@proceedings {Bruneo201598, title = {An SRN-based resiliency quantification approach}, journal = {Proceedings of the 36th International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets)}, volume = {9115 Lecture Notes in Computer Science}, year = {2015}, note = {cited By 0; Conference of 36th International Conference on Application and Theory of Petri Nets and Concurrency, Petri Nets 2015 ; Conference Date: 21 June 2015 Through 26 June 2015; Conference Code:119609}, pages = {98-116}, publisher = {Springer Verlag}, address = {Brussels, Belgium, 21-26 June 2015}, abstract = {

Resiliency is often considered as a synonym for faulttolerance and reliability/availability. We start from a different definition of resiliency as the ability to deliver services when encountering unexpected changes. Semantics of change is of extreme importance in order to accurately capture the real behavior of a system. We propose a resiliency analysis technique based on stochastic reward nets that allows the modeler: (1) to reuse an already existing dependability or performance model for a specific system with minimal modifications, and (2) to adapt the given model for specific change semantics. To automate the model analysis an algorithm is designed and the modeler is provided with a formalism that corresponds to the semantics. Our algorithm and approach is implemented to demonstrate the proposed resiliency quantification approach. Finally, we discuss the differences between our approach and an alternative technique based on deterministic and stochastic Petri nets and highlight the advantages of the proposed approach in terms of semantics specification. {\textcopyright} Springer International Publishing Switzerland 2015.

}, keywords = {Analysis techniques, Deterministic and stochastic Petri nets, Model analysis, Performance Model, Petri nets, Resiliency, Semantics, Stochastic models, Stochastic reward nets, Stochastic systems}, isbn = {9783319194875}, issn = {03029743}, doi = {10.1007/978-3-319-19488-2_5}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84937428490\&partnerID=40\&md5=7d0bbe99afba1a79df65b4e35e86a02c}, author = {Dario Bruneo and Francesco Longo and Marco Scarpa and Antonio Puliafito and Rahul Ghosh and Kishor S. 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} } @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} }