Authors- Saurabh Jain, Mahesh kr. Sharma
Abstract- Edge computing reduces latency by bringing computation closer to end devices, but the growing scale and heterogeneity of edge networks make resource management increas-ingly complex. Load balancing is essential for efficient resource use and low response times, yet static approaches struggle in dynamic environments. In this paper, a novel load balancing model is proposed in which task sequences are first generated using a trained mathematical model, and each generated sequence is further optimized using genetic algorithms. Use of genetic algorithm has increases the efficiency as dynamic situa-tion is manage by the algorithm. Experiments are conducted across various environ-ments, and the results demonstrate that the artificial immune-based model outperforms the group search algorithm in terms of overall performance.
