Heuristics and Meta-Heuristics based Algorithms for Resource Optimization in Fog Computing Environment: A Comparative Study

Fog computing is a challenging task because it requires all of the available resources, which increases as the number of users working in this environment grows. Most existing resource allocation techniques focus on providing performance for a particular workload, so more users create more demand for resources. The resource management algorithm is an NP-complete problem that has varying time requirements depending on the size of the problem. Optimal management of resources in fog computing can be done through heuristic, meta-heuristic and hybrid approaches. The meta-heuristics approaches can handle large search spaces and discover better solutions for resource management within a reasonable time. This paper presents a detailed review of heuristic and metaheuristic algorithms for resource optimization in fog computing environments. It compares each category of the algorithm based on performance metrics, tools used, advantages, and disadvantages so to make informed decisions about which algorithm will work best for any formulated problem. These algorithms are capable of achieving more than the previous state-of-the-art performance at lower cost, as well as improved utilization of resources and increased energy efficiency.