# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 4 Summary of Selected Research
### Cloud Computing
However, the simulation addresses only one network topology and considers just one traffic pattern. [PERSON] et al. (2016) [42] propose a new model called Combinatorial Double Auction Resource Allocation (CDARA) to address biased market-based resource allocation by the cloud provider. CDARA focuses on the principle of equitable fairness to both providers and buyers. The scenario of multiple users and multiple cloud providers was simulated using four entities (user, broker, cloud provider, and cloud marketplace). CDARA has seven phases: (1) advertising and resource discovery where the provider proposes his services (resources), the user proposes his requirements, and the Cloud Information Service (CIS) creates the list of resources that matches with the user's requirements; (2) creating the auction and the bundle; (3) informing the end user of an auction; (4) determining the winner; (5) resource allocation; (6) pricing model; and (7) task allocation and payment. [PERSON] et al. use CloudSim to evaluate the performance and efficiency of CDARA based on an economic perspective and intensive compatibility. The simulation results show the effectiveness of the CDARA model. In CDARA, the winners are determined by a mediator; determining an optimal winner that maximises total profit for the cloud providers would seem to remain an open challenge. [PERSON] et al. (2017) [48] propose Delay-based Dynamic Scheduling (DDS) algorithm for dynamic resource provisioning and scheduling to minimise resource renting costs while meeting workflow deadlines. New VMs are dynamically rented by the DDS according to the practical execution state and estimated task execution times, to fulfil the workflow deadline. Bag-based deadline division and delay scheduling strategies consider the bag structure to decrease the total renting cost. [PERSON] et al. use a simulator called ElasticSim, an extension of CloudSim, to evaluate the performance of their proposed algorithm. The simulation results suggest that the dynamic algorithm decreases the resource renting cost while guaranteeing the workflow deadline compared to existing algorithms. [PERSON] et al. (2017) [50] propose an extension for CloudSim, MultiRE-CloudSim. They add a multi-resource scheduling and power consumption model which allows for a more accurate evaluation of power consumption in dynamic multi-resource scheduling for cloud computing. MultiRE-CloudSim offers the following capabilities to CloudSim: (1) the ability to change the configuration of host and power models of resources easily, and test the effect of the algorithm under different parameters; (2) simulation of tasks that demand multiple types of resources and define different resource allocation algorithms with fine-grained evaluation; (3) seamlessly switch between static load and dynamical load experiments which makes it able to simulate more scenarios; (4) power simulation of multi-resource scenarios which is more accurate when compared with single resource CPU power simulation. [PERSON] et al. evaluate the performance of their proposed simulator by comparing the results of the simulation with different parameters such as task makespan, power consumption, and SLA violation. [PERSON] et al. (2017) [51] examine the key objectives in cloud ecosystems i.e., energy consumption, SLA violation, and number of VM migrations. They consider the importance of different system criteria including CPU, RAM, and network bandwidth in the decision-making process. Resource management is addressed by proposing novel Dynamic Voltage and Frequency Scaling (DVFS)-aware algorithms to solve the four sub-problems of an online consolidation problem as well as a novel DVFS governor. The four consolidation sub-problems are (1) determination of overloaded PMs, (2) determination of under-loaded PMs, (3) selection of VMs that should be migrated from overloaded PMs, and (4) placement of migrating VMs on PMs.