# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 4 Summary of Selected Research
### Cloud Computing
Simulation results show PSO-COGENT reduces execution time, execution cost, task rejection ratio, energy consumption, and increases the throughput in comparison to benchmark algorithms i.e., POS, Honey Bee and Min-Min. [PERSON] et al. (2018) [55] propose a dynamic resource optimisation methodology for SDN-enabled Big Data Stream Processing (BDSP) in public clouds. Their algorithm, named SDN Base VM Mapping for Resource Optimisation (SBVMMRO), uses CPU, memory and storage metrics to allocate workload. They also consider BDSP workloads including networking and computation. [PERSON] et al. use an extension of CloudSim, CloudSimDN, to validate their methodology by comparing delay times and the number of servers needed to process different types of BDSP workloads. Experimental results reveal the utility of an SDN-based approach for cloud resource optimisation for BDSP by decoupling data forwarding and network controlling. [PERSON] et al. (2018) [37] examine the performance of a novel dynamic cloud resource allocation framework based on the principles of self-organisation and self-management (SOSM). The evaluation is performed using a warehouse scale model of a cloud data center and a parallel time-advancing loop-based simulation framework. Simulation experiments take into account resource heterogeneity including hardware accelerators such as Graphics Processing Units (GPU), Many Integrated Cores (MIC) and Field Programmable Gate Arrays (FPGA). The SOSM framework evaluation is conducted by analysing resource utilisation parameters such as CPU, memory, network and energy consumption. Results suggest that the proposed approach enables more accurate decisions on the placement of VMs over resources compared to the traditional centralised management approach. [PERSON] et al. (2018) [58] propose SCORE, a hybrid discrete-event multi-agent scheduling and resource utilisation simulator to reduce energy consumption in cloud computing environments. SCORE comprises two parts: (a) an energy-aware independent batch scheduler; and (b) a set of energy-efficiency policies for the hibernation of idle VMs. Four scheduling policies have been proposed for the control of energy consumption and the makespan during the assignation of tasks to VMs. The proposed scheduler takes into account the security demands of each task and trust levels of VMs that are computing those tasks. Additionally, the proposed simulator enables the compute of energy consumption for the whole system including the energy spent on performing security operations. [PERSON] et al. test and validate their proposed simulator using different policies and parameters. Results suggest a reduction of up to 45% of cloud system energy consumption costs. [PERSON] et al. (2019) [68] propose a Multi-objective Cuckoo Search Optimisation (MOCSO) algorithm for dealing with the resource scheduling problem in cloud computing. The algorithm reduces costs for cloud users and enhances performance by minimising task execution time and maximising resource utilisation. Improved resource utilisation enables increased revenue for cloud providers as more user requests can be processed with existing hardware. The MOCSO algorithm is evaluated using CloudSim and its performance compared against other multi-objective algorithms i.e., ant colony, genetic, multiple order model migration and particle swarm. Simulation results suggest that the proposed MOCSO algorithm performs better than the existing algorithms, and that it balances multiple objectives for expected time to completion and expected cost to completion matrices for resource scheduling in the IaaS cloud computing environment. [PERSON] et al. (2019) [64] extend CloudSim to support the modelling of heterogeneous workloads and resources such as CPU in the cloud. The extended features also support scheduling policies, VM migration and energy consumption. Energy consumption is optimised by solving the problem of heap memory, by removing the VM object when finished and forcing the Java garbage collector to clear memory. [PERSON] et al.