed to be non‐preemptive and independent and is compared with round robin task scheduling, improved PSO task scheduling and a load‐balancing technique.
References
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M. Clerc,James Kennedy +1 more
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TL;DR: An adaptive particle swarm optimization that features better search efficiency than classical particle Swarm optimization (PSO) is presented and can perform a global search over the entire search space with faster convergence speed.
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Adaptive Particle Swarm Optimization
Zhi-Hui Zhan,Jun Zhang +1 more
TL;DR: An adaptive particle swarm optimization with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach is proposed, resulting in substantially improved quality of global solutions.
Journal ArticleDOI
Optimal choice of parameters for particle swarm optimization
TL;DR: The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature and guidelines for determining parameter values are given.
Journal ArticleDOI
Capacity planning and scheduling in Grid computing environments
TL;DR: This paper proposes an approach to Grid scheduling which abstracts over the details of individual applications, focusing instead on the global cost optimisation problem while taking into account the entire workload, dynamically adjusting to the varying service demands.