49,106 research outputs found
Online Resource Inference in Network Utility Maximization Problems
The amount of transmitted data in computer networks is expected to grow
considerably in the future, putting more and more pressure on the network
infrastructures. In order to guarantee a good service, it then becomes
fundamental to use the network resources efficiently. Network Utility
Maximization (NUM) provides a framework to optimize the rate allocation when
network resources are limited. Unfortunately, in the scenario where the amount
of available resources is not known a priori, classical NUM solving methods do
not offer a viable solution. To overcome this limitation we design an overlay
rate allocation scheme that attempts to infer the actual amount of available
network resources while coordinating the users rate allocation. Due to the
general and complex model assumed for the congestion measurements, a passive
learning of the available resources would not lead to satisfying performance.
The coordination scheme must then perform active learning in order to speed up
the resources estimation and quickly increase the system performance. By
adopting an optimal learning formulation we are able to balance the tradeoff
between an accurate estimation, and an effective resources exploitation in
order to maximize the long term quality of the service delivered to the users
Traveller Behaviour: Decision making in an unpredictable world
This paper discusses the nature and consequences of uncertainty in transport systems. Drawing on work from a number of fields, it addresses travellers’ abilities to predict variable phenomena, their perception of uncertainty, their attitude to risk and the various strategies they might adopt in response to uncertainty. It is argued that despite the increased interest in the representation of uncertainty in transport systems, most models treat uncertainty as a purely statistical issue and ignore the psychological aspects of response to uncertainty. The principle theories and models currently used to predict travellers’ response to uncertainty are presented and number of alternative modelling approaches are outlined. It is argued that the current generation of predictive models do not provide an adequate basis for forecasting response to changes in the degree of uncertainty or for predicting the likely effect of providing additional information. A number of alternative modelling approaches are identified to deal with travellers’ acquisition of information, the definition of their choice set and their choice between the available options. The use of heuristic approaches is recommended as an alternative to more conventional probabilistic methods
Shortest Path versus Multi-Hub Routing in Networks with Uncertain Demand
We study a class of robust network design problems motivated by the need to
scale core networks to meet increasingly dynamic capacity demands. Past work
has focused on designing the network to support all hose matrices (all matrices
not exceeding marginal bounds at the nodes). This model may be too conservative
if additional information on traffic patterns is available. Another extreme is
the fixed demand model, where one designs the network to support peak
point-to-point demands. We introduce a capped hose model to explore a broader
range of traffic matrices which includes the above two as special cases. It is
known that optimal designs for the hose model are always determined by
single-hub routing, and for the fixed- demand model are based on shortest-path
routing. We shed light on the wider space of capped hose matrices in order to
see which traffic models are more shortest path-like as opposed to hub-like. To
address the space in between, we use hierarchical multi-hub routing templates,
a generalization of hub and tree routing. In particular, we show that by adding
peak capacities into the hose model, the single-hub tree-routing template is no
longer cost-effective. This initiates the study of a class of robust network
design (RND) problems restricted to these templates. Our empirical analysis is
based on a heuristic for this new hierarchical RND problem. We also propose
that it is possible to define a routing indicator that accounts for the
strengths of the marginals and peak demands and use this information to choose
the appropriate routing template. We benchmark our approach against other
well-known routing templates, using representative carrier networks and a
variety of different capped hose traffic demands, parameterized by the relative
importance of their marginals as opposed to their point-to-point peak demands
A note on hierarchical hubbing for a generalization of the VPN problem
Robust network design refers to a class of optimization problems that occur
when designing networks to efficiently handle variable demands. The notion of
"hierarchical hubbing" was introduced (in the narrow context of a specific
robust network design question), by Olver and Shepherd [2010]. Hierarchical
hubbing allows for routings with a multiplicity of "hubs" which are connected
to the terminals and to each other in a treelike fashion. Recently, Fr\'echette
et al. [2013] explored this notion much more generally, focusing on its
applicability to an extension of the well-studied hose model that allows for
upper bounds on individual point-to-point demands. In this paper, we consider
hierarchical hubbing in the context of a previously studied (and extremely
natural) generalization of the hose model, and prove that the optimal
hierarchical hubbing solution can be found efficiently. This result is relevant
to a recently proposed generalization of the "VPN Conjecture".Comment: 14 pages, 1 figur
Applications of sensitivity analysis for probit stochastic network equilibrium
Network equilibrium models are widely used by traffic practitioners to aid them in making decisions concerning the operation and management of traffic networks. The common practice is to test a prescribed range of hypothetical changes or policy measures through adjustments to the input data, namely the trip demands, the arc performance (travel time) functions, and policy variables such as tolls or signal timings. Relatively little use is, however, made of the full implicit relationship between model inputs and outputs inherent in these models. By exploiting the representation of such models as an equivalent optimisation problem, classical results on the sensitivity analysis of non-linear programs may be applied, to produce linear relationships between input data perturbations and model outputs. We specifically focus on recent results relating to the probit Stochastic User Equilibrium (PSUE) model, which has the advantage of greater behavioural realism and flexibility relative to the conventional Wardrop user equilibrium and logit SUE models. The paper goes on to explore four applications of these sensitivity expressions in gaining insight into the operation of road traffic networks. These applications are namely: identification of sensitive, ‘critical’ parameters; computation of approximate, re-equilibrated solutions following a change (post-optimisation); robustness analysis of model forecasts to input data errors, in the form of confidence interval estimation; and the solution of problems of the bi-level, optimal network design variety. Finally, numerical experiments applying these methods are reported
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