Application of AR based model in proactive management of VBR traffic

Randhawa, T.S.; Hardy, R.H.S.

doi:10.1109/icatm.1998.688182

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…For this reason, also the bu!er occupancy levels must be considered to get a realistic view of the performance of the congestion control methods. We have de"ned the calculative overall performance variable described in Equation (10). This performance variable measures the performance as a combination of the throughput and bu!er occupancy values.…”

Section: Performance Variables Of the Testsmentioning

confidence: 99%

Comparison of different congestion control strategies for low priority controllable traffic in packet switched backbone networks

Pulakka

Harju

2001

Int J Communication

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SUMMARYIt is expected that a signi"cant part of the data #ows of future multi-service packet switched backbone networks will use low priority, non-real-time data transmission services of the networks. The common bene"t for both user applications and network operators is that the data #ows of the low priority services could use the free capacity of the networks, after the load of higher priority data #ows. Congestion control methods are needed for these low priority data #ows to reach an optimal utilisation level of the networks, high throughput and low packet loss ratios. This kind of low priority data transmission service which adjusts the data rates of the data #ows according to the data rate changes of higher priority data #ows, but does not guarantee any speci"c service for these data #ows, is called a controlled load service. In this paper, we have compared the performance, e$ciency and scalability of four di!erent congestion control methods designed for the controlled load service. Two of these methods were based on very simple congestion control algorithms and the other two used relatively complex control algorithms based on control methods utilising computational intelligence. The principal aim of this study was to research how remarkable were the e!ects that the di!erent complexities of the congestion control methods had on the achieved level of service. The simulation tests indicate that the complexity of the methods clearly a!ects the performance and e$ciency of the methods.

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Section: Performance Variables Of the Testsmentioning

confidence: 99%

Comparison of different congestion control strategies for low priority controllable traffic in packet switched backbone networks

Pulakka

Harju

2001

Int J Communication

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“…Such capability can significantly improve the effectiveness of tasks such as preventative congestion control. The scheme involves estimating the current traffic state over small observation intervals and predicting the future traffic (Randhawa and Hardy [37,38,39]). Using this capability, the onset of congestion could be forecasted and, subsequently, a preventative control action could be taken ahead of time.…”

Section: Packet Level Controlmentioning

confidence: 99%

“…These models include Markov Modulated Fluid Flow (MMFF) model [31], Markov Modulated Poisson Process (MMPP) model [45,47,48], Markov Modulated Deterministic Arrival model (MMDA) [45,47,48], and AutoRegressive model [31]. In the proposed approach (Randhawa and Hardy [37,38]) an AR modulated model, derived from the one proposed by Maglaris et al [31], is assumed. The first and second order statistics of the traffic at the multiplexer output are derived.…”

Section: Packet Level Controlmentioning

confidence: 99%

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Network Management in Wired and Wireless Networks

Randhawa¹,

Hardy²

2002

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“…During the past few years, several studies have been published where the behaviour of the VBR (Variable Bit Rate) type of traffic has been predicted using LMS (Least Mean Square), RLS (Recursive Least Square) or fuzzy logic based methods (e.g. [3] [4] [5] [6] ).…”

Section: Introductionmentioning

confidence: 99%

Efficiency of the Prediction of High Priority Traffic in Enhancing the Rate Based Control of Low Priority Traffic

Pulakka

Harju

2000

Telecommunication Network Intelligence

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Abstract:In this paper, we study how effectively the rate based control of a low priority data transmission service in a packet switched backbone network can be implemented, when the control decisions are based on the predictions of the amount of high priority traffic. ANFIS (adaptive neuro-fuzzy inference systems) predictors are used for traffic prediction. In our service model, all control functions of the low priority data transmission service are distributed to the edge switches of the backbone network. The routes and data rates of the low priority data flows are iteratively controlled by the control system, according to the predicted data rate variations of the high priority data flows. The efficiency of the service model has been tested by simulations. The emphasis is on the effects of different traffic distributions of the high priority data flows and on the impact and importance of the amount of overhead caused by the traffic prediction and the transmission of control information.

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Application of AR based model in proactive management of VBR traffic

Cited by 4 publications

References 9 publications

Comparison of different congestion control strategies for low priority controllable traffic in packet switched backbone networks

Comparison of different congestion control strategies for low priority controllable traffic in packet switched backbone networks

Network Management in Wired and Wireless Networks

Efficiency of the Prediction of High Priority Traffic in Enhancing the Rate Based Control of Low Priority Traffic

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