Showing papers by "Madhav V. Marathe published in 2009"

EpiFast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems

Abstract: Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease propagation in a contact network. We implement it using a master-slave computation model which allows scalability on distributed memory systems.EpiFast runs extremely fast for realistic simulations that involve: (i) large populations consisting of millions of individuals and their heterogeneous details, (ii) dynamic interactions between the disease propagation, the individual behaviors, and the exogenous interventions, as well as (iii) large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution. We find that EpiFast runs several magnitude faster than another comparable simulation tool while delivering similar results.EpiFast has been tested on commodity clusters as well as SGI shared memory machines. For a fixed experiment, if given more computing resources, it scales automatically and runs faster. Finally, EpiFast has been used as the major simulation engine in real studies with rather sophisticated settings to evaluate various dynamic interventions and to provide decision support for public health policy makers.

Generation and analysis of large synthetic social contact networks

Abstract: We describe "first principles" based methods for developing synthetic urban and national scale social contact networks. Unlike simple random graph techniques, these methods use real world data sources and combine them with behavioral and social theories to synthesize networks. We develop a synthetic population for the United States modeling every individual in the population including household structure, demographics and a 24-hour activity sequence. The process involves collecting and manipulating public and proprietary data sets integrated into a common architecture for data exchange and then using these data sets to generate new relations. A social contact network is derived from the synthetic population based on physical co-location of interacting persons. We use graph measures to compare and contrast the structural characteristics of the social networks that span different urban regions. We then simulate diffusion processes on these networks and analyze similarities and differences in the structure of the networks.

A unified approach to scheduling on unrelated parallel machines

Abstract: We develop a single rounding algorithm for scheduling on unrelated parallel machines; this algorithm works well with the known linear programming-, quadratic programming-, and convex programming-relaxations for scheduling to minimize completion time, makespan, and other well-studied objective functions. This algorithm leads to the following applications for the general setting of unrelated parallel machines: (i) a bicriteria algorithm for a schedule whose weighted completion-time and makespan simultaneously exhibit the current-best individual approximations for these criteria; (ii) better-than-two approximation guarantees for scheduling to minimize the Lp norm of the vector of machine-loads, for all 1

Modeling interaction between individuals, social networks and public policy to support public health epidemiology

Abstract: Human behavior, social networks, and civil infrastructure are closely intertwined. Understanding their co-evolution is critical for designing public policies. Human behaviors and day-to-day activities of individuals create dense social interactions that provide a perfect fabric for fast disease propagation. Conversely, people's behavior in response to public policies and their perception of the crisis can dramatically alter normally stable social interactions. Effective planning and response strategies must take these complicated interactions into account. The basic problem can be modeled as a coupled co-evolving graph dynamical system and can also be viewed as partially observable Markov decision process. As a way to overcome the computational hurdles, we describe an High Performance Computing oriented computer simulation to study this class of problems. Our method provides a novel way to study the co-evolution of human behavior and disease dynamics in very large, realistic social networks with over 100 Million nodes and 6 Billion edges.

Distributed Strategies for Channel Allocation and Scheduling in Software-Defined Radio Networks

Abstract: Equipping wireless nodes with multiple radios can significantly increase the capacity of wireless networks, by making these radios simultaneously transmit over multiple non-overlapping channels. However, due to the limited number of radios and available orthogonal channels, designing efficient channel assignment and scheduling algorithms in such networks is a major challenge. In this paper, we present provably-good distributed algorithms for simultaneous channel allocation of individual links and packet-scheduling, in software-defined radio (SDR) wireless networks. Our distributed algorithms are very simple to implement, and do not require any coordination even among neighboring nodes. A novel access hash function or random oracle methodology is one of the key drivers of our results. With this access hash function, each radio can know the transmitters' decisions for links in its interference set for each time slot without introducing any extra communication overhead between them. Further, by utilizing the inductive-scheduling technique, each radio can also backoff appropriately to avoid collisions. Extensive simulations demonstrate that our bounds are valid in practice.

Scheduling on Unrelated Machines under Tree-Like Precedence Constraints

Abstract: We present polylogarithmic approximations for the R|prec|C max and R|prec|∑j w j C j problems, when the precedence constraints are “treelike”—i.e., when the undirected graph underlying the precedences is a forest. These are the first non-trivial generalizations of the job shop scheduling problem to scheduling with precedence constraints that are not just chains. These are also the first non-trivial results for the weighted completion time objective on unrelated machines with precedence constraints of any kind. We obtain improved bounds for the weighted completion time and flow time for the case of chains with restricted assignment—this generalizes the job shop problem to these objective functions. We use the same lower bound of “congestion + dilation”, as in other job shop scheduling approaches (e.g. Shmoys, Stein and Wein, SIAM J. Comput. 23, 617–632, 1994). The first step in our algorithm for the R|prec|C max problem with treelike precedences involves using the algorithm of Lenstra, Shmoys and Tardos to obtain a processor assignment with the congestion + dilation value within a constant factor of the optimal. We then show how to generalize the random-delays technique of Leighton, Maggs and Rao to the case of trees. For the special case of chains, we show a dependent rounding technique which leads to a bicriteria approximation algorithm for minimizing the flow time, a notoriously hard objective function.

