Energy Efficient Load Balancing Algorithms in Fog Computing

Abstract

Fog computing is an amalgamation of many network technologies working at edges of networks, thus differentiating itself from cloud computing with an increased focus on traffic load balancing at edges. Fog computing combines shared, geographically distributed and heterogeneous resources to achieve high computational performance. The objective of fog computing is to provide enhanced responsiveness with reduced latency, that too near to the user (at one hop distance). Fog computing offloads huge tasks to cloud and performs latency-sensitive tasks with the help of collaborative heterogeneous fog nodes within the fog network, nearer to the end devices. This helps in reduction of excessive traffic on the network along with the optimal usage of the available resources. These resources may belong to homogeneous or heterogeneous environments like devices working in different institutions, different domains and may pose tasks that requires high computations. One of the major challenge in such heterogeneous and complex computing environments is devising energy-efficient load balancing algorithm(s) in fog environment. Such algorithm(s) should be efficient, robust, and scalable with optimal use of available resources. To meet the growing traffic needs on the Internet as well as for optimal or minimal energy utilization between available fog nodes present in the fog zone, energy-efficient load balancing algorithm(s) are the current needs in fog computing environment. Efficient use of such algorithms will produce better Quality of Service (QoS) parameters (such as latency, responsiveness, availability, bandwidth, scalability, storage, energy consumption etc.) and increases performance of the system. This research work mainly focuses on Energy-Efficient Load Balancing Algorithms in Fog Computing , which deals with designing of energy efficient fog load balancer and optimizing the fog network paths for better traffic management. Initially, an in-depth review of existing models, approaches and algorithms has been done.