A Particle Swarm Optimization for Workflow Scheduling based on Energy-Awareness in a Cloud Computing Environment

2013 ◽  
Author(s):  
K. Sellami ◽  
M. Ahmed-Nacer ◽  
D. Dris ◽  
P.F. Tiako
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Workflow Scheduling ◽  
Energy Awareness ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment

A Dynamic Task Scheduling Algorithm for Cloud Computing Environment

2020 ◽  
Vol 13 (2) ◽  
pp. 296-307
Author(s):  
Hicham Ben Alla ◽  
Said Ben Alla ◽  
Abdellah Ezzati
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Waiting Time ◽  
Queue Length ◽  
Particle Swarm ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment ◽  
Tasks Scheduling ◽  
Annealing Algorithms

Background: Cloud computing environment is a novel paradigm in which the services are hosted, delivered and managed over the internet. Tasks scheduling problem in the cloud has become a very interesting research area. However, the problem is more complex and challenging due to the dynamic nature of cloud and users’ needs as well as cloud providers’ requirements including the quality of service, users’ priorities and computing capabilities. Objective: The main objective is to solve the problem of tasks scheduling through an algorithm which can not only improves the client satisfaction, but also allows cloud service provider to gain maximum profit and ensure that the cloud resources are utilized efficiently. Method: (a) Optimization of the waiting time and the queue length. Methods: (a) Optimization of the waiting time and the queue length. (b) Distribution of all requests into a novel queueing system in a dynamic manner based on a decision threshold. (c) Assignment of requests to VMs based on Particle Swarm Optimization and Simulated Annealing algorithms. (d) Incorporation of the priority constraint in the scheduling process by considering three priorities levels including the tasks, queues and VMs. Results: The results comparison of our algorithm with particle swarm optimization and First Come First Serve algorithms demonstrate the effectiveness of our algorithm in terms of waiting time, makespan, resources utilization and degree of imbalance. Conclusion: This study introduces an efficient strategy to schedule users’ tasks by using dynamic dispatch queues and particle swarm optimization with simulated annealing algorithms. Moreover, it incorporates the priority issue in the scheduling process.

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