Modified Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing Systems

2018 ◽  
pp. 357-365 ◽  
Author(s):  
G. Narendrababu Reddy ◽  
S. Phani Kumar
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Systems

An Energy-Aware Task Scheduling in the Cloud Computing Using a Hybrid Cultural and Ant Colony Optimization Algorithm

2017 ◽  
Vol 7 (4) ◽  
pp. 20-40 ◽  
Author(s):  
Poopak Azad ◽  
Nima Jafari Navimipour
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Heterogeneous Computing ◽  
Heuristic Algorithms ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Energy Aware

In a cloud environment, computing resources are available to users, and they pay only for the used resources. Task scheduling is considered as the most important issue in cloud computing which affects time and energy consumption. Task scheduling algorithms may use different procedures to distribute precedence to subtasks which produce different makespan in a heterogeneous computing system. Also, energy consumption can be different for each resource that is assigned to a task. Many heuristic algorithms have been proposed to solve task scheduling as an NP-hard problem. Most of these studies have been used to minimize the makespan. Both makespan and energy consumption are considered in this paper and a task scheduling method using a combination of cultural and ant colony optimization algorithm is presented in order to optimize these purposes. The basic idea of the proposed method is to use the advantages of both algorithms while avoiding the disadvantages. The experimental results using C# language in cloud azure environment show that the proposed algorithm outperforms previous algorithms in terms of energy consumption and makespan.

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