DATA: Throughput and Deadline-Aware Genetic Approach for Task Scheduling in Fog Networks

Abstract

Fog devices in fog computing frameworks are responsible for fetching and executing the tasks submitted by the deployed resource-constraint embedded edge devices. Based on the availability of resources, tasks are offloaded to the virtual machines hosted by the fog devices. These tasks may then get scheduled to guarantee a number of efficiency-related metrics. While throughput has a decisive impact on the timely execution of tasks, the appropriate utilization of this metric has not been considered in the existing mechanisms. In this letter, we first discuss the proper use of this objective in the fitness function of meta-heuristic algorithms. Then, we explain that adopting throughput by the fitness functions in the form of two conventionally used weighted-sum, and fractional techniques may ignore solutions with a better guarantee ratio. Consequently, we propose a novel approach called DATA to be replaced with these two old approaches. DATA is a throughput, and deadline-aware task scheduling mechanism for time-sensitive fog frameworks, which its fitness function utilizes genetic optimization by encoding the solutions into chromosomes. It uses single gene mutation and two-point crossover. In this approach, two populations are considered to search the problem space. The main population is evaluated based on the guarantee ratio, while the helper population is evaluated based on the throughput. Furthermore, the helper population uses weighted-sum. The initial population is generated randomly by the uniform distribution, to provide a load-balancing. Based on our extensive evaluations, the selected solution by DATA provides the highest guarantee ratio, while having the lowest possible makespan.