Fair end-to-end window-based congestion control

Mo, Jeonghoon; Walrand, Jean

doi:10.1109/90.879343

Cited by 1,798 publications

(1,547 citation statements)

References 18 publications

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“…The results imply that the prototypical size-based scheduling strategies will certainly not yield optimal delay performance in resource-sharing networks. Instead, proper tuning of the parameters of so-called (weighted) α-fair bandwidth-sharing policies as introduced in [26] might provide a more promising approach for improving the delay performance. As is proved in [9], the usual necessary conditions are in fact sufficient for these policies to achieve stability.…”

Section: Resultsmentioning

confidence: 99%

Stability of size-based scheduling disciplines in resource-sharing networks

Verloop

Borst

Núñez-Queija

2005

Performance Evaluation

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Section: Resultsmentioning

confidence: 99%

Stability of size-based scheduling disciplines in resource-sharing networks

Verloop

Borst

Núñez-Queija

2005

Performance Evaluation

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“…Most of these real-time allocation schemes try to enforce some notion of fairness. Examples include the max-min fair allocation of Bertsekas and Gallager [1], and its extensions to proportional fairness and other related notions in Fayolle et al [11], Kelly [18,19], Kelly et al [21], Low [24], Massoulie and Roberts [27], Mo and Walrand [28], Wen and Arcak [32], and Ye [35] among many others. Typically, the real-time capacity allocation takes the form of a solution to an optimization problem, with the objective being a utility function (of the state and the allocation), and the constraints enforcing the capacity limit on the links.…”

Section: Literature Reviewmentioning

confidence: 99%

Heavy-Traffic Optimality of a Stochastic Network Under Utility-Maximizing Resource Allocation

Yao

2008

Operations Research

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We study a stochastic network that consists of a set of servers processing multiple classes of jobs. Each class of jobs requires a concurrent occupancy of several servers while being processed, and each server is shared among the job classes in a head-of-the-line processorsharing mechanism. The allocation of the service capacities is a real-time control mechanism: in each network state, the control is the solution to an optimization problem that maximizes a general utility function. Whereas this resource control optimizes in a "greedy" fashion, with respect to each state, we establish its asymptotic optimality in terms of (a) deriving the fluid and diffusion limits of the network under this control, and (b) identifying a cost function that is minimized in the diffusion limit, along with a characterization of the so-called fixed point state of the network.

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“…Various service protocols for resource sharing networks have been proposed. The most popular of them are proportional fairness, originating in a work of Kelly (1997), and more general α-fair policies, introduced by Mo and Walrand (2000). The literature on the latter two disciplines is large.…”

Section: Introductionmentioning

confidence: 99%

Edge minimality of EDF resource sharing networks

Kruk

2017

Math Meth Oper Res

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We consider a general real-time network with soft customer deadlines, in which users require service from several shared resources simultaneously. We call the service protocol for such a network edge minimal (locally edge minimal) if it minimizes globally (resp., locally) in time, in a suitable sense, the system resource idleness with respect to customers with lead times not greater than any given threshold value on all the routes of the network. We show that the preemptive Earliest-Deadline-First (EDF) service discipline is edge minimal. Moreover, we characterize the preemptive EDF policy as a protocol making the underlying network locally edge minimal. Our arguments are pathwise, independent on the network topology and requiring very mild assumptions, or even no assumptions, on the model stochastic primitives. Application of our characterization to fluid model analysis for EDF resource sharing networks is also discussed.

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Fair end-to-end window-based congestion control

Cited by 1,798 publications

References 18 publications

Stability of size-based scheduling disciplines in resource-sharing networks

Stability of size-based scheduling disciplines in resource-sharing networks

Heavy-Traffic Optimality of a Stochastic Network Under Utility-Maximizing Resource Allocation

Edge minimality of EDF resource sharing networks

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