A Flawless QoS Aware Task Offloading in IoT Driven Edge Computing System using Chebyshev Based Sand Cat Swarm Optimization

The rise in networks and end devices with limited resources highlights the need for efficient processing, where edge computing plays a vital role by offloading tasks to nearby nodes for faster response times. Offloading tasks to edge nodes minimizes response times and solves user demands but presents challenges, particularly in optimizing task scheduling to ensure efficient resource utilization and improved Quality of Service (QoS). In this study, the Chebyshev-based Sand Cat Swarm Optimization (Ch_SCSO) algorithm is introduced to optimize the task throughput in edge computing environments. By effectively managing the allocation of heterogeneous computational resources across edge nodes, Ch_SCSO addresses the limitations of existing offloading techniques, reducing execution time and improving overall performance. The proposed technique against established benchmarks is evaluated using metrics such as makespan, transmission delay, execution delay, energy consumption, and simulation time. The experimental results show that the proposed method significantly outperforms the current approaches, achieving a makespan of 101.82 s for 200 tasks, a transmission delay of 5277.04 ms for 50 tasks, and an execution delay of 5205.4 ms for 50 tasks. Additionally, energy consumption metrics indicate 166.81 J for 12 users and 10.48 J at a CPU frequency of 0.2 GHz, underscoring the algorithm's efficiency. The Ch_SCSO algorithm demonstrates substantial improvements in QoS, providing a robust solution for IoT-driven edge computing systems.