Showing papers by "Madhav V. Marathe published in 2008"

EpiSimdemics: an efficient algorithm for simulating the spread of infectious disease over large realistic social networks

Abstract: Preventing and controlling outbreaks of infectious diseases such as pandemic influenza is a top public health priority. We describe EpiSimdemics - a scalable parallel algorithm to simulate the spread of contagion in large, realistic social contact networks using individual-based models. EpiSimdemics is an interaction-based simulation of a certain class of stochastic reaction-diffusion processes. Straightforward simulations of such process do not scale well, limiting the use of individual-based models to very small populations. EpiSimdemics is specifically designed to scale to social networks with 100 million individuals. The scaling is obtained by exploiting the semantics of disease evolution and disease propagation in large networks. We evaluate an MPI-based parallel implementation of EpiSimdemics on a mid-sized HPC system, demonstrating that EpiSimdemics scales well. EpiSimdemics has been used in numerous sponsor defined case studies targeted at policy planning and course of action analysis, demonstrating the usefulness of EpiSimdemics in practical situations.

Approximation Algorithms for Computing Capacity of Wireless Networks with SINR Constraints

Abstract: A fundamental problem in wireless networks is to estimate its throughput capacity - given a set of wireless nodes, and a set of connections, what is the maximum rate at which data can be sent on these connections. Most of the research in this direction has focused on either random distributions of points, or has assumed simple graph-based models for wireless interference. In this paper, we study capacity estimation problem using the more general Signal to Interference Plus Noise Ratio (SINR) model for interference, on arbitrary wireless networks. The problem becomes much harder in this setting, because of the non-locality of the SINR model. Recent work by Moscibroda et al. (2006) has shown that the throughput in this model can differ from graph based models significantly. We develop polynomial time algorithms to provably approximate the total throughput in this setting.

Engineering Label-Constrained Shortest-Path Algorithms

Abstract: We consider a generalization of the shortest-path problem: given an alphabet Σ, a graph Gwhose edges are weighted and Σ-labeled, and a regular language L? Σ*, the L-constrained shortest-path problemconsists of finding a shortest path pin Gsuch that the concatenated labels along pform a word of L. This definition allows to model, e. g., many traffic-planning problems. We present extensions of well-known speed-up techniques for the standard shortest-path problem, and conduct an extensive experimental study of their performance with various networks and language constraints. Our results show that depending on the network type, both goal-directed and bidirectional search speed up the search considerably, while combinations of these do not.

An interaction-based approach to computational epidemiology

Abstract: Epidemiology is the study of patterns of health in a population and the factors that contribute to these patterns. Computational Epidemiology is the development and use of computer models to understand the spatio-temporal diffusion of disease through populations. An important factor that greatly influences an outbreak of an infectious disease is the structure of the interaction network across which it spreads. Aggregate or collective computational epidemiology models that have been studied in the literature for over a century, often assume that a population is partitioned into a few subpopulations (e.g. by age) with a regular interaction structure within and between subpopulations. Although useful for obtaining analytical expressions for a number of interesting parameters such as the numbers of sick, infected and recovered individuals in a population, it does not capture the complexity of human interactions that serves as a mechanism for disease transmission. In other words, the aggregate approach does not take the structure of underlying social network into account. Additionally, the number of different subpopulation types considered is small and parameters such as mixing rate and reproductive number are either unknown or hard to observe. Here we describe Simdemics: an interaction-based multiagent approach to support epidemic planning for large urban regions. Simdemics is an example of a disaggregated modeling approach in which interactions between every pair of individuals is represented. It is based on the idea that a better understanding of the characteristics of the social contact network can give better insights into disease dynamics and intervention strategies for epidemic planning. Simdemics details the demographic and geographic distributions of disease and provides decision makers with information about (1) the consequences of a biological attack or natural outbreak, (2) the resulting demand for health services, and (3) the feasibility and effectiveness of response options. A unique feature of Simdemics is the size and scale of urban regions that can be analyzed using it.

Capacity of Asynchronous Random-Access Scheduling in Wireless Networks

Abstract: We study the throughput capacity of wireless networks which employ (asynchronous) random-access scheduling as opposed to deterministic scheduling. The central question we answer is: how should we set the channel-access probability for each link in the network so that the network operates close to its optimal throughput capacity? We design simple and distributed channel-access strategies for random-access networks which are provably competitive with respect to the optimal scheduling strategy, which is deterministic, centralized, and computationally infeasible. We show that the competitiveness of our strategies are nearly the best achievable via random-access scheduling, thus establishing fundamental limits on the performance of random- access. A notable outcome of our work is that random access compares well with deterministic scheduling when link transmission durations differ by small factors, and much worse otherwise. The distinguishing aspects of our work include modeling and rigorous analysis of asynchronous communication, asymmetry in link transmission durations, and hidden terminals under arbitrary link-conflict based wireless interference models.

Epidemiology and Wireless Communication: Tight Analogy or Loose Metaphor?

