75 research outputs found
Cost-sharing in generalised selfish routing
© Springer International Publishing AG 2017. We study a generalisation of atomic selfish routing games where each player may control multiple flows which she routes seek-ing to minimise their aggregate cost. Such games emerge in various set-tings, such as traffic routing in road networks by competing ride-sharing applications or packet routing in communication networks by competing service providers who seek to optimise the quality of service of their cus-tomers. We study the existence of pure Nash equilibria in the induced games and we exhibit a separation from the single-commodity per player model by proving that the Shapley value is the only cost-sharing method that guarantees it. We also prove that the price of anarchy and price of stability is no larger than in the single-commodity model for general cost-sharing methods and general classes of convex cost functions. We close by giving results on the existence of pure Nash equilibria of a splittable variant of our model
Maximizing Routing Throughput with Applications to Delay Tolerant Networks
abstract: Many applications require efficient data routing and dissemination in Delay Tolerant Networks (DTNs) in order to maximize the throughput of data in the network, such as providing healthcare to remote communities, and spreading related information in Mobile Social Networks (MSNs). In this thesis, the feasibility of using boats in the Amazon Delta Riverine region as data mule nodes is investigated and a robust data routing algorithm based on a fountain code approach is designed to ensure fast and timely data delivery considering unpredictable boat delays, break-downs, and high transmission failures. Then, the scenario of providing healthcare in Amazon Delta Region is extended to a general All-or-Nothing (Splittable) Multicommodity Flow (ANF) problem and a polynomial time constant approximation algorithm is designed for the maximum throughput routing problem based on a randomized rounding scheme with applications to DTNs. In an MSN, message content is closely related to users’ preferences, and can be used to significantly impact the performance of data dissemination. An interest- and content-based algorithm is developed where the contents of the messages, along with the network structural information are taken into consideration when making message relay decisions in order to maximize data throughput in an MSN. Extensive experiments show the effectiveness of the above proposed data dissemination algorithm by comparing it with state-of-the-art techniques.Dissertation/ThesisDoctoral Dissertation Computer Science 201
Improving Real-Time Data Dissemination Performance by Multi Path Data Scheduling in Data Grids
The performance of data grids for data intensive, real-time applications is highly dependent on the data dissemination algorithm employed in the system. Motivated by this fact, this study first formally defines the real-time splittable data dissemination problem (RTS/DDP) where data transfer requests can be routed over multiple paths to maximize the number of data transfers to be completed before their deadlines. Since RTS/DDP is proved to be NP-hard, four different heuristic algorithms, namely kSP/ESMP, kSP/BSMP, kDP/ESMP, and kDP/BSMP are proposed. The performance of these heuristic algorithms is analyzed through an extensive set of data grid system simulation scenarios. The simulation results reveal that a performance increase up to 8 % as compared to a very competitive single path data dissemination algorithm is possible
Achieving target equilibria in network routing games without knowing the latency functions
The analysis of network routing games typically assumes precise, detailed information about the latency functions. Such information may, however, be unavailable or difficult to obtain. Moreover, one is often primarily interested in enforcing a desired target flow as an equilibrium. We ask whether one can achieve target flows as equilibria without knowing the underlying latency functions. We give a crisp positive answer to this question. We show that one can efficiently compute edge tolls that induce a given target multicommodity flow in a nonatomic routing game using a polynomial number of queries to an oracle that takes tolls as input and outputs the resulting equilibrium flow. This result is obtained via a novel application of the ellipsoid method, and extends to various other settings. We obtain improved query-complexity bounds for series-parallel networks, and single-commodity routing games with linear latency functions. Our techniques provide new insights into network routing games
Dynamic unsplittable flows with path-change penalties: new formulations and solution schemes for large instances
In this work, we consider the dynamic unsplittable flow problem. This
variation of the unsplittable flow problem has received little attention so
far. The unsplittable flow problem is an NP-hard extension of the
multi-commodity flow problem where each commodity sends its flow on only one
path. In its dynamic version, this problem features several time steps and a
penalty is paid when a commodity changes its path from one time step to the
next. We present several mixed-integer linear programming formulations for this
problem and compare the strength of their linear relaxation. These formulations
are embedded in several solvers which are extensively compared on small to
large instances. One of these formulations must be solved through a column
generation process whose pricing problem is more difficult than those used in
classical flow problems. We present limitations of the pricing schemes proposed
in earlier works and describe two new schemes with a better worst-case
complexity. Overall, this work lays a strong algorithmic baseline for the
resolution of the dynamic unsplittable flow problem, proposes original
formulations, and discusses the compared advantages of each, thus hopefully
contributing a step towards a better understanding of this problem for both OR
researchers and practical applications
On Routing Optimization in Networks with Embedded Computational Services
Modern communication networks are increasingly equipped with in-network
computational capabilities and services. Routing in such networks is
significantly more complicated than the traditional routing. A legitimate route
for a flow not only needs to have enough communication and computation
resources, but also has to conform to various application-specific routing
constraints. This paper presents a comprehensive study on routing optimization
problems in networks with embedded computational services. We develop a set of
routing optimization models and derive low-complexity heuristic routing
algorithms for diverse computation scenarios. For dynamic demands, we also
develop an online routing algorithm with performance guarantees. Through
evaluations over emerging applications on real topologies, we demonstrate that
our models can be flexibly customized to meet the diverse routing requirements
of different computation applications. Our proposed heuristic algorithms
significantly outperform baseline algorithms and can achieve close-to-optimal
performance in various scenarios.Comment: 16 figure
Randomized rounding algorithms for large scale unsplittable flow problems
Unsplittable flow problems cover a wide range of telecommunication and
transportation problems and their efficient resolution is key to a number of
applications. In this work, we study algorithms that can scale up to large
graphs and important numbers of commodities. We present and analyze in detail a
heuristic based on the linear relaxation of the problem and randomized
rounding. We provide empirical evidence that this approach is competitive with
state-of-the-art resolution methods either by its scaling performance or by the
quality of its solutions. We provide a variation of the heuristic which has the
same approximation factor as the state-of-the-art approximation algorithm. We
also derive a tighter analysis for the approximation factor of both the
variation and the state-of-the-art algorithm. We introduce a new objective
function for the unsplittable flow problem and discuss its differences with the
classical congestion objective function. Finally, we discuss the gap in
practical performance and theoretical guarantees between all the aforementioned
algorithms
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