On Resilient Behaviors in Computational Systems and Environments

The present article introduces a reference framework for discussing resilience of computational systems. Rather than a property that may or may not be exhibited by a system, resilience is interpreted here as the emerging result of a dynamic process. Said process represents the dynamic interplay between the behaviors exercised by a system and those of the environment it is set to operate in. As a result of this interpretation, coherent definitions of several aspects of resilience can be derived and proposed, including elasticity, change tolerance, and antifragility. Definitions are also provided for measures of the risk of unresilience as well as for the optimal match of a given resilient design with respect to the current environmental conditions. Finally, a resilience strategy based on our model is exemplified through a simple scenario.Comment: The final publication is available at Springer via http://dx.doi.org/10.1007/s40860-015-0002-6 The paper considerably extends the results of two conference papers that are available at http://ow.ly/KWfkj and http://ow.ly/KWfgO. Text and formalism in those papers has been used or adapted in the herewith submitted pape

Level of resilience measure for communication networks

Our daily life applications have come to depend on communication networks to deliver services in an efficient manner. This has made it possible for an attacker to sabotage its operation.Network resiliency is concerned with the degree the network is able to bounce back to a normal operation in the face of attacks.This paper introduced a new resiliency measure, called Levelof- Resilience (LoR) for communication networks, determined by examining: (a) the Level-of-Stability-Reduction (LoSR), as measured by percentage of "IP traffic dropped", (b) the eventual Level-of-Performance-Reduction (LoPR), as captured by the percentage of reduction in the application Quality-of-Service (QoS), namely latency and (c) Recovery-Time (RT), which is the time the network takes to detect and recover from an attack or a fault, as measured by convergence duration. Previous resiliency measures may only consider one aspect of the above parameters, while this measure is a composite of them. This paper showed that network topology can affect the network resilience, as indicated by the LoR metric.This measure is illustrated by comparing the resiliency level of two communication networks that served the same traffic, but differed in their network topology, under three different attack scenarios

Resilient infrastructure networks :managing the impacts of disruptive events on resource movements

Phd ThesisInterdependencies between infrastructures which enable the flow resources have the potential to increase the vulnerability of interconnected systems of supply chains to disruption via cascading mechanisms. These interactions are poorly understood as there are limited observations whilst the movement of resources can occur at many spatial scales. It is a complex problem because of both the number of components and the dynamic nature of the systems that allow these to move around. To analyse the disruption of resource flows within interdependent systems, this paper introduces a resource model that pulls together two established modelling methodologies: input-output modelling and network analysis. Data on supply, demand and flows are typically only provided at coarse spatial scales, so an important development was the disaggregation of regional economic input-output data into smaller spatial units. The model was tested using a case study of Lerwick in the Shetland Islands. It was found, when flood defences were taken into account, the level of risk from storm surges of various magnitudes was low. The model was able to highlight unknown linkages and reaffirm an increase in vulnerability caused by Just-in-time management strategies and the clustering of like industries. As part of this a flood risk analysis technique was presented which highlighted the potential impacts of floods of varying magnitudes, as well how the flood protection affected the levels of risk caused by these events. A second case study of the food distribution network in New York was also developed to provide validation through the recreation of the effects post Tropical Storm Sandy. The research provided a rationale for an encouragement of a move away from just-in-time production to take place and halt the fashion of making supply chains leaner. It also encouraged an increase in cooperation to take place between companies to understand the vulnerabilities within their own supply chains