Load Balancing Algorithms in Cloud, Fog Computing and Convergence of Fog and Cloud – A Survey

Cloud computing and fog computing are deployed as computing storage and services for the end-users. Fog computing promotes task performance through storage, computing, and networking services. Instead of taking place in centralized cloud computing data centers, these services can be provided via near-edge devices. Efficient load balancing in distributed computing systems has been the main challenge. The load balancing algorithm has an important role in enhancing the Quality of Service (QoS), throughput, and resource utilization and diminishing the potential cost and its strategy and architecture completely depend on the centralized or distributed architecture of the system and the type of requests. Cloud computing and fog computing use centralized and distributed architectures, respectively. The load balancing algorithm in these two environments cannot be the same. Meanwhile, the demand for near real-time processing requests is drastically increasing; load balancing should be able to handle real-time requests. This paper reviews and investigates the modern and diverse load balancing aspects of fog and cloud computing systems. We also categorize the load balancing algorithms in cloud and fog computing: meta-heuristic algorithms, heuristic algorithms, learning algorithms, and customized algorithms. We propose different research classes about the algorithm's type, objectives, simulation tools, and so forth. This review demonstrates that the most prevalent categories of methods used in load balancing in fog and cloud computing are custom approaches and meta-heuristic algorithms, respectively. While the most renowned load balancing algorithms have not yet succeeded in fog environments, meta-heuristic algorithms have shown their competence in cloud environments impeccably.