Optimization based rate control for multipath sessions

Kar, Koushik; Sarkar, Saswati; Tassiulas, Leandros

doi:10.1016/s1388-3437(01)80171-2

Cited by 47 publications

(31 citation statements)

References 15 publications

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“…When each source receives feedback congestion information, i.e., the path price, which is the sum of the link prices along its path, it adopts the following source algorithm to update the source rate: (14) where (15) is the path price of source , , and is the inverse of over the range . According to the definition of utility function, it is clear that given in (14) is decreasing over the path price .…”

Section: A a New Distributed Flow Control Algorithmmentioning

confidence: 99%

“…In between, source attains a utility factor of . Even though the source algorithms (14) and (8) both manage the transmission rate as a decreasing function of the path price, they are significantly different in their mathematical descriptions. We will show shortly that these two source algorithms may produce quite different resource allocation results.…”

Section: A a New Distributed Flow Control Algorithmmentioning

confidence: 99%

“…Remark 1: For each source , we can also use the following Lagrangian first-order algorithm: (23) to update source rate instead of using the dual algorithm (14). According to our earlier results shown in [29], this gentle and smooth adaptation gives a better performance than that of the dual algorithm (14), especially when the path price cannot be fed back perfectly to its source due to network implementation limitations, i.e., a random exponential marking (REM) [23] strategy must then be deployed.…”

Section: B Optimization and Convergencementioning

confidence: 99%

“…In [2], the network flow control problem is formulated, for the first time, as an optimization problem and explicit rate flow control algorithm is derived by solving the optimization problem via a link congestion pricing policy. Following this pioneering work, an extensive study of network flow control based on optimization method and game theory has been carried out by network researchers, e.g., [3]- [13] for single-path networks, [14], [15] for multiple-path networks and [16], [17] for multirate multicast networks.…”

mentioning

confidence: 99%

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Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness

Wang

Palaniswami

Low

2006

IEEE/ACM Trans. Networking

110

124

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Abstract-This paper is concerned with flow control and resource allocation problems in computer networks in which real-time applications may have hard quality of service (QoS) requirements. Recent optimal flow control approaches are unable to deal with these problems since QoS utility functions generally do not satisfy the strict concavity condition in real-time applications. For elastic traffic, we show that bandwidth allocations using the existing optimal flow control strategy can be quite unfair. If we consider different QoS requirements among network users, it may be undesirable to allocate bandwidth simply according to the traditional max-min fairness or proportional fairness. Instead, a network should have the ability to allocate bandwidth resources to various users, addressing their real utility requirements. For these reasons, this paper proposes a new distributed flow control algorithm for multiservice networks, where the application's utility is only assumed to be continuously increasing over the available bandwidth. In this, we show that the algorithm converges, and that at convergence, the utility achieved by each application is well balanced in a proportionally (or max-min) fair manner.

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Section: A a New Distributed Flow Control Algorithmmentioning

confidence: 99%

Section: A a New Distributed Flow Control Algorithmmentioning

confidence: 99%

Section: B Optimization and Convergencementioning

confidence: 99%

mentioning

confidence: 99%

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Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness

Wang

Palaniswami

Low

2006

IEEE/ACM Trans. Networking

110

124

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show abstract

“…Even though there are tremendous advances in solving the optimal flow control problem in data networks, most of these works focus at the single-path or unicast transmissions case, with some extensions to the multiple-path problem [17][18][19]. In general, the solutions for the unicast problem can be directly extended to the conventional unirate multicast problem, but designing the optimal flow control strategy in multirate multicast networks remains an open and interesting problem.…”

Section: Introductionmentioning

confidence: 99%

Necessary and sufficient conditions for optimal flow control in multirate multicast networks

2003

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The authors consider the optimal flow control problem in multirate multicast networks where all receivers of the same multicast group can receive service at different rates with different QoS. The objective is to achieve the fairness transmission rates that maximise the total receiver utility under the capacity constraint of links. They first propose necessary and sufficient conditions for the optimal solution to the problem, and then derive a new optimal flow control strategy using the Lagrangian multiplier method. Like the unicast case, the basic algorithm consists of a link algorithm to update the link price, and a receiver algorithm to adapt the transmission rate according to the link prices along its path. In particular if some groups contain only one receiver and become unicast, the algorithm will degrade to their previously proposed unicast algorithm.

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Network Optimization Theory

Glisic

Lorenzo

2009

Advanced Wireless Networks

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In the past, network protocols in layered architectures have been obtained on an ad hoc basis, and many of the recent crosslayer designs are also conducted through piecemeal approaches. It is only recently that network protocol stacks have instead been analyzed and designed as distributed solutions to some global optimization problems in the form of generalized network utility maximization (NUM), providing insight on what they optimize and on the structures of the network protocol stacks. This chapter will present material required for understanding of layering as optimization decomposition where each layer corresponds to a decomposed subproblem, and the interfaces among layers are quantified as functions of the optimization variables coordinating the subproblems.Decomposition theory provides the analytical tool for the design of modularized and distributed control of networks. This chapter will present results of horizontal decomposition into distributed computation and vertical decomposition into functional modules such as congestion control, routing, scheduling, random access, power control and channel coding. Key results from many recent works are summarized and open issues discussed. Through case studies, it is illustrated how layering as optimization decomposition provides a common framework for modularization, a way to deal with complex, networked interactions. The material presents a top-down approach to design protocol stacks and a mathematical theory of network architectures.Convex optimization has become a computational tool of central importance in engineering, thanks to its ability to solve very large, practical engineering problems reliably and efficiently. Many communication problems can either be cast as or be converted into convex optimization problems, which greatly facilitate their analytic and numerical solutions. Furthermore, powerful numerical algorithms exist to solve the optimal solution of convex problems efficiently.For the basics in the area of convex optimization, the basics of convexity, Lagrange duality, distributed subgradient methods and other solution methods for convex optimization, the reader is referred to References [1] to [8].Advanced Wireless Networks: Cognitive, Cooperative and Opportunistic 4G Technology Second Edition Savo Glisic and Beatriz Lorenzo

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Optimization based rate control for multipath sessions

Cited by 47 publications

References 15 publications

Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness

Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness

Necessary and sufficient conditions for optimal flow control in multirate multicast networks

Network Optimization Theory

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