8,610 research outputs found
The Robust Network Loading Problem under Hose Demand Uncertainty: Formulation, Polyhedral Analysis, and Computations
Cataloged from PDF version of article.We consider the network loading problem (NLP) under a polyhedral uncertainty description of traffic
demands. After giving a compact multicommodity flow formulation of the problem, we state a decomposition
property obtained from projecting out the flow variables. This property considerably simplifies the
resulting polyhedral analysis and computations by doing away with metric inequalities. Then we focus on a
specific choice of the uncertainty description, called the āhose model,ā which specifies aggregate traffic upper
bounds for selected endpoints of the network. We study the polyhedral aspects of the NLP under hose demand
uncertainty and use the results as the basis of an efficient branch-and-cut algorithm. The results of extensive
computational experiments on well-known network design instances are reported
The robust network loading problem under hose demand uncertainty: Formulation, polyhedral analysis, and computations
We consider the network loading problem (NLP) under a polyhedral uncertainty description of traffic demands. After giving a compact multicommodity flow formulation of the problem, we state a decomposition property obtained from projecting out the flow variables. This property considerably simplifies the resulting polyhedral analysis and computations by doing away with metric inequalities. Then we focus on a specific choice of the uncertainty description, called the "hose model," which specifies aggregate traffic upper bounds for selected endpoints of the network. We study the polyhedral aspects of the NLP under hose demand uncertainty and use the results as the basis of an efficient branch-and-cut algorithm. The results of extensive computational experiments on well-known network design instances are reported. Ā© 2011 INFORMS
A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
Robust network design under polyhedral traffic uncertainty
Ankara : The Department of Industrial Engineering and The Institute of Engineering and Science of Bilkent Univ., 2007.Thesis (Ph.D.) -- Bilkent University, 2007.Includes bibliographical references leaves 160-166.In this thesis, we study the design of networks robust to changes in demand
estimates. We consider the case where the set of feasible demands is defined by
an arbitrary polyhedron. Our motivation is to determine link capacity or routing
configurations, which remain feasible for any realization in the corresponding
demand polyhedron. We consider three well-known problems under polyhedral
demand uncertainty all of which are posed as semi-infinite mixed integer programming
problems. We develop explicit, compact formulations for all three problems
as well as alternative formulations and exact solution methods.
The first problem arises in the Virtual Private Network (VPN) design field.
We present compact linear mixed-integer programming formulations for the problem
with the classical hose traffic model and for a new, less conservative, robust
variant relying on accessible traffic statistics. Although we can solve these formulations
for medium-to-large instances in reasonable times using off-the-shelf MIP
solvers, we develop a combined branch-and-price and cutting plane algorithm to
handle larger instances. We also provide an extensive discussion of our numerical
results.
Next, we study the Open Shortest Path First (OSPF) routing enhanced with
traffic engineering tools under general demand uncertainty with the motivation to
discuss if OSPF could be made comparable to the general unconstrained routing
(MPLS) when it is provided with a less restrictive operating environment. To
the best of our knowledge, these two routing mechanisms are compared for the
first time under such a general setting. We provide compact formulations for
both routing types and show that MPLS routing for polyhedral demands can
be computed in polynomial time. Moreover, we present a specialized branchand-price
algorithm strengthened with the inclusion of cuts as an exact solution tool. Subsequently, we compare the new and more flexible OSPF routing with
MPLS as well as the traditional OSPF on several network instances. We observe
that the management tools we use in OSPF make it significantly better than the
generic OSPF. Moreover, we show that OSPF performance can get closer to that
of MPLS in some cases.
Finally, we consider the Network Loading Problem (NLP) under a polyhedral
uncertainty description of traffic demands. After giving a compact multicommodity
formulation of the problem, we prove an unexpected decomposition
property obtained from projecting out the flow variables, considerably simplifying
the resulting polyhedral analysis and computations by doing away with metric inequalities,
an attendant feature of most successful algorithms on NLP. Under the
hose model of feasible demands, we study the polyhedral aspects of NLP, used as
the basis of an efficient branch-and-cut algorithm supported by a simple heuristic
for generating upper bounds. We provide the results of extensive computational
experiments on well-known network design instances.Altın, AyÅegĆ¼lPh.D
PDDL2.1: An extension of PDDL for expressing temporal planning domains
In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community
On the Flow-level Dynamics of a Packet-switched Network
The packet is the fundamental unit of transportation in modern communication
networks such as the Internet. Physical layer scheduling decisions are made at
the level of packets, and packet-level models with exogenous arrival processes
have long been employed to study network performance, as well as design
scheduling policies that more efficiently utilize network resources. On the
other hand, a user of the network is more concerned with end-to-end bandwidth,
which is allocated through congestion control policies such as TCP.
Utility-based flow-level models have played an important role in understanding
congestion control protocols. In summary, these two classes of models have
provided separate insights for flow-level and packet-level dynamics of a
network
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