Advancing Task Scheduling in Edge Computing for Energy Efficiency: A Multi-Objective Method

Authors

Anila Gogineni

Abstract

The rising popularity of the Internet of Things has made energy efficiency an essential factor throughout IoT service system design and development processes. The edge computing model currently receives widespread attention as a modern approach to computing. The presented paper details a novel Task Scheduling Algorithm (TSA), which uses VM heterogeneity to dynamically place tasks based on system requirements. TSA prioritizes execution efficiency using key performance metrics, including task completion time, memory usage, energy consumption, as well as the total running time that it implements in the CloudSim 3.0.3 simulation framework. TSA’s superiority is shown to be in comparison with traditional methods of scheduling like First-Come, First-Served (FCFS) and Particle Swarm Optimization (PSO). Experimental data indicates that TSA finishes tasks in 6.6 units, which exceeds both FCFS (69.9 units) and PSO (9.63 units). Additionally, TSA optimizes energy consumption at 3690 units while maintaining efficient memory usage and execution speed. TSA demonstrates its capability to enhance cloud resource control as well as workload placement between multiple nodes which results in an energy-efficient scheduling system.

Keywords

Edge Computing, Task Scheduling, Multi-Objective Optimization, Energy Efficiency, Resource Allocation, Computation Offloading, Low-Latency Computing, IoT, Cloud-Edge Collaboration, Performance Optimization

Advancing Task Scheduling in Edge Computing for Energy Efficiency: A Multi-Objective Method. Anila Gogineni. 2023. IJIRCT, Volume 9, Issue 1. Pages 1-12. https://www.ijirct.org/viewPaper.php?paperId=2503004