@article {Longo201737, title = {An approach for resiliency quantification of large scale systems}, journal = {Performance Evaluation Review}, volume = {44}, number = {4}, year = {2017}, note = {cited By 0}, pages = {37-48}, publisher = {Association for Computing Machinery}, abstract = {

We quantify the resiliency of large scale systems upon changes encountered beyond the normal system behavior. Formal definitions for resiliency and change are provided together with general steps for resiliency quantification and a set of resiliency metrics that can be used to quantify the effects of changes. A formalization of the approach is also shown in the form of a set of four algorithms that can be applied when large scale systems are modeled through stochastic analytic state space models (monolithic models or interacting sub-models). In particular, in the case of interacting submodels, since resiliency quantification involves understanding the transient behavior of the system, fixed-point variables evolve with time leading to non-homogenous Markov chains. At the best of our knowledge, this is the first paper facing this problem in a general way. The proposed approach is applied to an Infrastructure-As-A-Service (IaaS) Cloud use case. Specifically, we assess the impact of changes in demand and available capacity on the Cloud resiliency and we show that the approach proposed in this paper can scale for a real sized Cloud without significantly compromising the accuracy.

}, keywords = {Available capacity, Chains, Formal definition, Homogenous Markov chain, Infrastructure as a service (IaaS), Large scale systems, Markov processes, Non-homogeneous, Resiliency quantification, State - space models, State space methods, Stochastic models, Stochastic systems, Submodels, Transient behavior}, issn = {01635999}, doi = {10.1145/3092819.3092825}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019853222\&doi=10.1145\%2f3092819.3092825\&partnerID=40\&md5=6266fffb7aad937fefad706e31fcd7da}, author = {Francesco Longo and Rahul Ghosh and Vijay K. Naik and A.J. Rindos and Kishor Trivedi} } @proceedings {Ghosh2017227, title = {Resiliency quantification for large scale systems: An IaaS cloud use case}, journal = {ValueTools 2016 - 10th EAI International Conference on Performance Evaluation Methodologies and Tools}, year = {2016}, note = {cited By 0; Conference of 10th EAI International Conference on Performance Evaluation Methodologies and Tools, ValueTools 2016 ; Conference Date: 25 October 2016 Through 28 October 2016; Conference Code:127816}, pages = {227-234}, publisher = {Association for Computing Machinery}, address = {Taormina; Italy; 25-28 October 2016}, abstract = {

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 {\textcopyright} 2016 EAI.

}, keywords = {Available 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}, isbn = {9781631901416}, doi = {10.4108/eai.25-10-2016.2266805}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021320622\&doi=10.4108\%2feai.25-10-2016.2266805\&partnerID=40\&md5=a6167b2554cad8a9aae02963254c8b52}, author = {Rahul Ghosh and Francesco Longo and Vijay K. Naik and A.J. Rindos and Kishor Trivedi} } @proceedings {Ghosh2010343, title = {Quantifying resiliency of IaaS cloud}, journal = {Proceedings of the 29th IEEE Symposium on Reliable Distributed Systems (SRDS)}, year = {2010}, note = {cited By 16; Conference of 29th IEEE Symposium on Reliable Distributed Systems, SRDS 2010 ; Conference Date: 31 October 2010 Through 3 November 2010; Conference Code:82914}, pages = {343-347}, publisher = {IEEE Computer Society}, address = {New Delhi, India, 31 October - 3 November 2010}, abstract = {

Cloud based services may experience changes - internal, external, large, small - at any time. Predicting and quantifying the effects on the quality-of-service during and after a change are important in the resiliency assessment of a cloud based service. In this paper, we quantify the resiliency of infrastructure-as-a-service (IaaS) cloud when subject to changes in demand and available capacity. Using a stochastic reward net based model for provisioning and servicing requests in a IaaS cloud, we quantify the resiliency of IaaS cloud w.r.t. two key performance measures - job rejection rate and provisioning response delay. {\textcopyright} 2010 IEEE.

}, keywords = {Available capacity, Infrastructure as a services, Performance measure, Quality of service, Rejection rates, Response delays, Stochastic models, Stochastic reward nets}, isbn = {9780769542508}, issn = {10609857}, doi = {10.1109/SRDS.2010.49}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78650562592\&partnerID=40\&md5=6542308f50d349fc7456919e658e6840}, author = {Rahul Ghosh and Francesco Longo and Vijay K. Naikz and Kishor S. Trivedi} }