iFogSim2: An extended iFogSim simulator for mobility, clustering, and microservice management in edge and fog computing environments

Internet of Things (IoT) has already proven to be the building block for next-generation Cyber-Physical Systems (CPSs). The considerable amount of data generated by the IoT devices needs latency-sensitive processing, which is not feasible by deploying the respective applications in remote Cloud datacentres. Edge/Fog computing, a promising extension of Cloud at the IoT-proximate network, can meet such requirements for smart CPSs. However, the structural and operational differences of Edge/Fog infrastructure resist employing Cloud-based service regulations directly to these environments. As a result, many research works have been recently conducted, focusing on efficient application and resource management in Edge/Fog computing environments. Scalable Edge/Fog infrastructure is a must to validate these policies, which is also challenging to accommodate in the real-world due to high cost and implementation time. Considering simulation as a key to this constraint, various software have been developed that can imitate the physical behavior of Edge/Fog computing environments. Nevertheless, the existing simulators often fail to support advanced service management features because of their monolithic architecture, lack of actual dataset, and limited scope for a periodic update. To overcome these issues, we have developed modular simulation models for service migration, dynamic distributed cluster formation, and microservice orchestration for Edge/Fog computing based on real datasets and extended the basic components of iFogSim, a widely used Edge/Fog computing simulator for their ease of adoption as iFogSim2. The performance of iFogSim2 and its built-in service management policies are evaluated using three use case scenarios and compared with the contemporary simulators and benchmark policies under different settings. Results indicate that our simulator consumes less memory and minimizes simulation time by an average of 28% when compared to other simulators. (C) 2022 Elsevier Inc. All rights reserved.