A Swarm Intelligence-based Approach for Dynamic Data Replication in a Cloud Environment
Authors: Ahmed Awad, Rashed Salem, Hatem Abdelkader, Mustafa Abdul Salam
Number of views: 148
In recent years, there has been increasing interest in cloud computing research, especially replication strategies and their applications. When the number of replicas is increased and placed in different places, maintaining the system’s data availability, performance and reliability will increase the cost. In this paper, two multi-objectives swarm intelligence algorithms are used to optimize the data replication selection and placement in a cloud environment. These algorithms are namely, multi-objective particle swarm optimization (MOPSO) and multi-objective ant colony optimization (MOACO). The first algorithm, (MOPSO), is used to find the best selected data replica according to the most popular data replication strategy. The improved time-based decay function (ITBDF), is used to enhance the proposed model. The second algorithm, (MOACO), is used to find the best data replica placement according to the minimum distance, the number of data transmissions and the availability of data replication. A simulation of the suggested strategy has been performed using CloudSim. the Cloud is formed to simulate different kinds of datacenters (DCs) with different structures. Moreover, 21 DCs are used. Each DC consists of a host that contains a set of virtual machines (VMs) that provides blocks of available data replications. Three different data placements for high datacenters were created. A total of one thousand cloudlets are randomly confirmed for the data replication order. All replication files are placed in high datacenters and randomly distributed in the suggested system. The performance of proposed strategy was evaluated relative to many well-known strategies such as, Enhance Fast Spread (EFS), Dynamic Cost-aware Re-replication and Re-balancing Strategy (DCR2S), Genetic Algorithm (GA), Genetic adaptive Selection Algorithm (GASA), Replica Selection and Placement (RSP), Dynamic Replica Selection Ant Colony Optimization (DRSACO), Adaptive Replica Dynamic Strategy (ARDS), Popular File Replication First (PFRF). The experimental results show that MOPSO, achieves better data replication than compared algorithms. Additionally, MOACO, achieves higher data availability, lower cost, and less bandwidth consumption than compared algorithms.