Network Flows

We undertake network efficiency analysis within an input-output model that allows us to assess potential technical efficiency gains by comparing technologies corresponding to different economies. Input-output tables represent a network where different sectoral nodes use primary inputs (endowments) to produce intermediate input and outputs (according to sectoral technologies), and satisfy final demand (preferences). Within the input-output framework it is possible to optimize primary inputs allocation, intermediate production and final demand production by way of non-parametric data envelopment analysis (DEA) techniques. DEA allows us to model the different subtechnologies corresponding to alternative production processes, to assess efficient resource allocation among them, and to determine potential output gains if inefficiencies were dealt with. The proposed model optimizes the underlying multi-stage technologies that the input-output system comprises identifying the best practice economies. The model is applied to a set of OECD countries.

In this paper, we consider a multicommodity flow problem where for each pair of vertices, (u,v), we are required to sendf half-units of commodity (uv) from u to v and f half-units of commodity (vu) from v to u without violating capacity constraints. Our main result is an algorithm for performing th9 task provided that the capacity of each cut exceeds the demand across the cut by a b(log n) factor. The condition on cuts is required in the worst case, and is trivially within a i(log n) factor of optimal for any flow problem.

: This thesis considers the combining of a ship overhaul project network with a shipyard project network to minimize possible scheduling conflicts during the ship overhaul process. The combined project is called the ship overhaul project network. A heuristic multi-pass scheduling algorithm is developed to combine the ship's and shipyard's project networks. The algorithm is tested with two sets of data. The work spaces on shipboard are considered as a common resource to both project networks, and the ship's complement is an uncommon but limited resource. Since the real-world problem is larger than the context of this thesis, some simplifying assumptions were made. The most important of these are the reliable communication link between ship and shipyard prior to and/or during the overhaul process, and sufficient knowledge about the shipyard project network as early as possible after overhaul initiation. (Author)

In studying traffic congestions at toll plazas, a basic model for traffic systems is introduced, which will hopefully contribute towards a longer term solution with the means for explaining and predicting congestions. It is targeted that this can then be followed by a more specific operations research approach, based on a simulation and queuing theory model, with a study on different arrival patterns, generating queue models, and constructing a Markov chain from the queuing systems, which would then provide a better insight towards a more sustainable solution.

— In studying traffic congestions at toll plazas, a basic model for traffic systems is introduced, which will hopefully contribute towards a longer term solution with the means for explaining and predicting congestions. It is targeted that this can then be followed by a more specific operations research approach, based on a simulation and queuing theory model, with a study on different arrival patterns, generating queue models, and constructing a Markov chain from the queuing systems, which would then provide a better insight towards a more sustainable solution.

We present a wide range of problems concerning minimum cost network flows, and give an overview of the classic linear single-commodity Minimum Cost Network Flow Problem (MCNFP) and some other closely related problems, either tractable or intractable. We also discuss state-of-the-art algorithmic approaches and recent advances in the solution methods for the MCNFP. Finally, optimization software packages for the MCNFP are presented.

This technical report documents the theoretical, computational, and practical aspects of the one-dimensional Navier-Stokes finite element flow model. The document is particularly useful to those who are interested in implementing, validating and utilizing this relatively-simple and widely-used model.

In any form of traffic, whether it is physical or digital, the main problem that sparks a lot of interest for researchers is congestion. This paper discusses a study of congestion around toll plazas. A newly formulated model called Basic Traffic Unit (BTU) is introduced from previous research. BTU is a representation of a basic network connected to form a larger network. A simulation study is conducted and some queuing theories are applied to calculate the measure of performance. Two sets of data from different modes of toll payment are collected. The same two types of payments are simulated and the measure of performance is recorded. The two types of payments from the collected real data are used to calculate the measure of performance using the formula developed by the BTU. This study demonstrates that the results from simulated data can be used to compare with the results from the real-time data using the formula developed by a static model of BTU to predict possible congestions. Comparing results from both methods validates the claim that BTU is useful for predicting congestions.

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This paper presents an experimental study of constraint propagation algorithms for preemptive scheduling. We propose generalizations of non-preemptive constraint propagation techniques (based on timetables, on disjunctive constraints, and on edge-finding) to preemptive and "mixed" problems, i.e., problems in which some activities can be interrupted and some cannot. Another approach relies on incremental flow-based techniques. We theoretically compare these approaches and

The classical maximum flow problem sometimes occurs in settings in which the arc capacities are not fixed but are functions of a single parameter, and the goal is to find the value of the parameter such that the corresponding maximum flow or minimum cut satisfies some side condition. Finding the desired parameter value requires solving a sequence of related maximum flow problems. In this paper it is shown that the recent maximum flow algorithm of Goldberg and Tarjan can be extended to solve an important class of such parametric maximum flow problems, at the cost of only a constant factor in its worst-case time bound. Faster algorithms for a variety of combinatorial optimization problems follow from the result.

