A multi-objective optimized replication using fuzzy based self-defense algorithm for cloud computing. (1st December 2020)

• Record Type: Journal Article
• Authors: Mansouri, N.; Mohammad Hasani Zade, Behnam; Javidi, Mohammad Masoud
• Is Part Of: Journal of network and computer applications. Volume 171(2020)
• Publication Date: 2020-12-01
• Subjects: Cloud computing -- Replication -- Self-defense -- Fuzzy -- Simulation; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2020.102811
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Cloud computing has attracted increasing attention in data management. Data replication, which brings files closer to the data consumers, is a well-known technique that reduces access time and bandwidth consumption. This paper addresses two issues concerning replica placement process. The first is how to reduce access costs and replication costs that are two conflicting goals. To achieve this, we propose a multi-objective optimized placement algorithm based on meta-heuristic technique and fuzzy system that finds the optimal locations for replicas by balancing the trade-offs among the six optimization objectives (i.e., system availability, service time, load, energy consumption, latency, and centrality). The second issue is how to determine the optimal number of replicas since storing a great number of replicas in cloud is expensive. To solve this problem, we determine the number of replicas without excessively reducing the performance. In addition, we improve self-defense algorithm by a new prey-predator model based on a fuzzy system to simulate the interaction between prey and predator population. The superior results with ten benchmark functions demonstrate the merits of the proposed fuzzy-self-defense algorithm in solving the problems compared with seven optimization algorithms. Moreover, the extensive simulations with CloudSim prove that the proposed replication algorithm outperforms the main existing replication strategies in terms of hit ratio, number of replications, load variance, latency, average service time, availability, and energy consumption.