Identification of vulnerable selfish peer in P2P network using nature-inspired optimization techniques

Abstract

Microservice is considered a distributed cloud native approach where a standalone application is composed of multiple loosely coupled and free-spirited deployable components in the form of peers. These peers are heterogeneous and autonomous by nature and behave according to their good interests. In a distributed environment, some peers subsidize others and could behave vulnerably or selfishly. These selfish peers severely affect the performance of the distributed P2P network if not addressed adequately. In distributed microservices applications, these selfish peers are addressed in two ways, i.e. Identification and Mitigation of the selfish peers. This study presents the AI-based approach towards identifying selfish peers in P2P microservices distributed networks with nature-inspired algorithms for feature selection. Efforts are also made to generate a real dataset SAMPARK followed by the standard procedure for the distributed P2P network. The developed hybrid feature selection (FS) technique composed of Grey-Wolf-Optimization (GWO) and Particle-Swarm-Optimization (PSO) is used for selecting the important feature subset. The feature selection technique combines AI-enabled techniques to develop six hybrid models for the identification of selfish peers in microservice networks. In experimentation, the effectiveness of all the proposed algorithms and models is found satisfactory, with an achieved maximum accuracy of 99.60% and outperforming other proposed approaches.