Solving Task Scheduling Problem in Mobile Cloud Computing Using the Hybrid Multi-Objective Harris Hawks Optimization Algorithm

Abstract

Nowadays, mobile devices can run a wide range of programs, and they all require more and more processing power. Due to their limited resources, mobile devices often make use of cloud computing $'\text{s}$ offloading features to do more complex tasks. The offloading problem in Mobile Cloud Computing (MCC) is the task scheduling problem, which entails deciding where to dump work to maximize its value. The task scheduling problem in MCC is an NP-hard problem because of the difficulty in moving resources and the size of the search space required to find the ideal scheduler, making the use of extensive search techniques impractical. For this reason, metaheuristic search strategies are provided, to yield a best-case or near-best-case scenario in terms of job completion time and energy savings. This work provides a non-dominated multi-objective strategy based on the Harris Hawks Optimization (HHO) technique called Hybrid Multi-objective Harris Hawks Optimization (HMHHO) to handle the described issue in MCC. The objectives of this research were allocating jobs from mobile source nodes to processors in the public cloud, cloud patches, and processors in mobile resources. In comparison to the other four algorithms - the Genetic Algorithm (GA), the Ant Colony Optimization (ACO), the Particle Swarm Optimization (PSO), and the Cuckoo Search Algorithm (CSA) the proposed method completes jobs faster and uses less energy on average.