# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 4 Summary of Selected Research
### Cloud Computing  
[PERSON] et al. (2015) [41] examine resource scheduling and provisioning for a scientific workflow ensemble on an Infrastructure as a Service (IaaS) cloud. The goal of the study was to maximise the number of prioritised workflows that can be completed given budget and deadline constraints. [PERSON] et al. develop three algorithms to solve this problem--(1) a dynamic provisioning dynamic scheduling (DPDS) algorithm, (2) a workflow-aware DPDS (WA-DPDS) algorithm that extends DPDS by introducing a workflow admission procedure, and (3) a static provisioning static scheduling (SPSS) algorithm that creates a provisioning and scheduling plan before running any workflow tasks. This enables SPSS to start only those workflows that it knows can be completed given the deadline and budget constraints, and eliminates any waste that may be allowed by the dynamic algorithms. The algorithms are evaluated using CloudSim on a range of synthetic workflows, which were generated based on statistics from real scientific applications. The results of the simulation studies indicate that the two algorithms that take into account the structure of the workflow and task run-time estimates, WA-DPDS and SPSS, yield better results than the simple priority-based scheduling strategy (DPDS), which makes provisioning decisions based purely on resource utilisation. [PERSON] et al. (2015) [44] explore consolidation in virtualised cloud data centers to tackle the problem of energy consumption and related increases in carbon emissions. They propose Enhanced Optimisation (EO) policy as a novel resource management procedure in cloud data center and novel multi-criteria algorithms for both phases of resource allocation, and determination of under-loaded physical machines (PMs) in cloud data centers. More precisely, they propose TPSA (TOPSIS (Technique of Order Preference Similarity to the Ideal Solution) Power and SLA-aware Allocation) as a novel policy for resource allocation as well as MDL (Migration Delay), AC (Available Capacity), and TACND (TOPSIS Available Capacity, Number of VMs, and Migration Delay) for determination of under-loaded PMs. [PERSON] et al. used CloudSim to test and evaluate the proposed policies and algorithms using synthetic workloads. They compare the simulation results using different parameters. Performance is measured by comparing energy consumption, number of VM migrations and SLA violations. [PERSON] (2015) [35] propose a new IaaS simulation framework called DISSECT-CF for simulating discrete event-based energy consumption for clouds and federations of clouds. It is designed for easy extensability, energy evaluation, and rapid evaluation of multiple scheduling and IaaS internal behaviours. The simulator has five main components: Event System (time reference), Unified Resource Sharing (resource consumption, resource scheduler), Energy Modelling (power state, consumption model), Infrastructure Simulation (VMs, PM, Network node), and Infrastructure Management (VM scheduling, PM scheduling). DISSECT-CF offers two major benefits: (i) a unified resource-sharing model, and (ii) a more complete IaaS stack simulation including, for example, VM image repositories, storage, and in-data-centre networking. DISSECT-CF is analysed by first validating it against the behaviour of a small-scale infrastructure cloud at the University of Innsbruck. The system simulated behaviour matched real-life experiments with negligible error (in terms of application execution time, larger-scale network transfers, and energy consumption). For larger-scale experiments, DISSECT-CF was validated by comparing it to CloudSim [6] and GrootSim [71] using both real-world and synthetic traces. The comparison results reveal that DISSECT-CF is scalable and produces more accurate results. [PERSON] et al. (2015) [43] propose an extension of CloudSim, DynamicCloudSim, for evaluating resource allocation and scheduling strategies for cloud computing.