We propose an approach to address data uncertainty for discrete optimization and network flow problems that allows controlling the degree of conservatism of the solution, and is computationally tractable both practically and theoretically. In particular, when both the cost coefficients and the data in the constraints of an integer programming problem are subject to uncertainty, we propose a robust integer programming problem of moderately larger size that allows controlling the degree of conservatism of the solution in terms of probabilistic bounds on constraint violation. When only the cost coefficients are subject to uncertainty and the problem is a 0 − 1 discrete optimization problem on n variables, then we solve the robust counterpart by solving at most n + 1 instances of the original problem. Thus, the robust counterpart of a polynomially solvable 0−1 discrete optimization problem remains polynomially solvable. In particular, robust matching, spanning tree, shortest path, matroid intersection, etc. are polynomially solvable. We also show that the robust counterpart of an N P -hard α-approximable 0 − 1 discrete optimization problem, remains α-approximable. Finally, we propose an algorithm for robust network flows that solves the robust counterpart by solving a polynomial number of nominal minimum cost flow problems in a modified network.

Network traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a thorough understanding of OD flows is essential for modeling network traffic, and for addressing a wide variety of problems including traffic engineering, traffic matrix estimation, capacity planning, forecasting and anomaly detection. However, to date, OD flows have not been closely studied, and there is very little known about their properties.

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In this paper a survey is presented of some of the recent results in stochastic open shop, flow shop and job shop scheduling. The distributions of the processing times of the jobs are known in advance, but the actual processing times are not known in advance. The jobs may have due dates. Optimal preemptive and nonpreemptive policies are determined for the minimization of various objective functions, such as the expected makespan, the expected flow time and the expected number of late jobs. The effect of various degrees of dependence between the processing times of any given job on the various machines is investigated. Under given conditions bounds are obtained for the expected makespan in the different models.

Supporting creative collaboration in the classroom is considered an important objective by current education research, as growing evidence suggests that this practice is linked with the quality of learning and the development of problem solving skills. Drawing on recent advances in group creativity research, social cognition and network science, we propose a theoretical framework for studying creative collaboration that integrates the concepts of group flow and social presence. Our key hypothesis is that in order for a group to enter the creative state of flow, members must be able to develop a "we-intention", in which the actions of the individuals and those of the collective are merged (social presence). According to our model, this is a staged process, beginning with the co-construction of a shared frame within the group and culminating with the creation of a novel artefact or concept. A further assumption is that the emergence of such optimal group state is associated with structural changes in the dynamics of group interaction, which can be effectively investigated using social network analysis techniques.

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In the last decade, the release of network flows has gained significant popularity among researchers and networking communities. Indeed, network flows are a fundamental tool for modeling the network behavior, identifying security attacks, and validating research results. Unfortunately, due to the sensitive nature of network flows, security and privacy concerns discourage the publication of such datasets. On the one hand, existing techniques proposed to sanitize network flows do not provide any formal guarantees. On the other hand, microdata anonymization techniques are not directly applicable to network flows. In this paper, we propose a novel obfuscation technique for network flows that provides formal guarantees under realistic assumptions about the adversary's knowledge. Our work is supported by extensive experiments with a large set of real network flows collected at an important Italian Tier II Autonomous System, hosting sensitive government and corporate sites. Experimental results show that our obfuscation technique preserves the utility of network flows for network traffic analysis.

Flash Crowds are the events that occur due to sudden increase in legitimate traffic towards a single web server due to popularity of that web server or a famous event posted on its web pages. Distributed DoS (DDoS) attacks are the attacking events conducted by an attacker to overwhelm the web server with huge amount of traffic due to which that web server cannot serve legitimate users. Distinguishing flash crowds from DDoS attacks is very important because the response of the defense systems should be different for these two types of traffic. Initially abnormal traffic condition on a web server is detected when there is a huge amount of traffic which is very larger than the normal traffic volume. Our proposed system makes use of flow strength as a metric for assigning suspicion mark to flows in order to group the flows as probable Flash Crowd flows or probable DDoS attack flows. The technique used here is very intuitive, functional and can be proved to be working in a simulation environment and is perhaps applicable for real time usage. We found some cyber crimes that are “Beyond DDoS Attacks” and we suggested a remedy for “Beyond DDoS Attacks

The minimum cost multiple-source unsplittable flow problem is studied in this paper. A simple necessary condition to get a solution is proposed. It deals with capacities and demands and can be seen as a generalization of the well-known semi-metric condition for continuous multicommdity flows. A cutting plane algorithm is derived using a superadditive approach. The inequalities considered here are valid for single knapsack constraints. They are based on nondecreasing superadditive functions and can be used to strengthen the relaxation of any integer program with knapsack constraints. Some numerical experiments confirm the efficiency of the inequalities introduced in the paper.

