135,343 research outputs found
Fast network configuration in Software Defined Networking
Software Defined Networking (SDN) provides a framework to dynamically adjust and re-program the data plane with the use of flow rules. The realization of highly adaptive SDNs with the ability to respond to changing demands or recover after a network failure in a short period of time, hinges on efficient updates of flow rules. We model the time to deploy a set of flow rules by the update time at the bottleneck switch, and formulate the problem of selecting paths to minimize the deployment time under feasibility constraints as a mixed integer linear program (MILP). To reduce the computation time of determining flow rules, we propose efficient heuristics designed to approximate the minimum-deployment-time solution by relaxing the MILP or selecting the paths sequentially. Through extensive simulations we show that our algorithms outperform current, shortest path based solutions by reducing the total network configuration time up to 55% while having similar packet loss, in the considered scenarios. We also demonstrate that in a networked environment with a certain fraction of failed links, our algorithms are able to reduce the average time to reestablish disrupted flows by 40%
A Linear Network Code Construction for General Integer Connections Based on the Constraint Satisfaction Problem
The problem of finding network codes for general connections is inherently
difficult in capacity constrained networks. Resource minimization for general
connections with network coding is further complicated. Existing methods for
identifying solutions mainly rely on highly restricted classes of network
codes, and are almost all centralized. In this paper, we introduce linear
network mixing coefficients for code constructions of general connections that
generalize random linear network coding (RLNC) for multicast connections. For
such code constructions, we pose the problem of cost minimization for the
subgraph involved in the coding solution and relate this minimization to a
path-based Constraint Satisfaction Problem (CSP) and an edge-based CSP. While
CSPs are NP-complete in general, we present a path-based probabilistic
distributed algorithm and an edge-based probabilistic distributed algorithm
with almost sure convergence in finite time by applying Communication Free
Learning (CFL). Our approach allows fairly general coding across flows,
guarantees no greater cost than routing, and shows a possible distributed
implementation. Numerical results illustrate the performance improvement of our
approach over existing methods.Comment: submitted to TON (conference version published at IEEE GLOBECOM 2015
Optimization-Based Linear Network Coding for General Connections of Continuous Flows
For general connections, the problem of finding network codes and optimizing
resources for those codes is intrinsically difficult and little is known about
its complexity. Most of the existing solutions rely on very restricted classes
of network codes in terms of the number of flows allowed to be coded together,
and are not entirely distributed. In this paper, we consider a new method for
constructing linear network codes for general connections of continuous flows
to minimize the total cost of edge use based on mixing. We first formulate the
minimumcost network coding design problem. To solve the optimization problem,
we propose two equivalent alternative formulations with discrete mixing and
continuous mixing, respectively, and develop distributed algorithms to solve
them. Our approach allows fairly general coding across flows and guarantees no
greater cost than any solution without network coding.Comment: 1 fig, technical report of ICC 201
Train-scheduling optimization model for railway networks with multiplatform stations
This paper focuses on optimizing the schedule of trains on railway networks composed of busy complex stations. A mathematical formulation of this problem is provided as a Mixed Integer Linear Program (MILP). However, the creation of an optimal new timetable is an NP-hard problem; therefore, the MILP can be solved for easy cases, computation time being impractical for more complex examples. In these cases, a heuristic approach is provided that makes use of genetic algorithms to find a good solution jointly with heuristic techniques to generate an initial population. The algorithm was applied to a number of problem instances producing feasible, though not optimal, solutions in several seconds on a laptop, and compared to other proposals. Some improvements are suggested to obtain better results and further improve computation time. Rail transport is recognized as a sustainable and energy-efficient means of transport. Moreover, each freight train can take a large number of trucks off the roads, making them safer. Studies in this field can help to make railways more attractive to travelers by reducing operative cost, and increasing the number of services and their punctuality. To improve the transit system and service, it is necessary to build optimal train scheduling. There is an interest from the industry in automating the scheduling process. Fast computerized train scheduling, moreover, can be used to explore the effects of alternative draft timetables, operating policies, station layouts, and random delays or failures.Postprint (published version
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