Dynamic Resource Allocation for Load Balancing in Fog Environment

TLDR

Fog computing extends cloud capabilities to the edge, enabling IoT applications to run locally, but achieving load balancing across fog nodes remains a significant challenge. This study introduces DRAM, a dynamic resource allocation method aimed at balancing load in fog environments. DRAM combines a fog‑computing framework with static allocation and dynamic service migration, and its effectiveness is demonstrated through experimental evaluation and comparative analysis.

Abstract

Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load‐balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM.