Resilience is an important aspect of computing systems. Previous work on resilience has often focused on the design and architectural aspects of such systems, and not on the quantification of resilience. In addition, quantification is often restricted to a limited portion of the system. In networked systems, where multiple heterogeneous components interact in a complex manner, resilience quantification becomes a nontrivial problem. This paper proposes a model for quantifying resilience on the basis of the interdependencies of services and their adaptation. It combines performance and adaptability metrics to compute resilience of individual services that are then applied to a Markov network that computes the overall system resilience. The adaptation metric, here called adaptivity, computes how often the service adapts and evaluates the efficiency of such adaptations in terms of performance improvement. This paper also presents an evaluation that considers critical infrastructure systems.

Item Type:	Refereed Article
Keywords:	adaptation models, computational modeling, Markov random fields, mathematical model, predictive models, resilience
Research Division:	Information and Computing Sciences
Research Group:	Distributed computing and systems software
Research Field:	Distributed systems and algorithms
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Information systems, technologies and services not elsewhere classified
UTAS Author:	Garg, SK (Dr Saurabh Garg)
ID Code:	93815
Year Published:	2013
Web of Science® Times Cited:	11
Deposited By:	Information and Communication Technology
Deposited On:	2014-08-19
Last Modified:	2018-02-28
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