Cloud service composition using an inverted ant colony optimisation algorithm. (3rd June 2019)

• Record Type: Journal Article
• Authors: Asghari, Saied; Navimipour, Nima Jafari
• Is Part Of: International journal of bio-inspired computation. Volume 13:Number 4(2019)
• Page Start: 257
• Page End: 268
• Publication Date: 2019-06-03
• Subjects: cloud computing -- inverted ant colony -- service composition -- load balancing -- optimal cloud composition; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

In recent years, clouds are becoming an important platform for scientific applications. Service composition is a growing approach that increases the number of applications of cloud computing by reusing attractive services. However, more available approaches focus on producing composite services from a single cloud, limiting the benefits derived from other clouds. Furthermore, in many traditional service composition methods, there is a key problem called load balancing that was inefficient among cloud servers. Therefore, this paper proposes the inverted ant colony optimisation (IACO) algorithm, a variation of the basic ant colony optimisation (ACO) algorithm, to solve this problem. This method inverts its logic by converting the effect of pheromone on the selected path by ants in order to improve load balancing among cloud servers. In this method, ants begin to traverse the graph from the start node and each ant selects the best node for moving, then other ants may not follow the track travelled by the previous ants. We evaluate the performance of the proposed method in comparison with the ACO, greedy and COM2 algorithms in terms of the obtained optimal cloud composition, load balancing, waiting time, cost and execution time.