Active queue management using adaptive RED

Verma, Rahul Kumar; Iyer, Aravind; Karandikar, Abhay

doi:10.1109/jcn.2003.6596822

Cited by 10 publications

(9 citation statements)

References 6 publications

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“…RED [10] (Random Early Detection) technique is used for managing the queue actively which provides a competence of early congestion by dropping/marking (if ECN is active) the packets as the queue size increases by some threshold. This system reduces the packet loss rate and increases throughput of the network [3][4] [5]. Although RED provides a solution for active queue management but the function used in RED algorithm for calculating the probability of dropping the packets when queue size increases by some minimum threshold is linear function of queue average length.…”

Section: Fig 1: a Mobile Ad Hoc Network With 6 Nodesmentioning

confidence: 99%

The Modified Gaussian Function based RED (MGF-RED) Algorithm for Congestion Avoidance in Mobile Ad Hoc Networks

Mahajan¹,

Singh²

2014

IJCA

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In this paper the idea is to rationale a new active queue management technique (MGF-RED) based on Random Early Detection (RED), a well known active queue management technique for congestion avoidance. The conventional RED uses the concept of minimum threshold, maximum threshold and average queue length. If the average queue length increases by minimum threshold then the algorithm start dropping the packets with some probability using linear function of average queue size. If the average queue size is greater than the maximum threshold then packets are dropped with 1 probability. The proposed algorithm will use Modified Gaussian function for calculating the likelihood of packet drop as the average queue length increases the minimum threshold. Simulation has been performed using NS-2.34. The simulation results show that the proposed algorithm will present better network performance in throughput, packet delivery fraction and number of dropped packets. General TermsMANET, RED, Gaussian Function.

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Section: Fig 1: a Mobile Ad Hoc Network With 6 Nodesmentioning

confidence: 99%

The Modified Gaussian Function based RED (MGF-RED) Algorithm for Congestion Avoidance in Mobile Ad Hoc Networks

Mahajan¹,

Singh²

2014

IJCA

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“…An ARED based algorithm 3 that adaptively varies w q along with max p is designed in [24]. The main goal of the algorithm is to modulate the aggressiveness of RED by varying the value of w q based on the changes in avg.…”

Section: Setting W Q For Ewmamentioning

confidence: 99%

ANLRED: A Robust AQM Mechanism for Congestion Avoidance

S¹

2013

IJCA

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Internet over the past few years has undergone dramatic changes in terms of scale, penetration rate and the diversity of applications. The demand for continuous network connectivity is profilerating. Moreover, it has been observed that the usage of real time applications like Voice over IP (VoIP) and Live Streaming has increased drastically. Passive Queue Management (PQM) mechanisms in the routers do not react to congestion till the buffers overflow. This has two severe consequences: (i) large queueing delays that hurt the performance of real time traffic because such traffic is sensitive to delay and (ii) a large number of consecutive packet drops which affect the network stability. Recently, there has been a lot of interest in the deployment of Active Queue Management (AQM) mechanisms in modern Internet routers to overcome drawbacks of buffer overflow. Although Random Early Detection (RED) is the most widely studied AQM mechanism, it is highly sensitive to parameter settings. In this paper, we propose a robust AQM mechanism named Adaptive Nonlinear RED (ANLRED) which minimizes the parameter sensitivity of RED. Results obtained using ns-2 in a wide variety of Internet scenarios show that ANLRED improves the overall performance of the network in terms of link utilization while maintaining an acceptable mean queue length and minimal packet drop rate. Moreover, ANLRED implementation requires minimum algorithmic changes and hence, is easy to deploy. General Terms:Network Congestion, Queue Management

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“…Many schemes that improve the performance of RED by tuning the different parameters, such as control-parameter settings [27], varying the rate of change of the average queue size [5], and improving the response time when RED recovers from congestion [28] have been proposed lately.…”

Section: Lalred Algorithmmentioning

confidence: 99%

Random Early Detection for Congestion Avoidance in Wired Networks: A Discretized Pursuit Learning-Automata-Like Solution

Misra

Oommen

Yanamandra

et al. 2010

IEEE Trans. Syst., Man, Cybern. B

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Abstract-In this paper, we present a learning-automata-like 1 (LAL) mechanism for congestion avoidance in wired networks. Our algorithm, named as LAL Random Early Detection (LALRED), is founded on the principles of the operations of existing RED congestion-avoidance mechanisms, augmented with a LAL philosophy. The primary objective of LALRED is to optimize the value of the average size of the queue used for congestion avoidance and to consequently reduce the total loss of packets at the queue. We attempt to achieve this by stationing a LAL algorithm at the gateways and by discretizing the probabilities of the corresponding actions of the congestion-avoidance algorithm. At every time instant, the LAL scheme, in turn, chooses the action that possesses the maximal ratio between the number of times the chosen action is rewarded and the number of times that it has been chosen. In LALRED, we simultaneously increase the likelihood of the scheme converging to the action, which minimizes the number of packet drops at the gateway. Our approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue-loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of LALRED over the traditional RED methods which were chosen as the benchmarks for performance comparison purposes.Index Terms-Average queue size, discretized pursuit learning, queue loss, random early detection (RED), stochastic learning automata (LA).

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Active queue management using adaptive RED

Cited by 10 publications

References 6 publications

The Modified Gaussian Function based RED (MGF-RED) Algorithm for Congestion Avoidance in Mobile Ad Hoc Networks

The Modified Gaussian Function based RED (MGF-RED) Algorithm for Congestion Avoidance in Mobile Ad Hoc Networks

ANLRED: A Robust AQM Mechanism for Congestion Avoidance

Random Early Detection for Congestion Avoidance in Wired Networks: A Discretized Pursuit Learning-Automata-Like Solution

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