6,341 research outputs found
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
Speeding up Martins' algorithm for multiple objective shortest path problems
The latest transportation systems require the best routes in a large network with respect to multiple objectives simultaneously to be calculated in a very short time. The label setting algorithm of Martins efficiently finds this set of Pareto optimal paths, but sometimes tends to be slow, especially for large networks such as transportation networks. In this article we investigate a number of speedup measures, resulting in new algorithms. It is shown that the calculation time to find the Pareto optimal set can be reduced considerably. Moreover, it is mathematically proven that these algorithms still produce the Pareto optimal set of paths
SCOR: Software-defined Constrained Optimal Routing Platform for SDN
A Software-defined Constrained Optimal Routing (SCOR) platform is introduced
as a Northbound interface in SDN architecture. It is based on constraint
programming techniques and is implemented in MiniZinc modelling language. Using
constraint programming techniques in this Northbound interface has created an
efficient tool for implementing complex Quality of Service routing applications
in a few lines of code. The code includes only the problem statement and the
solution is found by a general solver program. A routing framework is
introduced based on SDN's architecture model which uses SCOR as its Northbound
interface and an upper layer of applications implemented in SCOR. Performance
of a few implemented routing applications are evaluated in different network
topologies, network sizes and various number of concurrent flows.Comment: 19 pages, 11 figures, 11 algorithms, 3 table
Information Centric Networking in the IoT: Experiments with NDN in the Wild
This paper explores the feasibility, advantages, and challenges of an
ICN-based approach in the Internet of Things. We report on the first NDN
experiments in a life-size IoT deployment, spread over tens of rooms on several
floors of a building. Based on the insights gained with these experiments, the
paper analyses the shortcomings of CCN applied to IoT. Several interoperable
CCN enhancements are then proposed and evaluated. We significantly decreased
control traffic (i.e., interest messages) and leverage data path and caching to
match IoT requirements in terms of energy and bandwidth constraints. Our
optimizations increase content availability in case of IoT nodes with
intermittent activity. This paper also provides the first experimental
comparison of CCN with the common IoT standards 6LoWPAN/RPL/UDP.Comment: 10 pages, 10 figures and tables, ACM ICN-2014 conferenc
Estimating Dynamic Traffic Matrices by using Viable Routing Changes
Abstract: In this paper we propose a new approach for dealing with the ill-posed nature of traffic matrix estimation. We present three solution enhancers: an algorithm for deliberately changing link weights to obtain additional information that can make the underlying linear system full rank; a cyclo-stationary model to capture both long-term and short-term traffic variability, and a method for estimating the variance of origin-destination (OD) flows. We show how these three elements can be combined into a comprehensive traffic matrix estimation procedure that dramatically reduces the errors compared to existing methods. We demonstrate that our variance estimates can be used to identify the elephant OD flows, and we thus propose a variant of our algorithm that addresses the problem of estimating only the heavy flows in a traffic matrix. One of our key findings is that by focusing only on heavy flows, we can simplify the measurement and estimation procedure so as to render it more practical. Although there is a tradeoff between practicality and accuracy, we find that increasing the rank is so helpful that we can nevertheless keep the average errors consistently below the 10% carrier target error rate. We validate the effectiveness of our methodology and the intuition behind it using commercial traffic matrix data from Sprint's Tier-1 backbon
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