Comparative analysis of queuing mechanisms: Droptail, RED and NLRED

Rastogi, Shubhangi; Zaheer, Hira

doi:10.1007/s13278-016-0382-5

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…[ Rastogi and Zaheer 2016] apresenta uma comparac ¸ão de três mecanismos de enfileiramento, ou seja, Droptail, RED e Nonlinear Random Early Detection (NLRED) com base em diferentes métricas de desempenho. A comparac ¸ão é feita para saber qual mecanismo é adequado, de acordo com a rede e o tráfego.…”

Section: Trabalhos Relacionadosunclassified

Análise do RED e sua Influência na Autossimilaridade do Tráfego de Rede

Moraes

Callado

2022

Anais Do XXVII Workshop De Gerência E Operação De Redes E Serviços (WGRS 2022)

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Com a descoberta de que o tráfego da rede possui a característica de autossimilaridade, alguns estudos buscaram diminuí-la, pois essa característica causa alguns efeitos negativos, como maior atraso na fila e congestionamento do tráfego. Entre os fatores trabalhados nas últimas décadas estão algoritmos de gerenciamento de filas, como Random Early Detection (RED). No entanto, os pesquisadores não estudaram a influência do RED na auto-similaridade mais profundamente. Afinal, esse algoritmo possui quatro parâmetros configuráveis (o peso da fila, a probabilidade máxima de queda e os limiares mínimo e máximo), que, quando modificados, podem levar a uma alteração no desempenho e, consequentemente, na autossimilaridade. Portanto, este artigo pretende verificar a influência do RED na auto-similaridade. Para isso, desenvolvemos um padrão para auxiliar na configuração dos limiares de RED. Também, mostramos o impacto de diferentes arranjos de limite na autossimilaridade e no desempenho do tráfego de rede. Além disso, comparamos algumas das melhores configurações de RED com Droptail.

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Section: Trabalhos Relacionadosunclassified

Análise do RED e sua Influência na Autossimilaridade do Tráfego de Rede

Moraes

Callado

2022

Anais Do XXVII Workshop De Gerência E Operação De Redes E Serviços (WGRS 2022)

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“…Droptail is a passive method where router drops the tail of the data packets as queue overflows, thereby it reduces the throughput. The Random Early Detection (RED) queue mechanism does not wait until the buffer is filled up to detect congestion or by removing data packets, but start to detect congestion before the buffer is nearing overflow [13][14]. A review of reliable transport protocols in WSN is carried out in [15].…”

Section: Related Workmentioning

confidence: 99%

“…When the average queue length is more than the MinThres, the packets will be dropped randomly or will be marked in terms of Explicit Congestion Notification (ECN) bit. If average queue size is more than the MaxThres, all the packets arrived will be dropped [13][14]. The Avg parameter is initialized to zero and is monitored.…”

Section: Random Early Discard (Red)mentioning

confidence: 99%

A Joint Congestion Control Mechanism Through Dynamic Alternate Route Selection Algorithm in IoT Based Wireless Sensor Network

JayavigneshThyagarajan

Suganthi

Rajesh

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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Internet of Things (IoT) based Wireless Sensor Networks (WSN) comprises several miniaturized sensor nodes limited in terms of transmission range, available battery power and data rate. These nodes work collaboratively to monitor physical/environmental conditions and provide appropriate control action. Congestion has become one of the key issues in WSN because of the increase in multimedia traffic and IoT proliferation in WSN. The higher traffic load can easily lead to link-level or node-level congestion in the network. A joint layer 4/layer 3 driven congestion control distributed algorithm is proposed to detect congestion and thereby avoid congestion through alternate routing. In this work, congestion detection and avoidance method are presented which uses Random Early Discard (RED) scheme, instead of the Droptail Queue. This scheme computes a threshold to avoid further congestion and uses Location Aided Energy Efficient Routing protocol (LAEER) to find the alternate path for routing data packets. As a result, this approach achieves load balancing as it spreads traffic throughout the network. Also, simulation results infer that LAEER shows better results in terms of Quality of Service (QoS) metrics such as PDR in comparison with AODV as it has avoided further packet drops proactively through alternate forwarding neighbor selection.

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“…RED drops packets with non-zero drop probability after the average queue size exceeds a certain minimum threshold, it doesn't wait for the queue to be completely filled up. Its objectives are to provide high link utilization, remove biases against busty sources, attenuate packet loss and queuing delay, and reduce the need of global synchronization of sources [9]. RED was proposed to reduce limitations in Drop Tail, it is an active queue technique.…”

Section: B Random Early Detection (Red)mentioning

confidence: 99%

Comparative Analysis of Drop Tail, Red and NLRED Congestion Control Algorithms Using NS2 Simulator

Eva¹,

Kabari²

2018

IJSRP

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Congestion in a network may occur when the load on the network is greater than the capacity of the network and consequently, performance degrades. Congestion control is a technique and mechanisms that can either prevent congestion before it happens, or remove congestion after it has happened. This work presents a comparative analysis of Drop Tail, RED and NLRED congestion control algorithm and performed a simulation experiment to show their relative effectiveness. The experiment was done using NS2 simulator on the basis of Endto-End Delay, Throughput, Packet Drop, and Packet Delivery Ratio in a wired network. The simulation result showed that NLRED performs best in high congestion network, while in low cohesive network, Drop Tail gives a good result. Drop Tail, RED and NLRED was also analysed in real audio traffic and the entire results showed that in congested network, NLRED and RED are better while in low congested network Drop Tail is better. This is because in heavy congested network, congestion avoidance mechanism aids the network to gain better performance while in low congested network unnecessary computation avoidance mechanisms degrades the performance of the network. If parameters are set effectively in RED, it will be the best queuing mechanism for that particular network. However, it is of great importance to know which congestion control mechanism is suitable for each network and traffic.

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Comparative analysis of queuing mechanisms: Droptail, RED and NLRED

Cited by 13 publications

References 12 publications

Análise do RED e sua Influência na Autossimilaridade do Tráfego de Rede

Análise do RED e sua Influência na Autossimilaridade do Tráfego de Rede

A Joint Congestion Control Mechanism Through Dynamic Alternate Route Selection Algorithm in IoT Based Wireless Sensor Network

Comparative Analysis of Drop Tail, Red and NLRED Congestion Control Algorithms Using NS2 Simulator

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