Opti-FOG: Optimizing Resource Allocation With Anti-Inspired FOG Node Selection

Abstract

Fog computing is a decentralized environment capable of data collection, storage, and analysis, in addition to providing cloud computing services to edge devices. In a fog computing environment, several noteworthy challenges have been faced with data storage technologies. Based on the literature, to enhance fog node data storage when traffic in a specific location exceeds capacity, the capacity of the fog node is enhanced by adding another fog node to the existing node to accommodate the demand. However, selecting a neighbor node in networking brings several challenges, such as node capacity, security, and availability. This article proposes a framework Optimizing Resource Allocation with Anti-Inspired Fog Node Selection (Opti-FOG), for resolving the issues of node capacity and availability for selecting the optimal node to allow a storage system to grow or shrink without impairing the performance of surrounding nodes. The proposed framework, Opti-FOG, addresses the challenges of fog node selection in decentralized environments, enhancing both latency and availability in fog computing systems. By leveraging the Ant Colony Optimization (ACO) algorithm, Opti-FOG optimizes resource allocation while considering proximity and weighted node values, thereby improving the efficiency and responsiveness of fog node selection processes. The simulation results underscore the significance of Opti-FOG in accurately assessing fog node selection performance, demonstrating its potential for practical deployment scenarios where latency and availability are crucial factors.

Keywords

• Ant colony optimization
• fog computing
• latency
• security
• scalability