Abstract: The analogy between viral dynamics in humans and in computers is a detailed and useful one. At first glance, the extension to infectious disease epidemiology on human social networks and communication in wireless networks is also a compelling analogy. Mathematical epidemiology has a long history and seems to offer a biological inspiration for communication network design. In this paper, however, we argue that while epidemiology as a metaphor may hold insights into communication networks, the relationship is not concrete enough to permit us to adapt solutions from one domain to another. Our conclusion is that it is certain new mathematics and methodologies, rather than the results themselves, that are most likely to generalize well to communication systems.

An Interaction Based Composable Architecture for Building Scalable Models of Large Social, Biological, Information and Technical Systems.

Abstract: This article describes our ongoing efforts to develop a global modeling, information & decision support cyberinfrastructure (CI) that will provide scientists and engineers novel ways to study large complex socio-technical systems. It consists of the following components: High-resolution scalable models of complex socio-technical systems Service-oriented architecture and delivery mechanism for facilitating the use of these models by domain experts Distributed coordinating architecture for information fusion, model execution and data processing Scalable data management architecture and system to support model execution and analytics Scalable methods for visual and data analytics to support analysts To guide the initial development of our tools, we are concentrating on agent-based models of inter-dependent societal infrastructures, spanning large urban regions. Examples of such systems include: regional transportation systems; regional electric power markets and grids; the Internet; ad-hoc telecommunication, communication and computing systems; and public health services. Such systems can be viewed as organizations of organizations. Indeed, functioning societal infrastructure systems consist of several interacting public and private organizations working in concert to provide the necessary services to individuals and society. Issues related to privacy of individuals, confidentiality of data, data integrity and security all arise while developing microscopic models for such systems. See [1] [2] [3] for additional discussion (also see Figure 1). Figure 1 Schematic of societal infrastructure systems (adapted from [2]). The need to represent functioning population centers during complex incidents such as natural disasters and human initiated events poses a very difficult scientific and technical challenge that calls for new state-of-the-art technology. The system must be able to handle complex co-evolving networks with over 300 million agents (individuals), each with individual itineraries and movements, millions of activity locations, thousands of activity types, and hundreds of communities, each with local interdependent critical infrastructures. The system must be able to focus attention on demand and must support the needs of decision makers at various levels. The system must also support related functions such as policy analysis, planning, course-of-action analysis, incident management, and training in a variety of domains (e.g., urban evacuation management, epidemiological event management, bio-monitoring, population risk exposure estimation, logistical planning and management of isolated populations, site evacuations, interdependent infrastructure failures). Constructing large socio-technical simulations is challenging and novel, since, unlike physical systems, socio-technical systems are affected not only by physical laws but also by human behavior, regulatory agencies, courts, government agencies and private enterprises. The urban transportation system is a canonical example of such interaction; traffic rules in distant parts of the city can have an important bearing on the traffic congestion in downtown, and seemingly “reasonable” strategies such as adding a new road somewhere might worsen the traffic. The complicated inter-dependencies within and among various socio-technical systems, and the need to develop new tools, are highlighted by the failure of the electric grid in the northeastern U.S in 2003. The massive power outage left people in the dark along a 3,700 mile stretch through portions of Michigan, Ohio, Pennsylvania, New Jersey, New York, Connecticut, Vermont and Canada. Failure of the grid led to cascading effects that slowed down Internet traffic, closed down financial institutions and disrupted the transportation; the New York subway system came to a halt, stranding more than 400,000 passengers in tunnels [4]. The CI we are building was motivated by the considerations to understand the complex inter-dependencies between infrastructures and the society as described above. Over the past 15 years and in conjunction with our collaborators, we have established a program for modeling, simulation and associated decision support tools for understanding large socio-technical systems. The extremely detailed, multi-scale computer simulations allow users to interact among themselves as well as interact with the environment and the networked infrastructure. The simulations are based on our theoretical program in discrete dynamical systems, complex networks, AI and design and analysis of algorithms (see [2] [5] [6] [7] [8]and the references therein). Until 2003, much of our efforts were concentrated on building computational models of individual infrastructures, see [2]. Over the last 7-10 years, significant advances have been made in developing computational techniques and tools that have the potential of transforming how these models are delivered to and used by the end users [9] [10] [11]. This includes, web services, grid computing and methods to process large amounts of data. With the goal of harnessing this technology, since 2005, we have expanded the scope of our effort. In addition to building scalable models, we have also begun the development of an integrated CI for studying such inter-dependent, socio-technical systems. It consists of mechanisms to deliver the access to these models to end users over the web, development of a data management environment to support the analysis and data, and a visual analytics environment to support decision-making and consequence analysis (see [2] [6]). The CI will provide social scientists unprecedented Internet-based access to data and models pertaining to large social organizations. In addition, the associated modeling tools will generate new kinds of synthetic data sets that cannot be created in any other way (e.g., direct measurement). The data generated by these methods will protect the privacy of individuals as well as the confidentiality of data obtained from proprietary datasets. This will enable social scientists to investigate entirely new research questions about functioning societal infrastructures and the individuals interacting with these systems. Everything, from the scope and precision of socio-technical analysis to the concept of collaboration and information integration, will change, as a dispersed framework that supports detailed interdependent interaction of very large numbers of complex individual entities that come into use and evolve. The tools will also allow policy makers, planners, and emergency responders unprecedented opportunities for coordination of and integration with the information for situation assessment and consequence management. This is important for planning and responding in the event of a large-scale disruption of the societal infrastructures.

