Fair and efficient dynamic bandwidth allocation for multi-application networks

Salles, Ronaldo Moreira; Barria, Javier A.

doi:10.1016/j.comnet.2004.12.008

Cited by 18 publications

(7 citation statements)

References 37 publications

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“…A generalized scheduling approach for both single-wavelength and WDM PONs, referred to as the K-out-of-N technique, was presented and analyzed in [23]; with this technique, efficiency is improved by having the OLT grant the top K requests out of the N ONUs for transmission within a round, where K is a tunable parameter. Finally, several DBA variants that use quality of service (QoS) and fairness criteria have been proposed in [24][25][26][27][28]; these schemes aim to support differentiated services and applications with heterogeneous requirements while making efficient use of the network resources. For an in-depth discussion of DBA algorithms for EPONs, please refer to [9].…”

Section: A Related Workmentioning

confidence: 99%

Multipoint Control Protocol With Look-Ahead for Wavelength Division Multiplexed Ethernet Passive Optical Network

Liu

Rouskas²,

et al. 2014

J. Opt. Commun. Netw.

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Abstract-We present a simple yet effective enhancement to the operation of the Ethernet passive optical network (EPON) multipoint control protocol (MPCP) for wavelength division multiplexing (WDM) EPON. The enhancement, inspired by our earlier work in a related but different context, allows the optical line terminal (OLT) to perform look-ahead scheduling on each of the upstream channels. The look-ahead operation is fully compatible with the existing standard and may be implemented via software updates to the OLT without affecting the operation of optical network units in EPON. The look-ahead enhanced MPCP achieves significant performance gains across a wide range of traffic loads and opens up new opportunities for the design of sophisticated dynamic bandwidth algorithms to support advanced quality of service capabilities.

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Section: A Related Workmentioning

confidence: 99%

Multipoint Control Protocol With Look-Ahead for Wavelength Division Multiplexed Ethernet Passive Optical Network

Liu

Rouskas²,

et al. 2014

J. Opt. Commun. Netw.

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“…Any telecommunications business will need to assess their income as part of their operations viability and hence we investigate this aspect here. Other targets, which have been suggested in the research community are: i) Welfare maximisation: max u i (where u i is the utility of user i), and ii) Fairness: max(min i u i ) [25]. We note that the target of welfare maximisation could be achieved within the framework presented in this work, where instead of price we would consider the utility of the user.…”

Section: Qualitymentioning

confidence: 99%

“…Our proposal can rely on a WFQ algorithm (see e.g. [25]), where the weights for packets of each class depend on the number of active flows for the class and the allocated bandwidth per flow. Potential users of the network with specific requirements arrive according to a Poisson process and if the QoS and the price for a class are acceptable for them then they request admission for this specific class.…”

Section: Single-link Service and Demand Model For Dynamically Chmentioning

confidence: 99%

Dynamic pricing and resource allocation using revenue management for multiservice networks

Zachariadis

Barria

2008

IEEE Trans. Netw. Serv. Manage.

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Abstract-In this paper we develop a novel single-link multiple-classes-of-service framework where offered prices and QoS are allowed to be actively modified by the provider, depending on the demand and the congestion of the system. We obtain a solution to the problem using dynamic programming. The obtained solution is then extended to a network environment using a decomposition approach. The decomposition approach makes our solution scalable, since single-link solutions are used and minimal amount of information is explicitly exchanged. Assessments carried out for small networks show that the obtained income is improved between 2%-20% when compared to a static approach and to approaches where only price or only quality are allowed to be adaptive.

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“…For example, in [22] the proportional fairness principle was used to assign rates fairly to different contending elastic data traffic demands. For more on fairness and optimization the reader is referred to [23], [24]. With this choice of objective function in mind, the socket controllers (agents) set the recharging rate of connected EVs by solving the following optimization problems.…”

Section: A Problem Formulation Notationmentioning

confidence: 99%

Distributed Recharging Rate Control for Energy Demand Management of Electric Vehicles

Hamid

Barria

2013

IEEE Trans. Power Syst.

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Abstract-In this paper we propose a distributed recharging rate control algorithm which combines the objectives of regulating frequency and improving the utilization of electric generators. An incentive policy κ is created that encourages Electric Vehicles (EVs) to demand energy when non-EV demand is low and the electric generators are underutilized. EVs also act as frequency regulators which can control their participation role by modifying their respective payment rate wi. The proposed distributed recharging rate control algorithm can realize a Demand Management solution for EVs and does not require explicit real time communication from the electric generators or between the recharging sockets. A mechanism is presented that can trade off an ideal incentive policy with its approximation, and enable the integration of the proposed controller with legacy protection system. Simulation is used to assess the algorithm and to highlight its embedded characteristics.

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Fair and efficient dynamic bandwidth allocation for multi-application networks

Cited by 18 publications

References 37 publications

Multipoint Control Protocol With Look-Ahead for Wavelength Division Multiplexed Ethernet Passive Optical Network

Multipoint Control Protocol With Look-Ahead for Wavelength Division Multiplexed Ethernet Passive Optical Network

Dynamic pricing and resource allocation using revenue management for multiservice networks

Distributed Recharging Rate Control for Energy Demand Management of Electric Vehicles

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