# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 4 Summary of Selected Research
### Cloud Computing
They propose novel fuzzy multi criteria and an objective resource allocation algorithm that enables resource administrators to weight different criteria in the resource management solution by importance using fuzzy weights. CloudSim is used to evaluate the performance of the proposed algorithms. Experimental results suggest that the combination of the proposed resource management policies leads to a notable reduction in both energy consumption and SLA violations compared to benchmarks. [PERSON] et al. (2018) [52] not only evaluate CPU usage but propose an algorithm using learning automate overload detection (LAOD) to predict CPU usage of PMs based on user resource usage history in order to improve resource utilisation and reduce energy consumption. The technique is tested using CloudSim and compared to benchmark algorithms. The results suggest that the approach outperforms comparators in reducing data center energy consumption. In addition, this study illustrates the benefit of using learning algorithms to predict resource usage for efficient resource management in the cloud. [PERSON] et al. (2018) [56] propose Energy-aware Task-based Virtual Machine Consolidation (ETVMC), a new task-based VM placement algorithm for mapping tasks to a VM, and a VM to a PM, in the cloud. The goal of ETVMC is to efficiently allocate tasks to VMs and then VMs to hosts so the allocation minimises energy consumption, makespan and task rejection rate. The proposed approach is evaluated and tested using CloudSim, and the simulation results demonstrate the effectiveness of the proposed algorithm compared to FCFS (First-Come First-Served), Round-Robin and EERACC (Energy Efficient Resource Allocation in Cloud Computing). [PERSON] et al. (2018) [53] propose an heuristic approach for optimising task scheduling and resource allocation in the cloud. The heuristic is a combination of four methods: (i) the modified analytic hierarchy process (MAHP), (ii) bandwidth aware divisible scheduling (BATS) + BAR (Balance-Reduce) optimisation, (iii) longest expected processing time preemption (LEPT), and (iv) divide-and-conquer methods. The MAHP is used to process the task before its allocation in the cloud while BATS + BAR optimisation is used to allocate the resources for that given task. The optimisation takes into consideration the bandwidth and cloud resources as constraints. The LEPT is used to preempt resource-intensive tasks. The divide-and-conquer method is applied to aggregate the results after task preemption (scheduling and allocation). [PERSON] et al. use CloudSim to test and evaluate their proposed approaches. Experimental results suggest that the proposed approach outperforms the existing BATS algorithm in terms of turnaround time and response time metrics. [PERSON] et al. (2018) [22] propose a highly parallel MPI- and OpenMP-based simulation framework for hyperscale cloud computing, the CloudLightning Simulator. The simulation implements a sequential search and deploy algorithm that iterates through available resources to deploy VMs and tasks. The validation of the framework is based on number of tasks deployed, task execution time and resource utilisation parameters. Results suggest that the proposed simulator can cater for extremely large numbers of cloud nodes, the execution of extremely large numbers of tasks, and can support CPU, memory and network over-commitment. [PERSON] et al. (2018) [57] propose a resource allocation model for processing applications efficiently in the cloud, PSO-COGENT. PSO-COGENT uses the meta-heuristic based particle swarm optimisation (PSO) approach to allocate the task to resources in an optimal way. PSO-COGENT optimises execution time, cost, and reduces energy consumption in cloud data centers, considering deadline as a constraint. The problem is formulated as a multi-objective scheduling problem in the form of a mathematical model and defined objective and fitness functions. [PERSON] et al. use CloudSim to evaluate the performance of their proposed algorithm.