Towards Automated Data-Driven Model Creation for Cloud Computing Simulation

Abstract

The increasing complexity and scale of cloud computing environments due to widespread data centre heterogeneity makes measurement-based evaluations highly difficult to achieve. Therefore the use of simulation tools to support decision making in cloud computing environments to cope with this problem is an increasing trend. However the data required in order to model cloud computing environments with an appropriate degree of accuracy is typically large, very difficult to collect without some form of automation, often not available in a suitable format and a time consuming process if done manually. In this research, an automated method for cloud computing topology definition, data collection and model creation activities is presented, within the context of a suite of tools that have been developed and integrated to support these activities.

Rapid Testing of IaaS Resource Management Algorithms via Cloud Middleware Simulation

“…The terminate request references the prior StartApplicationRequest, as the VM to be terminated is not yet running in the initial simulation model. [21] present an initial approach that leverages a runtime model snapshot from the CACTOS Runtime Toolkit as foundation for simulations. The runtime model lacks information on historically executed VMs and their workloads.…”

“…This paper contributes a novel model extraction approach that supports the reconstruction of timeline-based workload models from historical measurements. It leverages [21] to gather basic infrastructure information, i.e., on the available servers. Our model extraction approach applies the algorithm from [12] to reconstruct workload models for individual VMs.…”

“…This paper contributes extensions to the toolkit that enable data center operators and algorithm engineers to systematically investigate the effectiveness and efficiency of resource management algorithms. Our work builds upon [19,21,12], which we discuss in the following.…”

Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges

“…To solve the problem an automated approach is required for model building. Integration with a monitoring data collection system can partially address the challenge by taking snap-shot of an existing infrastructure state [25]. However, in order to build meaningful system behaviour models the monitoring data has to undergo additional processing to extract behaviour trends of workload and application resource demands.…”

Reliable capacity provisioning for distributed cloud/edge/fog computing applications

“…For networking simulation, NS-3 [17], DaSSF [18] and OMNET++ [19] are commonly used. Several simulation tools support cloud computing simulation and the design of these simulators can be divided into two classes: layered architectures representing virtualized data center components (CloudSim [20], EMUSIM [21], DCSim [22], SPECI [23], Palladio [24], CactoSim [25] ) and network-based components layout (GreenCloud [18], SimGrid [26]). As the RECAP Simulator is to assist the RECAP Optimizer in experimentation and evaluation, examples of related projects are the CACTOS project [27] and the CloudLightning project [28], where the simulation environment uses a footprint of the data center allowing the users to study different optimization strategies and VM placements.…”