—The current energy infrastructure heavily depends on fossil energy, which will be mostly depleted beyond 21st century. Another built-in disadvantage of fossil energy is the pollutant and green house gas emission. It is time to reform the environment-degrading energy infrastructure into a sustainable and resilient energy infrastructure such that it is more environmental friendly. Compared with fossil energy, it is expensive to transport renewable energy for a long distance. Another problem of renewable energy is fluctuation and it is not so stable as fossil energy. To solve the two bottleneck energy investment planning problems (transmission and fluctuation) of renewable energy development, we propose a long-term investment planning model that can help analysts, investors and policy makers find out how to take full use of current and emerging technologies to support the development of renewable energy so that our energy infrastructure can be refomed to be cleaner in a long-term periond, e.g. 40 years. In this model, we propose and implement a parallel planning method for power systems. In this method, a large region that needs to be planned is partitioned into multiple subregions. Each subregion is modeled as two optimization models. One is an hour-level model with the goal to minimize the power price volatility caused by imbalance of power demand and supply and the CO2 emission at hour level. Another is a year-level model with the goal to minimize the investment cost of transmission, operation, and fossil/clean power capapcity expansion at year-level. The year-level model also needs to satisfy the RPS [1] (Renewable Portfolio Standard, which has been approved by 27 states and D.C.) requirements because it is a year-level policy. We use an energy storage system to store surplus clean power e.g. wind power and this helps solve the fluctuation problem of wind energy. The stored energy is allowed to be traded among neighbouring subregions. All models are linear or mixed integer linear programming models and need to satisfy the constraints about fossil/clean power capacity expansion and available clean energy. We use Midwest area and wind energy as an example and implement the parallel modeling method in a cluster system , which supports parallel computing. According to our best knowledge, this is the first parallel long-term energy investment planning model for exploring the relationships between public policy (RPS), renewable energy and fossil energy. It can be used to solve large-scale planning problems on supercomputers.

In case a CSP is over-constrained, it is natural to allow some constraints, called soft constraints, to be violated. We propose a generic method to soften global constraints that can be represented by a flow in a graph. Such constraints are softened by adding violation arcs to the graph and then computing a minimum-weight flow in the extended graph to measure the violation. We present efficient propagation algorithms, based on different violation measures, achieving domain consistency for the alldifferent constraint, the global cardinality constraint, the regular constraint and the same constraint.

We propose a variational model for one of the most important problems in traffic networks, namely, the network equilibrium flow that is, traditionally in the context of operations research, characterized by minimum cost flow. This model has the peculiarity of being formulated by means of a suitable variational inequality (VI) and its solution is called "equilibrium". This model becomes a minimum cost model when the cost function is separable or, more general, when the Jacobian of the cost operator is symmetric; in such cases a functional representing the total network utility exists. In fact in these cases we can write the first order optimality conditions which turn out to be a VI. In the other situations (i.e., when global utility functional does not exist), which occur much more often in the real problems, we can study the network by looking for equilibrium solutions instead of minimum points. The Lagrangean approach to the study of the VI allows us to introduce dual variables, associated to the constraints of the feasible set, which may receive interesting interpretations in terms of potentials associated to the arcs and the nodes of the network. This interpretation is an extension and generalization of the classic Bellman conditions. Finally, we deepen the analysis of the networks having capacity constraints.

A methodology to solve a large and complex problem is proposed. OR methods as Multilevel Planning, Network Techniques, Multicriteria Decision Aid (MCDA) and Mixed Integer Linear Programming (MILP) were used to structure the methodology. One of the principal objectives of this work is reduce the complexity of a large problem and solve it to find the better solution for the decision makers. The methodology is applied to a petrochemical industry of Mexico, which is structured in a network, having different alternative routes of production; each of them having also a different technology. This network begins from the crude oil as raw material in order to produce the basic petrochemicals until finals ones. It has been considered that basic petrochemicals will be produced through a set of Refineries with a high production of basic petrochemicals yield, searching the best configuration among it, according with the needs of basic petrochemicals coming from the final's and its best route selected.

Production planning for large-scale production systems requiring the allocation of numerous resources is considered. It is demonstrated how the dynamic activity analysis developed by Shephard leads to linear programming solutions of production planning problems. Three types of planning problems are formulated: maximization of output levels for a given time horizon; minimization of production duration for given output histories; and minimization of production costs for given output histories.

