# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 3 Methods and Review Framework
### Data Extraction  
The following data were extracted for each of the 35 articles--bibliographic data, quality indicators, the resource management context, the variable or KPI under study, the C2T context (cloud, fog, edge or some combination thereof), the simulation software, and the simulation methodology. Data were extracted by two authors independently and compared. A third author cross-checked on random samples. Again, where conflict arose, a compromise meeting was convened and issues resolved. Extracted data were stored in a shared spreadsheet and entered using a form designed for this purpose. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Inclusion Criteria**} & \multicolumn{1}{c}{**Exclusion Criteria**} \\ \hline Full-text & Uncompleted studies \\ \hline Published at any time & Non English \\ \hline Published in the above selected databases & Duplicated studies \\ \hline Published in a Scopus Q1 or Q2 journal & Studies on Grid Computing \\ \hline Study manuscript written in English on the topic of & Studies using analytical modelling techniques, \\ evaluation of resource management across the C2T & e.g., Petri Nets, Markov Chains,Fault Trees, Reliability \\ continuum using simulation frameworks & Graphs, and Reliability Block Diagrams \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Inclusion and Exclusion Criteria. \begin{table} \begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**No** & **Title** & **Authors** & **Journal (Published)** & **Year** & **Scopus Ranking** & **Clution** \\ \hline
1 & Algorithms for cost- and deadline-constrained provisioning for scientific workflow ensembles in last clouds & [PERSON] et al. [41] & Future Generation Computer Systems (Elsevier) & 2015 & Q1 & 433 \\ \hline
2 & DISSECT-CF: A simulator to foster energy-aware scheduling in infrastructure clouds & [PERSON] [53] & Simulation Modelling Practice and Theory (Elsevier) & 2015 & Q1 & 46 \\ \hline
3 & DynamicCloudClouds: Simulating heterogeneity in computational clouds & [PERSON] and [PERSON] [43] & Future Generation Computer Systems (Elsevier) & 2015 & Q1 & 78 \\ \hline
4 & Novel energy and SLA: Efficient resource management heuristics for consolidation of virtual machines in cloud data centers & [PERSON] et al. [44] & Computers \& Electrical Engineering (Elsevier) & 2015 & Q2 & 82 \\ \hline
5 & Workload modeling for resource usage analysis and simulation in cloud computing & [PERSON] et al. [48] & Computers \& Electrical Engineering (Elsevier) & 2015 & Q2 & 77 \\ \hline
6 & A combinatorial double auction resource allocation model in cloud computing & [PERSON] et al. [42] & Information sciences (Elsevier) & 2016 & Q1 & 178 \\ \hline
7 & Topoke-Aware Virtual Machine Placement in data centers & [PERSON] and [PERSON] [46] & Journal of Grid Computing (Springer) & 2016 & Q1 & 21 \\ \hline
8 & A joint CPU-RAM energy efficient and SLA-compliant approach for cloud data centers & [PERSON] et al. [47] & Computer Networks (Elsevier) & 2016 & Q1 & 19 \\ \hline
9 & CTSpin: Modelling and simulation of Complex Event Processing systems in cloud environments & [PERSON] et al. [27] & Future Generation Computer Systems (Elsevier) & 2016 & Q1 & 33 \\ \hline \hline \end{tabular}   
* A delay-based dynamic scheduling algorithm for bug-of-task workflows with stochastic task execution times in clouds
* Location-aware networking for consumers in multi-cloud computing environments
* Multi-resource scheduling and power simulation for cloud computing