FACO: a hybrid fuzzy ant colony optimization algorithm for virtual machine scheduling in high-performance cloud computin
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ORIGINAL RESEARCH
FACO: a hybrid fuzzy ant colony optimization algorithm for virtual machine scheduling in high‑performance cloud computing Awatif Ragmani1 · Amina Elomri1 · Noreddine Abghour1 · Khalid Moussaid1 · Mohammed Rida1 Received: 24 July 2019 / Accepted: 30 October 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
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. Keywords Ant colony optimization · Fuzzy logic · Cloud computing · Load balancing · Scheduling · Taguchi DOE
1 Introduction Cloud computing corresponds to programs and services that run on a distributed network based on virtualized infrastructure and accessed using common Internet protocols and networking standards. It is an efficient and economical model for provisioning different types of services. Cloud computing is based on two main concepts including abstraction that is based on the idea of pooling physical resources and virtualization (Sosinsky 2011). The virtualization technique is based on the concept of sharing and abstraction of material resources. Thus, a physical machine can host multiple virtual machines (VMs) and can be used by several users at the same time. Virtualization relies on a central operating * Awatif Ragmani [email protected] 1
LIMSAD Laboratory, Faculty of Sciences Ain Chock, University Hassan II of Casablanca, 20100 Casablanca, Morocco
system called a host system. This technology has different benefits such as energy efficiency, optimization of infrastr
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