# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 5 Analysis and Discussion  
### Resource Management Mechanisms  
As we can see in Table 3, there is a significant imbalance in the evaluation of resource management across the C2T continuum. At a high level, every paper included resource scheduling. More specifically, 28 of the 35 papers (80%) focus on resource allocation; it was the primary focus of all papers regardless of C2T layer (see Table 3). This is not wholly unsurprising given that resource allocation is the central task in resource scheduling. With respect to other resource scheduling tasks, only two papers focus on resource monitoring [35; 63] and three others specifically on load balancing [65; 66; 67]. Five papers focus on resource mapping [41; 48; 50; 58; 68] all in relation to cloud computing rather than fog and edge computing. This suggests a significant opportunity for research in evaluating resource mapping, resource monitoring and load balancing using simulation, in all layers of the C2T continuum. With regard to resource provisioning, only nine papers [41; 48; 49; 50; 62; 64; 65; 66; 69] specifically addressed both detection and selection tasks; with the exception of [62; 69] in fog computing and [66] in edge computing, the remainder of articles focus on the cloud layer.  
There is much more variation in the variables investigated. As well as a variety of performance metrics (e.g., VM placement, scalability, run-time, latency, accuracy, failure rates, and resource utilisation), three major themes that emerge are energy consumption/efficiency, cost, and SLA adherence (see Table 3). These are largely consistent across research on all layers of the C2T continuum although the number of articles on fog and edge computing is low. Fog and edge computing have obvious idiosyncrasies that do not feature in the research on centralised cloud computing relating to the different resources that feature in architectures built on these paradigms, for example, network usage and utilisation of fog nodes, and importance, for example, latency, device energy constraints, and reliability.  
It is worth noting a significant difference between cloud computing and fog and edge computing in terms of focus. Resource management research in cloud computing overwhelmingly focuses on IaaS and as such the research focuses on the data center and infrastructure deployed in that environment for both input and output variables. In one sense, cloud computing models used in the simulation of resource management are much simpler. In contrast, fog and edge computing need to consider significantly expanded universe of variables not least the network infrastructure connecting the cloud to the fog and the edge, the configuration and location of the fog nodes and the edge devices, the connectivity of those edge devices, the capability and limitations of the form factors of the edge devices, and the use of context both in terms of user behaviour and situational context. All these factors complicate the time, effort and resources needed to undertake research across the cloud-to-thing continuum. Extant surveys identified more than 215 papers (of all types) on resource management in cloud computing, 105 on aspects of resource provisioning and 110 papers on aspects of resource scheduling. The research identified in this survey uses very similar QoS parameters to those reported in [8; 9]; however, there is significant variance in resource management mechanisms used. There would seem to be significant opportunities for researchers to revisit extant research in these areas to evaluate the efficacy of these mechanisms across the C2T continuum and not in centralised clouds and the impact on QoS parameters and particularly C2T-specific KPIs, e.g., latency, fog utilisation, etc. In particular, given the extreme complexity of the C2T continuum, nature and bio-inspired approaches (including self-managing and self-learning approaches) may provide particularly fruitful avenues for research.