Edge computing (EC) has emerged as a paradigm to support computation-intensive Internet of Things (IoT) applications by enabling task offloading to nearby servers. Despite its potential, the inherent heterogeneity of edge resources and the dynamic, unpredictable nature of task arrivals present significant challenges for designing and evaluating effective load balancing strategies. Traditional evaluation methods are limited as follows: physical testbeds lack scalability and flexibility, while abstract simulators often oversimplify network behavior, failing to capture realistic system dynamics. To address these limitations, we present an emulated dynamic edge computing framework (EDECF) designed for evaluating load balancing schemes in EC networks. First, we developed dedicated service models for each EC node within the EDECF and implemented them using the common open research emulator (CORE) platform, thereby providing a scalable, flexible, and realistic environment for testing optimization strategies. Second, we introduced a robust fitness function that explicitly models latency, queue stability, and fairness for metaheuristic-based load balancing under dynamic edge conditions. To assess its effectiveness, this function was incorporated and tested using the following methods: the particle swarm optimization, genetic algorithm, differential evolution and simulated annealing-based load balancing algorithms. In addition, baseline methods such as the round robin and shortest queue techniques were also deployed to demonstrate the framework’s capacity to facilitate rigorous analysis in heterogeneous and time-varying scenarios. Overall, results are presented to demonstrate EDECF’s capability to emulate realistic workloads, capture resource variability at the edge, and support comprehensive evaluation of algorithmic performance across diverse network settings. Thus, this work aims to establish a practical and extensible foundation for researchers and practitioners to design, test, and optimize load balancing strategies in EC environments.
Molokomme et al. (Sun,) studied this question.