On single-path network routing subject to max-min fair flow allocation

Amaldi, E.; Coniglio, Stefano; Gianoli, Luca G.; Ileri, Can Umut

doi:10.1016/j.endm.2013.05.136

Cited by 15 publications

(16 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, given a nominal optimization problem, a solution to its version where we look for an MMF allocation can be found by solving a sequence of problem which are easily derived from the original one [19]. Unlike in the above mentioned work, in [4], [6] we were the first to propose to consider the MMF allocation of elastic demands as constraint in a more general traffic engineering problem aiming at maximizing a utility function selected by the network operator (e.g. the maximization of the total network throughput).…”

Section: Related Workmentioning

confidence: 96%

“…Along the line of our recent work on general traffic engineering (with no energy consumption issues) [4], [6], we consider MMF allocation as a problem constraint rather than as an optimization objective. Our approach is based on a bi-level optimization problem where, at the upper level, a routing path is assigned to each demand to maximize a utility function (traffic engineering operated by the ISP), and, at the lower one, the rate of each flow is adjusted according to the MMF paradigm (resource allocation made by the transport protocol).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy-aware traffic engineering with elastic demands and MMF bandwidth allocation

Amaldi

Capone

Coniglio

et al. 2013

2013 IEEE 18th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

Self Cite

View full text Add to dashboard Cite

In recent years, there has been a remarkable growth of the Internet energy consumption, which is expected to persist in the future at an even higher pace. At the same time the network access capacity of individual subscribers is rapidly reaching values high enough to move the traffic bottleneck from the access network to the core network in most scenarios. This will soon make the elastic nature of traffic an important aspect of network resource management and will require a redesign of the energy-aware traffic engineering techniques so far based on inelastic traffic demands. We propose a novel optimization approach to select a routing path for each elastic traffic demand and decide which routers and links to put to sleep so as to maximize a network utility measure depending on the traffic demand rates, while satisfying a constraint on the total energy consumption. Bandwidth is allocated to each elastic demand according to the Max-Min Fairness (MMF) paradigm, which approximates the resource allocation of the transport layer

show abstract

Section: Related Workmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Energy-aware traffic engineering with elastic demands and MMF bandwidth allocation

Amaldi

Capone

Coniglio

et al. 2013

2013 IEEE 18th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Single-path optimization problems remain -hard also when fairness is implemented as a constraint rather than a criterion. Amaldi et al [150] showed that the Max-Throughput Single-Path Network Routing subject to MMF flow allocation is -hard even with equal (unit) capacities for all links. Nilsson [149] has also shown than nonconvexity introduced by modular flows (granular) causes that even splittable traffic allocation problems become -hard.…”

Section: Complexity Issuesmentioning

confidence: 99%

Fair Optimization and Networks: A Survey

Ogryczak

Luss

Pióro

et al. 2014

Journal of Applied Mathematics

View full text Add to dashboard Cite

Optimization models related to designing and operating complex systems are mainly focused on some efficiency metrics such as response time, queue length, throughput, and cost. However, in systems which serve many entities there is also a need for respecting fairness: each system entity ought to be provided with an adequate share of the system’s services. Still, due to system operations-dependant constraints, fair treatment of the entities does not directly imply that each of them is assigned equal amount of the services. That leads to concepts of fair optimization expressed by the equitable models that represent inequality averse optimization rather than strict inequality minimization; a particular widely applied example of that concept is the so-called lexicographic maximin optimization (max-min fairness). The fair optimization methodology delivers a variety of techniques to generate fair and efficient solutions. This paper reviews fair optimization models and methods applied to systems that are based on some kind of network of connections and dependencies, especially, fair optimization methods for the location problems and for the resource allocation problems in communication networks.

show abstract

“…In symmetric games even the worst SE is still a 4-approximation. For n = 2, we can tighten the bound on the SPoA to the lower bound of the SPoS of 3 2 .…”

Section: Efficiency Of Equilibriamentioning

confidence: 99%

“…Theorem 28. In single-commodity networks with 3 players, there exist constant allocation rate functions and a PNE that is a 3 2 -approximation to the MCAP. The allocation rate functions and the PNE can be computed in polynomial time.…”

Section: Changing the Allocation Rate Functionsmentioning

confidence: 99%

Routing games with progressive filling

Harks

Hoefer

Schewior

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

View full text Add to dashboard Cite

Max-min fairness (MMF) is a widely known approach to a fair allocation of bandwidth to each of the users in a network. This allocation can be computed by uniformly raising the bandwidths of all users without violating capacity constraints. We consider an extension of these allocations by raising the bandwidth with arbitrary and not necessarily uniform time-depending velocities (allocation rates). These allocations are used in a game-theoretic context for routing choices, which we formalize in progressive filling games (PFGs).We present a variety of results for equilibria in PFGs. We show that these games possess pure Nash and strong equilibria. While computation in general is NP-hard, there are polynomial-time algorithms for prominent classes of Max-Min-Fair Games (MMFG), including the case when all users have the same source-destination pair. We characterize prices of anarchy and stability for pure Nash and strong equilibria in PFGs and MMFGs when players have different or the same source-destination pairs. In addition, we show that when a designer can adjust allocation rates, it is possible to design games with optimal strong equilibria. Some initial results on polynomial-time algorithms in this direction are also derived.

show abstract

On single-path network routing subject to max-min fair flow allocation

Cited by 15 publications

References 5 publications

Energy-aware traffic engineering with elastic demands and MMF bandwidth allocation

Energy-aware traffic engineering with elastic demands and MMF bandwidth allocation

Fair Optimization and Networks: A Survey

Routing games with progressive filling

Contact Info

Product

Resources

About