21 research outputs found

    Systems for characterizing Internet routing

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    2018 Spring.Includes bibliographical references.Today the Internet plays a critical role in our lives; we rely on it for communication, business, and more recently, smart home operations. Users expect high performance and availability of the Internet. To meet such high demands, all Internet components including routing must operate at peak efficiency. However, events that hamper the routing system over the Internet are very common, causing millions of dollars of financial loss, traffic exposed to attacks, or even loss of national connectivity. Moreover, there is sparse real-time detection and reporting of such events for the public. A key challenge in addressing such issues is lack of methodology to study, evaluate and characterize Internet connectivity. While many networks operating autonomously have made the Internet robust, the complexity in understanding how users interconnect, interact and retrieve content has also increased. Characterizing how data is routed, measuring dependency on external networks, and fast outage detection has become very necessary using public measurement infrastructures and data sources. From a regulatory standpoint, there is an immediate need for systems to detect and report routing events where a content provider's routing policies may run afoul of state policies. In this dissertation, we design, build and evaluate systems that leverage existing infrastructure and report routing events in near-real time. In particular, we focus on geographic routing anomalies i.e., detours, routing failure i.e., outages, and measuring structural changes in routing policies

    Structure-oriented prediction in complex networks

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    Complex systems are extremely hard to predict due to its highly nonlinear interactions and rich emergent properties. Thanks to the rapid development of network science, our understanding of the structure of real complex systems and the dynamics on them has been remarkably deepened, which meanwhile largely stimulates the growth of effective prediction approaches on these systems. In this article, we aim to review different network-related prediction problems, summarize and classify relevant prediction methods, analyze their advantages and disadvantages, and point out the forefront as well as critical challenges of the field

    Modeling social resilience: Questions, answers, open problems

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    Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, non-equilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) \emph{delimitation}, i.e., narrowing down the target systems, (ii) \emph{conceptualization}, .e., identifying how to approach social organizations, (iii) formal \emph{representation} using a combination of agent-based and network models, (iv) \emph{operationalization}, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the \emph{robustness} of social organizations and their \emph{adaptivity}, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation

    Routing on the Channel Dependency Graph:: A New Approach to Deadlock-Free, Destination-Based, High-Performance Routing for Lossless Interconnection Networks

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    In the pursuit for ever-increasing compute power, and with Moore's law slowly coming to an end, high-performance computing started to scale-out to larger systems. Alongside the increasing system size, the interconnection network is growing to accommodate and connect tens of thousands of compute nodes. These networks have a large influence on total cost, application performance, energy consumption, and overall system efficiency of the supercomputer. Unfortunately, state-of-the-art routing algorithms, which define the packet paths through the network, do not utilize this important resource efficiently. Topology-aware routing algorithms become increasingly inapplicable, due to irregular topologies, which either are irregular by design, or most often a result of hardware failures. Exchanging faulty network components potentially requires whole system downtime further increasing the cost of the failure. This management approach becomes more and more impractical due to the scale of today's networks and the accompanying steady decrease of the mean time between failures. Alternative methods of operating and maintaining these high-performance interconnects, both in terms of hardware- and software-management, are necessary to mitigate negative effects experienced by scientific applications executed on the supercomputer. However, existing topology-agnostic routing algorithms either suffer from poor load balancing or are not bounded in the number of virtual channels needed to resolve deadlocks in the routing tables. Using the fail-in-place strategy, a well-established method for storage systems to repair only critical component failures, is a feasible solution for current and future HPC interconnects as well as other large-scale installations such as data center networks. Although, an appropriate combination of topology and routing algorithm is required to minimize the throughput degradation for the entire system. This thesis contributes a network simulation toolchain to facilitate the process of finding a suitable combination, either during system design or while it is in operation. On top of this foundation, a key contribution is a novel scheduling-aware routing, which reduces fault-induced throughput degradation while improving overall network utilization. The scheduling-aware routing performs frequent property preserving routing updates to optimize the path balancing for simultaneously running batch jobs. The increased deployment of lossless interconnection networks, in conjunction with fail-in-place modes of operation and topology-agnostic, scheduling-aware routing algorithms, necessitates new solutions to solve the routing-deadlock problem. Therefore, this thesis further advances the state-of-the-art by introducing a novel concept of routing on the channel dependency graph, which allows the design of an universally applicable destination-based routing capable of optimizing the path balancing without exceeding a given number of virtual channels, which are a common hardware limitation. This disruptive innovation enables implicit deadlock-avoidance during path calculation, instead of solving both problems separately as all previous solutions

