# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 2 Related Works
### Resource Management  
Resource management is an umbrella term that encompasses all the characteristics and usage of resources in the cloud. It includes two main steps: (i) resource provisioning (resource detection and resource selection), and (ii) resource scheduling (resource mapping, resource allocation, resource monitoring and load balancing) [10]. These two steps face similar challenges, namely dispersion, uncertainty and heterogeneous resources. As such research has focused on new research provisioning methods and resource scheduling algorithms so that users receive cloud services within the time, cost and QoS parameters set out in the SLA [8; 9]. Resource management in joint edge-fog computing environments faces similar challenges to traditional cloud computing however with significantly greater resource constraints, heterogeneity, multi-tenancy, and dynamism [5; 11]. While a detailed discussion of resource management is beyond the scope of this paper, a number of surveys have been completed on resource management for cloud computing and, to a lesser extent, fog and edge computing that are worth referencing. [PERSON] and [PERSON] (2016) [9] is one of the most widely-cited survey papers on resource provisioning. They categorise literature from 2007 to 2014, identifying different types of study, and classifying resource provisioning mechanisms and their sub-types. In their analysis of 105 papers, only eight different simulators were identified for validating resource provisioning in the cloud--CloudSim, CloudAnalyst, GreenCloud, NetworkCloudSim, EMUSIM, SPECI, GroundSim and DCSim [9]. [PERSON] and [PERSON] (2016) [8] present a similarly widely-cited survey on resource scheduling based on works published from 2005 to mid-2015. They classify papers by study type, resource type, scheduling algorithms (and subtypes) and QoS parameters, resource distribution policies and resource scheduling tools. Again, the same eight simulators as [9] were identified. Unsurprisingly given the time, neither fog nor edge computing appear in either survey. More recently, [PERSON] et al. (2019) [10] published a survey of 209 papers published from 2010 to 2018 on scheduling techniques in cloud computing. While they note advancement in the state of the art, they also highlight the same underlying resource management issues persist. In the context of this paper, it is worth noting that [PERSON] et al. (2019) [10] analyse the evaluation methods and simulation tools used, and note simulation is used in 81% of the papers; however, they only identify six simulators--CloudSim, CloudAnalyst, EMUSIM, GroundSim, GreenCloud, and NetworkCloudsSim. Fog computing is referenced as an opportunity for future research and edge computing is not referenced at all. In their recent survey of resource management in fog and edge computing, [PERSON] et al. (2019) [5] analysed 84 publications of various types from 1991 to 2018; 86% of which were published after 2013. They categorise the literature by architectures, infrastructure, and algorithms for resource management. Only two simulators are referenced in this work--iFogSim and EdgeCloudSim, both extensions of CloudSim. Similarly, [PERSON] et al. (2019) [12] published a survey of 100 papers on resource management in fog computing; their analysis identified the preponderance of iFogSim and CloudSim in 49% of studies reviewed but also noted use of OmNet++, Java JMT as well as other mathematical simulation techniques. [PERSON] et al. (2019) [11] recently published a survey of 73 papers on resource provisioning for edge-cloud environments. Of the 38 papers identified using simulation, the majority used mathematical simulation and simulation software was only identified in two studies, both of which used CloudSim or extensions thereof. As can be seen from this brief overview, resource management is a key research topic in cloud, fog and edge computing.