# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 6 Conclusions and Future Directions  
This paper provides a methodical survey of scholarly literature of top quartile journal articles that evaluate resource management mechanisms across the C2T continuum using simulation software tools. While Computer Science research traditionally places greater emphasis on conference publications and those publications can garner comparatively favourable citations to journals, inconsistent acceptance and rejection rates and regional bias can skew citations [77]. Rightly or wrongly, in the wider academic community, peer review journal publication continues to be perceived to be of higher academic and review rigour. As such, a decision was made to limit this review to publications in the two top quartiles (Q1 and Q2) of journals. To this end, from an initial pool of 317 journal articles across all time, 35 relevant articles on the survey topic from January 2015 to March 2019 were reviewed. Resource management evaluation using simulation software emerged in top quartile journal publications in 2015 for cloud computing in 2015 with the adoption of CloudSim, but publications on fog computing and edge computing are a recent phenomenon. They can be directly traced back to the release and first publications on iFogSim in 2017. This is consistent with efforts to agree on definitions and reference architectures on fog computing and edge computing by NIST and others in 2017 and 2018 [3; 74]. Given the significant interest in the IoT and the conference proceedings pipeline, it is likely the trend towards increased publications on resource management across the cloud-to-thing continuum is likely to continue. From a resource management perspective, the review reveals a heavier research interest in resource scheduling over resource provisioning, and specifically in resource allocation; 28 of the 35 papers reviewed dealt with resource allocation. There are not proportionate efforts in the resource provisioning (resource detection and resource selection), and the wider resource scheduling tasks, e.g., resource mapping, resource monitoring, and load balancing are significantly under-represented, even allowing for the small sample size. Similarly, the evaluation of resource management mechanisms for cloud computing is the dominant theme. There were only ten fog and edge computing articles in total. While this is relatively low compared to cloud computing, it reflects the nascent stage of research on these topics. This represents an opportunity to revisit extant studies, policies, algorithms and other techniques on resource management and investigate their applicability in new fog and edge computing contexts. In particular, we encourage researchers to explore how nature or bio-inspired approaches to resource management mechanisms, including self-organising, self-managing and self-learning approaches, might be used to address the complexity of resource management across the C2T continuum. Likewise, there is a need to explore whether the appropriate variables and optimisation functions under examination are the most important and appropriate for C2T continuum contexts. The KPIs for each layer, cloud, fog, edge, the endpoint, and indeed the network infrastructure supporting these, are likely to be significantly different and contingent on context much more than a centralised (albeit distributed) cloud computing model, which is the basis for much of extant research. Given the particular idiosyncrasies and scale of the cloud-fog-edge environments and the criticality of many use cases, not least e-health and autonomous transport, and the importance of QoE in highly competitive markets, more complex modelling and simulation will be needed. Highly contextualised infrastructure, user, workload distribution, workload propagation, QoS and QoE models are required. Future research needs to explore, simulate, and evaluate mechanisms not only for each of these in isolation but how they impact each other.