  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 has been
developed that can imitate the physical behaviour 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 multiple simulation models for service
migration, dynamic distributed cluster formation, and microservice
orchestration for Edge/Fog computing in this work and integrated with the
existing iFogSim simulation toolkit for launching it as iFogSim2. The
performance of iFogSim2 and its built-in policies are evaluated using three use
case scenarios and compared with the contemporary simulators and benchmark
policies under different settings. Results indicate that the proposed solution
outperform others in service management time, network usage, ram consumption,
and simulation time.

Distributed, Parallel, and Cluster Computing;Performance;Software Engineering