# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 4 Summary of Selected Research
### Fog Computing  
[PERSON] et al. (2017) [28] propose an extension to CloudSim, iFogSim, which is capable of modelling fog computing and IoT scenarios. It enables simulation of resource management and application scheduling policies across edge and cloud resources under different scenarios and conditions. Additionally, iFogSim allows application designers to validate the design of their application with regards to cost, network use, and latency. Resource management is the core component of the iFogSim architecture and consists of components that coherently manage resources of the Fog device layer in such a way that application-level QoS constraints are met and resource wastage is minimised. The paper also presents a simple IoT simulation recipe and two case studies to demonstrate how one can model an IoT environment and plug in and compare resource management policies. [PERSON] (2018) [54] propose a social network analysis (SNA) technique to detect resource deadlock at the fog layer. SNA is a rule-based approach for priority resource allocation. If the deadlock is detected, SNA tries to find free resources from jobs already allocated, and if the deadlock is not solved, the job is sent to the cloud. The allocation is based on rules and priorities. [PERSON] uses CloudSim to evaluate the performance of their proposed technique and examine resource utilisation and request processing metrics. Simulation results suggest that SNA provides an effective solution to detect and solve deadlock in the fog and enables efficient resource utilisation in both the cloud and fog layers. Notwithstanding this, the study does not compare SNA to other state-of-the-art techniques. [PERSON] et al. (2018) [60] propose an energy-aware allocation policy for placing application modules in fog devices. It is an energy-efficient strategy that allocates the incoming tasks to Fog devices based on the remaining CPU capacity and energy consumption. The aim of the proposed allocation strategy is to increase energy efficiency at fog devices by allocating application tasks based on improved Round Robin and DVFS algorithms. [PERSON] et al. use iFogSim to evaluate the performance of the proposed approach. Remote patient monitoring was used as the use to evaluate the energy efficiency of the proposed placement strategy. The performance of the proposed strategy is evaluated by comparing it with the default allocation and cloud-only policies. Simulation results suggest that the proposed solution is more energy-efficient with approximately 2.72% in energy savings compared to cloud-only policies and approximately 1.6% in energy savings compared to the fog-default. [PERSON] et al. (2018) [61] propose FogNetSim++, a novel simulation framework that provides detailed configuration options to simulate large fog network scenarios. The framework is based on OMNet++ and incorporates customised mobility models, fog node scheduling algorithms, and handover management mechanisms. FogNetSim++ can simulate network traffic at a packet level including packet drop, delay, network errors, handovers, latency, and re-transmissions. To evaluate the performance of the simulator, a communication network scenario is simulated based on a smart traffic management system. The simulation analysis demonstrates the scalability and effectiveness of the FogNetSim++ in terms of CPU and memory usage. The network parameters, such as execution delay, packet error rate, handovers, and latency are also benchmarked to demonstrate the FogNetSIM++'s ability to simulate network functionality. [PERSON] et al. (2018) [62] present a study of a popular applications used in the Fog/IoT paradigm, followed by an architectural design for resource management using a container middleware layer. The proposed architecture allows dynamic scaling of compute and network resources to meet the demands of IoT and meet required QoS. In addition, the proposed management architecture is extended by a bin packing heuristic that works with scaling options to jointly reduce energy consumption.