Adversarial Scheduling in Evolutionary Game Dynamics

Abstract: Consider a system in which players at nodes of an underlying graph G repeatedly play Prisoner's Dilemma against their neighbors The players adapt their strategies based on the past behavior of their opponents by applying the so-called win-stay lose-shift strategy This dynamics has been studied in (Kittock 94), (Dyer et al 2002), (Mossel and Roch, 2006) With random scheduling, starting from any initial configuration with high probability the system reaches the unique fixed point in which all players cooperate This paper investigates the validity of this result under various classes of adversarial schedulers Our results can be sumarized as follows: 1 An adversarial scheduler that can select both participants to the game can preclude the system from reaching the unique fixed point on most graph topologies 2 A nonadaptive scheduler that is only allowed to choose one of the participants is no more powerful than a random scheduler With this restriction even an adaptive scheduler is not significantly more powerful than the random scheduler, provided it is "reasonably fair" The results exemplify the adversarial scheduling approach we propose as a foundational basis for the generative approach to social science (Epstein 2007)

On the Complexity of Minimum Partition of Frequency-Agile Radio Networks

Abstract: New advances in cognitive radio technology, and the recent proposals for opening up the licensed spectrum bands raise new challenges for frequency allocation problems. In this paper, we study the clustering in frequency-agile radios problem (CFRP), which involves partitioning the network into the smallest number of connected clusters, where nodes within a cluster can communicate with a common frequency without creating interference to any primary user. This problem was formulated by Steenstrup (DySPAN '05) to model one-to-many broadcasts and Steenstrup's paper explored the empirical performance of greedy heuristics. In this paper, we focus on the formal computational complexity of this problem, and prove that it is NP-complete. Moreover, we show that it is unlikely that there exists a polynomial-time algorithm for this problem that always produces a solution (for n-vertex graphs) with at most In n times the optimal number of components. We show that several natural greedy heuristics, including the one studied by Steenstrup, can have highly sub-optimal performance in the worst case. For trees, we show that the optimum solution can be computed in polynomial time. There are instances where the number of components required is very large, if we require them to be strictly disjoint. We observe that in practice, allowing even a small amount of overlap among clusters significantly reduces the number of clusters needed. Motivated by this, we study a relaxation of the CFRP problem, that allows components to overlap, and study bicriteria approximations for this version of the problem, by simultaneously bounding the number of components and the average overlap between them. We present efficient algorithms that produce solutions with the cost related to both of these metrics being within a factor of O(log n) of the optimum. We end with simulations results for our algorithms on a wide range of instances.

Power Efficient Throughput Maximization in Multi-Hop Wireless Networks

Abstract: We study the problem of total throughput maximization in arbitrary multi-hop wireless networks, with constraints on the total power usage (denoted by PETM), when nodes have the capability to adaptively choose their power levels, which is the case with software defined radio devices. The underlying interference graph changes when power levels change, making PETM a complex cross-layer optimization problem. We develop a linear programming formulation for this problem, that leads to a constant factor approximation to the total throughput rate, for any given bound on the total power usage. Our result is a rigorously provable worst case approximation guarantee, which holds for any instance. Our formulation is generic and can accommodate different interference models and objective functions. We complement our theoretical analysis with simulations and compute the explicit tradeoffs between fairness, total throughput and power usage.

Adversarial Scheduling Analysis of Game-Theoretic Models of Norm Diffusion

Abstract: In [IMR01] we advocated the investigation of robustness of results in the theory of learning in games under adversarial scheduling models. We provide evidence that such an analysis is feasible and can lead to nontrivial results by investigating, in an adversarial scheduling setting, Peyton Young's model of diffusion of norms [You98]. In particular, our main result incorporates contagioninto Peyton Young's model.

Adversarial Scheduling Analysis of Game Theoretic Models of Norm Diffusion

Abstract: In (Istrate, Marathe, Ravi SODA 2001) we advocated the investigation of robustness of results in the theory of learning in games under adversarial scheduling models. We provide evidence that such an analysis is feasible and can lead to nontrivial results by investigating, in an adversarial scheduling setting, Peyton Young's model of diffusion of norms. In particular, our main result incorporates into Peyton Young's model.

Adversarial scheduling analysis of Game-Theoretic Models of Norm Diffusion

Abstract: In (Istrate et al. SODA 2001) we advocated the investigation of robustness of results in the theory of learning in games under adversarial scheduling models. We provide evidence that such an analysis is feasible and can lead to nontrivial results by investigating, in an adversarial scheduling setting, Peyton Young's model of diffusion of norms . In particular, our main result incorporates contagion into Peyton Young's model.