Resiliency quantification for large scale systems: An IaaS cloud use case

TitleResiliency quantification for large scale systems: An IaaS cloud use case
Publication TypeConference Proceedings
Year of Conference2016
AuthorsGhosh, R., F. Longo, V. K. Naik, A.J.. Rindos, and K. Trivedi
Conference NameValueTools 2016 - 10th EAI International Conference on Performance Evaluation Methodologies and Tools
PublisherAssociation for Computing Machinery
Conference LocationTaormina; Italy; 25-28 October 2016
ISBN Number9781631901416
KeywordsAvailable capacity, Chains, Clouds, Homogenous Markov chain, Impact of changes, Infrastructure as a service (IaaS), Large scale systems, Markov processes, Non-homogeneous, Numerical results, Resiliency, Submodels, Transient behavior

We quantify the resiliency of large scale systems upon changes encountered beyond the normal system behavior. General steps for resiliency quantification are shown and resiliency metrics are defined to quantify the effects of changes. The proposed approach is illustrated through an Infrastructureas-a-Service (IaaS) Cloud use case. Specifically, we assess the impact of changes in demand and available capacity on the Cloud resiliency using interacting state-space based submodels. Since resiliency quantification involves understanding the transient behavior of the system, fixed-point variables evolve with time leading to non-homogenous Markov chains. In this paper, we present an algorithm for resiliency analysis when dealing with such non-homogenous sub-models. A comparison is shown with our past research, where we quantified the resiliency of IaaS Cloud performance using a one level monolithic model. Numerical results show that the approach proposed in this paper can scale for a real sized Cloud without significantly compromising the accuracy. Copyright © 2016 EAI.