FACO: a hybrid fuzzy ant colony optimization algorithm for virtual machine scheduling in high-performance cloud computing

Abstract

High-performance cloud computing has recently become the focus of much interest. Extensive research has shown that scheduling and load balancing are among the key aspects of performance optimization. The allocation of a set of requests into a set of computing resources, which is considered as an NP-hard problem, aims to distribute efficiently the load within the cloud architecture. To resolve this problem, the last decade has seen a growing trend towards using hybrid approaches to combine the advantages of different algorithms. In this paper, we propose a hybrid fuzzy ant colony optimization algorithm (FACO) for virtual machine scheduling to guarantee high-efficiency in a cloud environment. The proposed fuzzy module evaluates historical information to calculate the pheromone value and select a suitable server while keeping an optimal computing time. The experimental work presented in this study provides one of the first investigations into how to choose the optimal parameters of ant colony optimization algorithms using the Taguchi experimental design. We have simulated the proposed algorithm through the Cloud Analyst and CloudSim simulators by applying different cloud configurations to evaluate the performance of the proposed algorithm. Our findings highlight how response time and processing time are improved compared to the Round Robin algorithm, Throttled algorithm and Equally Spread Current Execution Load algorithm, especially in the case of a high number of nodes. FACO algorithm could be applied to define efficient cloud architecture adapted to high-performance applications.