A New Hybrid Metaheuristic Algorithm for Energy-Aware Data Replication Based on IoT Applications

Abstract The Internet of Things allows us to connect different devices and objects, generate a large volume of huge data, process it, and store it on cloud computing. Recently, cloud computing has become necessary in using pay-for-use spaces to save time and cost. Used data replication in traditional cloud computing leads to excessive use of resources and devices, performance burdens, increased loads, excessive energy consumption, advanced user waiting, and improved response time. This paper proposes the use of multi-objective optimization (MOO) in combination with harris hawks optimization (HHO) and IoT-based salp swarm algorithm (SSA) in cloud computing. The new method called Multi-objective optimization Harris Hawks Optimization with Salp Swarm Algorithm (MOHHOSSA) is based on a set of objectives and integrating two critical technologies, HHO and SSA, to identify and place data replication across nodes using the least path between nodes in cloud computing. The proposed algorithm improves energy consumption (EC), carbon dioxide emission rate (CDER), and means service time (MST). The laboratory results proved the superiority of the proposed algorithm MOHHOSSA over other algorithms in terms of energy, load balancing, Mean service time, and the least expensive path between nodes in the proposed system.