    Robustness of hierarchical spatial critical infrastructure networks

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    PhD ThesisThe economic state and wellbeing of a nation is dependent upon the critical infrastructure networks that deliver resources, goods and services. However, these are increasingly exposed to a number of hazards, both natural and man-made, which threaten to disrupt their ability to function. It is essential that in order to develop long-term strategic plans of infrastructure provision we are able to understand their current robustness to such hazards. The robustness of critical infrastructure networks has typically been investigated from a topological perspective as a means of simplifying the complexities associated with their analysis. Such work has led to many studies suggesting critical infrastructures exhibit a topological structure, from random to exponential degree distributions. However, often such analysis ignores the explicit spatial characteristics of the node and edge assets. Furthermore, the very nature of topological analysis means that flows/movements that take place over such networks cannot be considered. This work addresses these weaknesses by extending traditional topological analysis to consider emergent properties critical infrastructure networks exhibit when considering higher-order connectivity and flows. An analysis of a suite of synthetic networks with a spectrum of topologies alongside real infrastructure spatial networks, in terms of their basic topology and high-order connectivity, shows that a number of critical infrastructure networks seem to be better characterised as hierarchical networks. Subsequent failure modelling reveals that such hierarchical networks responded in a dramatically different manner to perturbations; complete failure occurring approximately 19 and 34 percent sooner for random and targeted failures compared to random networks. Such poor robustness is further exacerbated when flow simulation modelling over the resulting hierarchical networks is undertaken, revealing particular sensitivity to cascading failures from spatial hazards. In light of these results, it is suggested that it is essential to improve the robustness of critical infrastructure networks that exhibit a hierarchical spatial organisation.School of Civil Engineering and Geosciences, Newcastle University

    Protecting infrastructure networks from disinformation

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    Massive amount of information shared on online platforms makes the verification of contents time-consuming. Concern arises when the misleading or false information, called "disinformation", is exposed to many online platform users who have potential to react on it. The spread of disinformation can cause malicious consequences such as damage to critical infrastructure networks such as electric power, gas, and water distribution networks. Imagine a fake electricity discount, shared by disinformation campaigns, is exposed to many users on Twitter encouraging them to shift their electricity usage to a specific peak hour. If the population of users who engage with the fake discount exceeds a threshold, a blackout can happen due to the overconsumption of electricity. Thus, users access and exposure to accurate information on time can reinforce the infrastructures which are backbone of well-being for societies and economic growth. In this dissertation, we propose solutions to protect infrastructure networks from disinformation campaigns. The solutions include: (i) an integrated epidemiological-optimization (EPO) model involving a mixed integer linear programming model (MIP) and SIR (Susceptible, Infected, Recovered) model to protect physical infrastructure networks by counter disinformation (accurate information) spread in information networks, (ii) a disinformation interdiction model to influence physical infrastructure commodity consumers with accurate information given the topology of social network, (ii) a robust mixed integer linear programming model to propose solutions superior to the original EPO model under uncertain spread of disinformation scenarios. We illustrate our proposed models with two different case studies: (i) a sub-network of the western US interconnection power grid located in Los Angeles County in California, and (ii) the New York City subway system

    Network resilience

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    Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.Comment: Review chapter

    The Game Situation:An object-based game analysis framework

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    ACCESSIBILITY BASED EVALUATION OF COASTAL RURAL COMMUNITIES’ VULNERABILITY TO COASTAL FLOODING AND THEIR ADAPTATION OPTIONS

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    Global climate change and sea-level rise will cause significant risks to coastal communities. To make inclusive and cost-effective adaptation planning decisions, we need to understand who may be impacted and when. Currently, planning literature generally focuses on housing impacts; when will a house be inundated, and what adaptation strategies are useful to keep a house habitable? Housing, though, is only one of many types of infrastructures people need to reside in an area. Reliable roads are another. This dissertation conducts an analysis of parcel-level impacts of SLR on local residents’ ability to reach key amenities such as emergency services, grocery stores, and schools. Furthermore, it strategically evaluates where road protection should be implemented so that access is maintained in an equitable manner. Next, I use the accessibility analysis to identify the important roads for gathering high-resolution flood data to improve the accuracy of the analysis. I use Dorchester County, Maryland, U.S., as a case study. It is an extremely low-lying rural county and is expected to shrink in half by the end of the century due to SLR. The results from the case study indicate that some parcels are not expected to be inundated by SLR but are expected to experience accessibility impacts. Road protection appears to be a temporary strategy that can buy time for long-term adaptation strategies such as relocation. However, the protection strategies should be cautiously selected based on decision-makers priorities. The insight obtained by this dissertation highlights that when policy and decision-makers are deciding among adaptation strategies, they need to reach some level of consensus about assumptions for which a possible future is planned, and also the trade-off between increasing accessibility levels and balancing the distribution of accessibility among different demographic groups
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