Multi-task Scheduling Algorithm Based on Self-adaptive Hybrid ICA–PSO Algorithm in Cloud Environment

Author(s):  
Hamed Tabrizchi ◽  
Marjan Kuchaki Rafsanjani ◽  
Valentina Emilia Balas
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.


2020 ◽  
Vol 1427 ◽  
pp. 012007
Author(s):  
BJ. Hubert Shanthan ◽  
L. Arockiam ◽  
A. Cecil Donald ◽  
A.Dalvin Vinoth Kumar ◽  
R. Stephen

2019 ◽  
Vol 9 (1) ◽  
pp. 279-291 ◽  
Author(s):  
Proshikshya Mukherjee ◽  
Prasant Kumar Pattnaik ◽  
Tanmaya Swain ◽  
Amlan Datta

AbstractThis Paper focuses on multi-criteria decision making techniques (MCDMs), especially analytical networking process (ANP) algorithm to design a model in order to minimize the task scheduling cost during implementation using a queuing model in a cloud environment and also deals with minimization of the waiting time of the task. The simulated results of the algorithm give better outcomes as compared to other existing algorithms by 15 percent.


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