@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} } @article {Ghosh201457, title = {Scalable analytics for IaaS cloud availability}, journal = {IEEE Transactions on Cloud Computing - IEEE Computer Society}, volume = {2}, number = {1}, year = {2014}, note = {cited By 5}, pages = {57-70}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {

In a large Infrastructure-as-a-Service (IaaS) cloud, component failures are quite common. Such failures may lead to occasional system downtime and eventual violation of Service Level Agreements (SLAs) on the cloud service availability. The availability analysis of the underlying infrastructure is useful to the service provider to design a system capable of providing a defined SLA, as well as to evaluate the capabilities of an existing one. This paper presents a scalable, stochastic model-driven approach to quantify the availability of a large-scale IaaS cloud, where failures are typically dealt with through migration of physical machines among three pools: hot (running), warm (turned on, but not ready), and cold (turned off). Since monolithic models do not scale for large systems, we use an interacting Markov chain based approach to demonstrate the reduction in the complexity of analysis and the solution time. The three pools are modeled by interacting sub-models. Dependencies among them are resolved using fixed-point iteration, for which existence of a solution is proved. The analytic-numeric solutions obtained from the proposed approach and from the monolithic model are compared. We show that the errors introduced by interacting sub-models are insignificant and that our approach can handle very large size IaaS clouds. The simulative solution is also considered for the proposed model, and solution time of the methods are compared. {\textcopyright} 2014 IEEE.

}, keywords = {Availability, availability analysis, cloud computing, Downtime, Existence of a solutions, Infrastructure as a service (IaaS), Iterative methods, Lakes, Maintenance, Markov processes, Model driven approach, Numeric solutions, Service level agreement (SLAs), simulation, Stochastic models, Stochastic reward nets, Stochastic systems}, issn = {21687161}, doi = {10.1109/TCC.2014.2310737}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84906976356\&partnerID=40\&md5=a63dc67c92ef7620c8a3b33aca08348b}, author = {Rahul Ghosh and Francesco Longo and Flavio Frattini and Stefano Russo and Kishor S. Trivedi} } @proceedings {Longo2011335, title = {A scalable availability model for Infrastructure-as-a-Service cloud}, journal = {Proceedings of the 41st IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)}, year = {2011}, note = {cited By 33; Conference of 2011 IEEE/IFIP 41st International Conference on Dependable Systems and Networks, DSN 2011 ; Conference Date: 27 June 2011 Through 30 June 2011; Conference Code:86090}, pages = {335-346}, publisher = {IEEE Computer Society}, address = {Hong Kong, Hong Kong, 27-30 June 2011}, abstract = {

High availability is one of the key characteristics of Infrastructure-as-a- Service (IaaS) cloud. In this paper, we show a scalable method for availability analysis of large scale IaaS cloud using analytic models. To reduce the complexity of analysis and the solution time, we use an interacting Markov chain based approach. The construction and the solution of the Markov chains is facilitated by the use of a high-level Petri net based paradigm known as stochastic reward net (SRN). Overall solution is composed by iteration over individual SRN sub-model solutions. Dependencies among the sub-models are resolved using fixed-point iteration, for which existence of a solution is proved. We compare the solution obtained from the interacting sub-models with a monolithic model and show that errors introduced by decomposition are insignificant. Additionally, we provide closed form solutions of the sub-models and show that our approach can handle very large size IaaS clouds. {\textcopyright} 2011 IEEE.

}, keywords = {Analytic models, availability analysis, Closed form solutions, Fixed-point iterations, High availability, Key characteristics, Large sizes, Markov Chain, Markov model, Markov processes, Petri nets, Scalability, Scalable methods, Solution time, Stochastic reward nets, Submodels}, isbn = {9781424492336}, doi = {10.1109/DSN.2011.5958247}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80051928903\&partnerID=40\&md5=37f3360476d39837acf5098ca20408c7}, author = {Francesco Longo and Rahul Ghosh and Vijay K. Naik and Kishor S. Trivedi} }