A New Optimization Approach for Task Scheduling Problem Using Water Cycle Algorithm in Mobile Cloud Computing

Abstract

Mobile devices are used by numerous applications that continuously need computing power to grow. Due to limited resources for complex computing, offloading, a service offered for mobile devices, is commonly used in cloud computing. In Mobile Cloud Computing (MCC), offloading decides where to execute the tasks to efficiently maximize the benefits. Hence, we represent offloading as a Task Scheduling Problem (TSP). This latter is a Multi-Objective Optimization (MOO) problem where the goal is to find the best schedule for processing mobile source tasks, while minimizing both the average processor energy consumption and the average task processing time. Owing to the combinatorial nature of the problem, the TSP in MCC is known as NP-hard. To overcome this difficulty in practice, we adopt meta-heuristic search techniques as they offer a good trade-off between solution quality and scalability. More precisely, we introduce a new optimization approach, that we call Multi-objective Discrete Water Cycle Algorithm (MDWCA), to schedule tasks from mobile source nodes to processor resources in a hybrid MCC architecture, including public cloud, cloudlets, and mobile devices. To evaluate the performance of our proposed approach, we conducted several comparative experiments on many generated TSP instances in MCC. The simulation results show that MDWCA outperforms the state-of-the-art optimization algorithms for several quality metrics.