EpiNet: a simulation framework to study the spread of malware in wireless networks

Abstract: We describe a modeling framework to study the spread of malware over realistic wireless networks. We develop (i) methods for generating synthetic, yet realistic wireless networks using activity-based models of urban population mobility, and (ii) an interaction-based simulation framework to study the dynamics of worm propagation over wireless networks. We use the prototype framework to study how Bluetooth worms spread over realistic wireless networks. This required developing an abstract model of the Bluetooth worm and its within-host behavior. As an illustration of the applicability of our framework, and the utility of activity-based models, we compare the dynamics of Bluetooth worm epidemics over realistic wireless networks and networks generated using random waypoint mobility models. We show that realistic wireless networks exhibit very different structural properties. Importantly, these differences have significant qualitative effect on spatial as well as temporal dynamics of worm propagation. Our results also demonstrate the importance of early detection to control the epidemic.

Interactions among human behavior, social networks, and societal infrastructures: A Case Study in Computational Epidemiology

Abstract: Human behavior, social networks, and the civil infrastructures are closely intertwined. Understanding their co-evolution is critical for designing public policies and decision support for disaster planning. For example, human behaviors and day to day activities of individuals create dense social interactions that are characteristic of modern urban societies. These dense social networks provide a perfect fabric for fast, uncontrolled disease propagation. Conversely, people’s behavior in response to public policies and their perception of how the crisis is unfolding as a result of disease outbreak can dramatically alter the normally stable social interactions. Effective planning and response strategies must take these complicated interactions into account. In this chapter, we describe a computer simulation based approach to study these issues using public health and computational epidemiology as an illustrative example. We also formulate game-theoretic and stochastic optimization problems that capture many of the problems that we study empirically.

Computational Epidemiology in a Connected World

Abstract: New technologies help epidemiologists model the socioeconomic context of disease outbreaks. Epidemiologists and computer scientists are developing new data-driven, high-performance-computing-powered inference engines to model the socioeconomic context and strategies necessary to counter disease outbreaks.

Bi-Criteria Approximation Algorithms for Power-Efficient and Low-Interference Topology Control in Unreliable Ad Hoc Networks

Abstract: Topology control in ad hoc networks is a multi- criteria optimization problem involving (contradictory) objectives of connectivity, interference, and power minimization. Addition- ally, nodes can be unreliable, which adds another dimension to an already challenging problem. In this paper, we study topology control problems in ad hoc networks under node failures for arbitrary node distributions. We consider a simple and natural stochastic failure model, in which each node can fail independently with a given probability. The Topology Control Problem Under Stochastic Failures is to choose a power level for each node and a subset of edges such that the residual graph (i.e., the graph formed by the nodes which have not failed) is connected and can be scheduled efficiently, with high probability. We develop provably efficient bi-criteria approximation algorithms for this problem that simultaneously minimize power, reduce interference, and ensure that the surviving graph is connected with high probability. Our algorithms can be implemented efficiently in a distributed manner.

Approximation Algorithms for Degree-Constrained Minimum-Cost Network-Design Problems

Abstract: We study network-design problems with two different design objectives: the total cost of the edges and nodes in the network and the maximum degree of any node in the network. A prototypical example is the degree-constrained node-weighted Steiner tree problem: We are given an undirected graph G(V,E), with a non-negative integral function d that specifies an upper bound d(v) on the degree of each vertex v∈V in the Steiner tree to be constructed, nonnegative costs on the nodes, and a subset of k nodes called terminals. The goal is to construct a Steiner tree T containing all the terminals such that the degree of any node v in T is at most the specified upper bound d(v) and the total cost of the nodes in T is minimum. Our main result is a bicriteria approximation algorithm whose output is approximate in terms of both the degree and cost criteria—the degree of any node v∈V in the output Steiner tree is O(d(v)log k) and the cost of the tree is O(log k) times that of a minimum-cost Steiner tree that obeys the degree bound d(v) for each node v. Our result extends to the more general problem of constructing one-connected networks such as generalized Steiner forests. We also consider the special case in which the edge costs obey the triangle inequality and present simple approximation algorithms with better performance guarantees.

Policy informatics for co-evolving socio-technical networks

Abstract: We summarize our ongoing integrated program to represent and reason about very large co-evolving social, technological, information and organization (STIO) networks. The program comprises of four basic elements: (i) a mathematical and computational theory of co-evolving STIO networks, (ii) methods for integrating diverse data sources to generate synthetic representations of STIO networks, (iii) high performance computing oriented models for simulating the dynamical phenomenon of interest on these networks, and (iv) an integrated high performance oriented pervasive cyber-environment that provides analysts and decision makers a web-based environment that provides seamless access to the models and synthetic networks for policy planning and response. We will illustrate some of these ideas by discussing the development of information-support environments for supporting the study of epidemics in social as well as wireless networks.