Reinforcement Learning for Load Management in DiffServ-MPLS Mobile Networks

Vučević, Nemanja; Perez-Romero, J.; Sallent, O.; Agustí, R.

doi:10.1109/vetecs.2009.5073831

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…Authors in [27] consider different link scheduling and dynamic routeing algorithms for load balancing and failure recovery in a millimetrewave (mmWave) backhaul network, as part of the MiWaveS project. 3 SON is used in routers of a DiffServ-MPLS 4 mobile transport network to manage load balancing and resource allocation while preserving the quality of service [28]. Moreover, authors in [29] propose heuristic algorithms to manipulate routeing metrics centrally, by adopting dynamic, static, or vector-metric setting to find optimum values, following link status (capacity, load, availability).…”

Section: A Dynamic and Adaptive Routeingmentioning

confidence: 99%

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

et al. 2016

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5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, selfoptimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes.INDEX TERMS Backhaul, fronthaul, user-centric, user-cell association, SON, reinforcement learning, multiple attribute decision making.

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Section: A Dynamic and Adaptive Routeingmentioning

confidence: 99%

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

et al. 2016

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“…Although a great variety of proposals have been made to improve congestion control at the transport or at the application layer, as surveyed in [3], enhancements at the network layer seem a more promising approach due to the effective control of the network resources they provide [4], [5]. Thus, a significant research effort is being put in the design of efficient queuing and scheduling disciplines, as well as in definitions for policies.…”

Section: A Congestion Control Techniquesmentioning

confidence: 99%

“…This is the case of the works by [4] and [5], where distributed agents at the edge of the MPLS-enabled network help alleviate overload situations or perform load balancing by means of labeling choices.…”

Section: Learning Techniques In Qos Controlmentioning

confidence: 99%

Profile-Based Adaptive DiffServ Policing with Learning Techniques

Cruvinel

Vazão

2011

2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN)

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IETF's Differentiated Services (DiffServ) is a scalable, distributed architecture aimed to provide a fair distribution of network resources according to expected levels of service. However, variable traffic and performance expectations may require an adjustment of management policies so that congestion situations are avoided and service parameters are met. Existing solutions that improve the standard DiffServ architecture do not cope well with this dynamics and network resources are not efficiently managed, preventing user expectations from being properly fulfilled. In this paper we show how a reinforcement learning approach can optimize the choice of adaptation profiles for the optimal adjustment of ingress policies when a precongestion state is detected. Numerical results from simulations showed improvements for priority traffic (video) without impacting excessively the other traffic.

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Reinforcement Learning for Load Management in DiffServ-MPLS Mobile Networks

Cited by 2 publications

References 18 publications

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

Profile-Based Adaptive DiffServ Policing with Learning Techniques

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