Sergej Svorobej

The CACTOS Vision of Context-Aware Cloud Topology Optimization and Simulation

Recent advances in hardware development coupled with the rapid adoption and broad applicability of cloud computing have introduced widespread heterogeneity in data centers, significantly complicating the management of cloud applications and data center resources. This paper presents the CACTOS approach to cloud infrastructure automation and optimization, which addresses heterogeneity through a combination of in-depth analysis of application behavior with insights from commercial cloud providers. The aim of the approach is threefold: to model applications and data center resources, to simulate applications and resources for planning and operation, and to optimize application deployment and resource use in an autonomic manner. The approach is based on case studies from the areas of business analytics, enterprise applications, and scientific computing.

A review of cloud computing simulation platforms and related environments

Recent years have seen an increasing trend towards the development of Discrete Event Simulation (DES) platforms to support cloud computing related decision making and research. The complexity of cl ...

Towards automated data-driven model creation for cloud computing simulation

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.

A Preliminary Systematic Review of Computer Science Literature on Cloud Computing Research using Open Source Simulation Platforms.

Research and experimentation on live hyperscale clouds is limited by their scale, complexity, value and and issues of commercial sensitivity. As a result, there has been an increase in the development, adaptation and extension of cloud simulation platforms for cloud computing to enable enterprises, application developers and researchers to undertake both testing and experimentation. While there have been numerous surveys of cloud simulation platforms and their features, few surveys examine how these cloud simulation platforms are being used for research purposes. This paper provides a preliminary systematic review of literature on this topic covering 256 papers from 2009 to 2016. The paper aims to provide insights into the current status of cloud computing research using open source cloud simulation platforms. Our two-level analysis scheme includes a descriptive and synthetic analysis against a highly cited taxonomy of cloud computing. The analysis uncovers some imbalances in research and the need for a more granular and refined taxonomy against which to classify cloud computing research using simulators. The paper can be used to guide literature reviews in the area and identifies potential research opportunities for cloud computing and simulation researchers, complementing extant surveys on cloud simulation platforms.

CactoSim simulation framework final prototype: accompanying document for project deliverable D6.4

SimPlugin # simulationProcess : SimProcess # remainingSimulationDuration : double # nextStopPoint : double +resumeSimulation(time : double):void +run():void +finishExecution():void +waitForSimProcessResume():void #stopFromSim(time: double double, mainEvent: SimEvent) :void Figure 75: Simulation Plugin class diagram The abstract methods to be implemented at the Simulation engine are grouped in four categories depending on their main aim: to control the simulation flow, to control the optimisation steps, reactive actions from simulation the simulation engine, and to manage the optimisation of resources.