Analysis of Optimal Task Scheduling Using Optimization Algorithms in Cloud-Edge Computing

K. Vinothkumar, Dr. D. Maruthanayagam

Abstract: Edge-cloud computing involves deploying a set of edge servers near mobile devices so that these devices can schedule tasks to the servers with low latency. One fundamental and critical problem in edge-cloud systems is determining how to dispatch and schedule tasks in such a way that the task response time (defined as the interval between the release of a task and the arrival of the computation result at its device) is minimized. Edge computing (EC), which distributes resources to the network edge, is gaining traction in applications requiring low latency and high reliability. Nowadays, EC provides resources in a decentralized manner; a large number of cloud-based services are deployed on the network's edge, as processing data at the edge can reduce costs. This paper presents an optimised scheduling algorithm based on the Lion Optimization Algorithm (LOA), PSO (Particle Swarm Optimization), Firefly Algorithm (FF), and Ant Colony Optimization (ACO) to improve cloud edge task scheduling. To achieve a better result in terms of reducing energy consumption and increasing the lifespan of the cloud-edge system after task completion (LOA). The simulation results demonstrate the efficacy of the comparison by employing research parameters such as Task delay, Task Completion Time, Execution Time, Average Make span, Energy consumption, Average response time, and Energy Latency.

Keywords: Edge Cloud Computing, Lion Optimization Algorithm, Particle Swarm Optimization, Firefly Algorithm, Ant Colony Optimization, Optimization Algorithms, Energy Consumption, Average Makespan