Behaviour Analysis of Interdependent Critical Infrastructure Components upon Failure

Urban life increasingly depends on intact critical infrastructures (CIs). For this reason, protecting critical infrastructure systems from natural disasters and man-made hazards has become an important topic in urban development research in recent years as a prerequisite for building and optimizing smart cities. To increase efficiency, the connections between CIs have been strengthened increasingly, resulting in highly interdependent large-scale infrastructure systems that are vulnerable to cascading failures. Hence, studying the cascading and feedback effects caused by the failure of a CI component in a given system can help strengthen this system. Understanding the response of the system in the event of a disaster can lead to better disaster management and better planning of critical infrastructures in the future. The population heavily depends on water, electricity, and the transportation network. These three components also depend on each other to function individually. This complex nature of interdependencies must be studied in order to understand the effects induced in one system due to the failure of another. The three systems (water, transport, and electricity) and their interdependencies can be modeled using graph theory. Water, transport, and electricity networks can be further broken down into smaller components. For example, the water network comprises water treatment plants, water storage tanks, pumping stations, sewage treatment, etc. interdependency factors into the model when, for instance, a pumping station depends on electricity. Graph theory can be used to depict the pairwise relationship between the individual components. Each node in the graph represents a critical infrastructure and the edges between these critical infrastructures represent their dependency. The modeled graph is a multigraph (inter-network dependency) and multidirectional (mutual dependence of two or more components). The idea behind building this model is to simulate the response of the interdependent systems upon failure. Building a simulation tool with an underlying interdependency graph model can not only help in understanding the failure response, but can also help in building a robust system for preserving the infrastructures. The data obtained from the simulation results will contribute to a better emergency response in the event of a disaster. The failure response of a system depends largely on the failed component. Hence, three cases are considered to simulate and identify the state of the system upon failure of a component: The failed component can be a node with maximum outward dependencies, a node with maximum inward dependencies, or a random failure of a component. If a component has the maximum number of outward edges, the simulation tool will help visualize the cascading effects, whereas a system with the maximum number of incoming edges will contribute to the understanding of the feedback response as the outward nodes are not affected immediately. Another goal of CI failure analysis is to develop an algorithm for the partial restoration of specific critical services when a CI is not working at full capacity. The selection of critical infrastructure components for restoration is based on the number of people being affected

Collusion in Peer-to-Peer Systems

Peer-to-peer systems have reached a widespread use, ranging from academic and industrial applications to home entertainment. The key advantage of this paradigm lies in its scalability and flexibility, consequences of the participants sharing their resources for the common welfare. Security in such systems is a desirable goal. For example, when mission-critical operations or bank transactions are involved, their effectiveness strongly depends on the perception that users have about the system dependability and trustworthiness. A major threat to the security of these systems is the phenomenon of collusion. Peers can be selfish colluders, when they try to fool the system to gain unfair advantages over other peers, or malicious, when their purpose is to subvert the system or disturb other users. The problem, however, has received so far only a marginal attention by the research community. While several solutions exist to counter attacks in peer-to-peer systems, very few of them are meant to directly counter colluders and their attacks. Reputation, micro-payments, and concepts of game theory are currently used as the main means to obtain fairness in the usage of the resources. Our goal is to provide an overview of the topic by examining the key issues involved. We measure the relevance of the problem in the current literature and the effectiveness of existing philosophies against it, to suggest fruitful directions in the further development of the field

Improved Algorithms for MST and Metric-TSP Interdiction

We consider the MST-interdiction problem: given a multigraph G = (V, E), edge weights {w_e >= 0}_{e in E}, interdiction costs {c_e >= 0}_{e in E}, and an interdiction budget B >= 0, the goal is to remove a subset R of edges of total interdiction cost at most B so as to maximize the w-weight of an MST of G-R:=(V,E-R). Our main result is a 4-approximation algorithm for this problem. This improves upon the previous-best 14-approximation [Zenklusen, FOCS 2015]. Notably, our analysis is also significantly simpler and cleaner than the one in [Zenklusen, FOCS 2015]. Whereas Zenklusen uses a greedy algorithm with an involved analysis to extract a good interdiction set from an over-budget set, we utilize a generalization of knapsack called the tree knapsack problem that nicely captures the key combinatorial aspects of this "extraction problem." We prove a simple, yet strong, LP-relative approximation bound for tree knapsack, which leads to our improved guarantees for MST interdiction. Our algorithm and analysis are nearly tight, as we show that one cannot achieve an approximation ratio better than 3 relative to the upper bound used in our analysis (and the one in [Zenklusen, FOCS 2015]). Our guarantee for MST-interdiction yields an 8-approximation for metric-TSP interdiction (improving over the 28-approximation in [Zenklusen, FOCS 2015]). We also show that maximum-spanning-tree interdiction is at least as hard to approximate as the minimization version of densest-k-subgraph

Network diversity and maritime flows

Coupled and interdependent networks constitute a relatively recent research field that has been so far little invested by port and maritime specialists. The extent to which certain ports benefit from being connected to multiple commodity flows in the maritime network has in fact been poorly addressed. A global database of merchant vessel inter-port movements that occurred in October and November 2004 allows building the respective weighted graphs of solid bulk, liquid bulk, container, general cargo, and passenger/vehicles. Main results underline a very strong influence of commodity diversity on the distribution of maritime traffics among ports and links between them. The research also underlines the role of different regional settings in the specialization of traffic flows

A Framework to Formalise the MDE Foundations

International audienceDomain-Specific Language (DSL) are getting more and more popular and are being used in critical systems like aerospace and car industries. Methods for simulating and validating DSL models are now necessary in order to make the new software generation more reliable and less costly. Developing analysis tools for DSL requires the definition of models semantics. In this paper, we propose a framework to give a formal foundation of the Model-Driven Engineering (MDE) approach. We separate the usually common notions of models and modelling languages associating to each of them a different goal. In order to prove the consistency of our proposal we express a subset of EMOF, its static semantics and validate its meta-circularity

Asynchronous Graph Pattern Matching on Multiprocessor Systems

Pattern matching on large graphs is the foundation for a variety of application domains. Strict latency requirements and continuously increasing graph sizes demand the usage of highly parallel in-memory graph processing engines that need to consider non-uniform memory access (NUMA) and concurrency issues to scale up on modern multiprocessor systems. To tackle these aspects, graph partitioning becomes increasingly important. Hence, we present a technique to process graph pattern matching on NUMA systems in this paper. As a scalable pattern matching processing infrastructure, we leverage a data-oriented architecture that preserves data locality and minimizes concurrency-related bottlenecks on NUMA systems. We show in detail, how graph pattern matching can be asynchronously processed on a multiprocessor system.Comment: 14 Pages, Extended version for ADBIS 201