Let G=(V,E) be a supply graph and H=(V,F) a demand graph defined on the same set of vertices. An assignment of capacities to the edges of G and demands to the edges of H is said to satisfy the cut condition if for any cut in the graph, the total demand crossing the cut is no more than the total

The "roof dual" of a QUBO (Quadratic Unconstrained Binary Optimization) problem has been introduced in [P.L. Hammer, P. Hansen, B. Simeone, Roof duality, complementation and persistency in quadratic 0-1 optimization, Mathematical Programming 28 (1984) 121-155]; it provides a bound to the optimum value, along with a polynomial test of the sharpness of this bound, and (due to a "persistency" result) it also determines the values of some of the variables at the optimum. In this paper we provide a graph-theoretic approach to provide bounds, which includes as a special case the roof dual bound, and show that these bounds can be computed in O(n 3) time by using network flow techniques. We also obtain a decomposition theorem for quadratic pseudo-Boolean functions, improving the persistency result of [P.L. Hammer, P. Hansen, B. Simeone, Roof duality, complementation and persistency in quadratic 0-1 optimization, Mathematical Programming 28 (1984) 121-155]. Finally, we show that the proposed bounds (including roof duality) can be applied in an iterated way to obtain significantly better bounds. Computational experiments on problems up to thousands of variables are presented.

This paper proposes a method for improving the performance of Web servers servicing static HTTP requests. The idea is to give preference to those requests which are quick, or have small remaining processing requirements, in accordancewith the SRPT (Shortest-Remaining-Processing-Time) scheduling policy. The implementation is at the kernel level and involves controlling the order in which socket buffers are drained into the network. Experiments use the Linux operating system and the Apache web server. All experiments are also repeated using the FlashWeb server. Experiments are run under both trace-based workloads and those generated by a Web workload generator. Results indicate that SRPT-based scheduling of connections yields significant reductions in mean response time, mean slowdown, and variance in response time at the Web server. Most significantly, and counter to intuition, the large requests are only negligibly penalized (or not at all penalized) as a result of SRPT-based schedu...

Researchers in the field of electoral systems have recently turned their attention to network flow techniques, with the intention to resolve certain practically relevant problems of contemporary electoral systems. Here we review some of this work, with a focus on biproportional apportionment methods and on the give-up problem. In the biproportional apportionment problem, the whole electoral region is subdivided into several electoral districts. The input data consists of a matrix of the vote counts a party receives in a district. The task is to convert the vote matrix into a (integer) seat matrix, maintaining proportionality "as much as possible". Moreover each district must be allocated its pre-specified number of seats, and each party must receive the number of seats it is entitled to on the basis of the aggregate, national vote counts. The give-up problem arises in the current electoral law for the Italian Parliament. For each region each party submits a blocked, ordered lists of candidates. Candidates may run on more than one list (that is, in more than one region), and many of them do in order to advertise their national standing. Candidates winning a seat in more than one region must give-up all of them but one. The give-up problem finds a schedule of give-ups, for all lists of a party, that results in a globally best, in some sense, choice of deputies for that party.

Continuous quantitative assessment is critical for security, correctness, performance, and scalability in distributed information sharing environments. Current approaches to quantitative assessment suffer from a number of shortcomings including limited coverage, tight integration with functional code, and off-line evaluation, which limits their use for continuous assessment. In this article, we present Metrinome, a framework that enables remote monitoring and controlled experimentation. Metrinome provides (1) increased coverage over observables by virtue of ingesting fine-grained application logs, (2) reduced integration cost due to loose coupling with the monitored system and its ability to hook into existing logging infrastructures, and (3) real-time visualization of key metrics as well as export of collected metric sets for persistence and reporting.

We present a novel methodology for identifying internal network performance characteristics based on end-to-end multicast measurements. The methodology, solidly grounded on statistical estimation theory, can be used to characterize the internal loss and delay behavior of a network. Measurements on the MBone have been used to validate the approach in the case of losses. Extensive simulation experiments provide further validation of the approach, not only for losses, but also for delays. We also describe our strategy for deploying the methodology on the Internet. This includes the continued development of the National Internet Measurement Infrastructure (NIMI) to support RTP-based end-to-end multicast measurements and the development of software tools for analyzing the traces. Once complete, this combined software/hardware infrastructure will provide a service for understanding and forecasting the performance of the Internet. ¦

This technical report documents the theoretical, computational, and practical aspects of the one-dimensional Navier-Stokes finite element flow model. The document is particularly useful to those who are interested in implementing, validating and utilizing this relatively-simple and widely-used model.