Tuna Optimization Algorithm-Based Data Placement and Scheduling in Edge Computing Environments

Mobile edge computing (MEC) represents the promising technology that targets at facilitating different resources for processing and storing near the edge of mobile devices. Nevertheless, limited availability of resources in MECs possesses a necessity for adopting an appropriate management for preventing wastage of resources. In specific, the scheduling of workflow indicates a process that attempts in the mapping of tasks to the most suitable set of resources available in MECs based on the satisfaction of the objectives. In this paper, Tuna Optimization Algorithm-based Data Placement and Scheduling (TOA-DPS) is proposed with improved convergence by preventing the problems of local optima to map tasks to most suitable resources. It adopted a method of task prioritization for determining the order in which the tasks in the scientific workflows are executed. Further, Tuna Optimization Algorithm (TOA) is utilized for attaining data-demanding workflow scheduling along with data placement using Dynamic Voltage and Frequency Scaling (DVFS) in MEC environments. The performance evaluation of proposed TOA-DPS-based scheduling mechanism is conducted using extensive simulations carried out over renowned scientific workflows of various sizes. The outcomes of the proposed TOA-DPS-based scheduling scheme confirmed better performance of data access by 21.38%, and minimized energy consumption by 19.84%, better than the baseline approaches used for investigation.