Performance Analysis of Network with Different Queuing Mechanisms in TCP/FTP and UDP/FTP Scenario

Patel, Nehal; Patel, Radhika

doi:10.1007/978-981-13-8406-6_2

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Nele, sempre que a fila está cheia, os pacotes que chegam são descartados até que seja gerado um espac ¸o adequado para aceitar novos pacotes. Logo, além de simples, é o mais utilizado por apresentar um alto grau de eficiência [Patel and Patel 2019].…”

Section: Execuc ¸ãO Dos Experimentosunclassified

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: Execuc ¸ãO Dos Experimentosunclassified

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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“…As a consequence, the maximum bandwidth IOSv images can support is around 2Mb/sec. On the contrary, the end-users in our experiments are transferring files based on the TCP windowing algorithm [35] and a single end-user is expected to fully occupy this limited bandwidth. To alleviate this bandwidth restraint, we limit the maximum transfer speed to 20 KB/sec.…”

Section: A Testbedmentioning

confidence: 99%

A Machine-Learning-Based Action Recommender for Network Operation Centers

Mohammed

Côté

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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Failure management and cost-aware traffic engineering are two important tasks done in Network Operation Centers (NOC). These are performed by expert technicians who must carefully analyze the network state and the flow of incoming alarms to decide how, where and when to take actions on the network. While based on implicit guiding principles, these network actions are very hard to automate with explicit rules due to the high complexity of the system; hence NOC action is essentially a manual process today. To automate part of that process, in this paper we introduce an Action Recommendation Engine (ARE) that can learn implicit NOC action rules with supervised machine learning from historical data. As a result, ARE can recommend suitable action(s) to remedy network faults and engineer the traffic to minimize costs, all while maximizing the users' Quality of Experience. To quantify the effectiveness of different NOC action scenarios, we introduce the QoE-OPEX metric which balances between users' quality of Experience and ISP's operational costs. After proper model training on 56,000 data points with 66 features, we demonstrate that ARE can effectively reproduce implicit action-taking logic of NOC technicians, thus moving us one step closer to reliable autonomous networks and fully-automated NOCs.

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“…Various techniques are developed and analyzed for congestion RED, where the early congestion control (ECC) is employed, three sections random early detection (TRED), other methods are based on non-congestion notification, fuzzy logic dimensions, characterization of problems for the congestions in the RED, Hemi-rise cloud model (CRED), and congestion avoidance mechanisms to improve by Learning Automata Like (LAL) philosophy, also several suggestions to improve the congestion control mechanism are presented, (Zala & Vyas, 2020), Congestion considered as a most important challenges and critical issue in Wireless sensor networks (WSNs), which affects energy consumption and various parameters of QoS in sensor nodes. Various methods and algorithms employed, and the effective parameter in detecting and controlling congestion is used, (Bohloulzadeh, 2020), the Drop Tail, RED, SFQ, and FQ are assessed by varying the queue size, and the performance analysis and comparison of the various queues are represented in terms of throughput and packet loss, (Patel, N. & Patel, R. 2020), one-dimensional, discrete-time nonlinear model for Internet congestion control at the routers, which lead to an adaptive congestion control algorithm with a more stable performance than other algorithms currently in use, where the states correspond to the average queue sizes of the incoming data packets, ( Amigó, 2020).…”

Section: Related Workmentioning

confidence: 99%

Influencing Performance Measurements through Varying Packet Capacities of Queue Nodes - DRED

Ababneh¹

2020

MAS

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This paper explores the impact of changes to the packet capacities of queue nodes on the performance of congestion algorithms. The dynamic random early drop (DRED) algorithm is crucial in managing congestion, predicting it, and selecting the optimal packet number. Two performance measures were employed to evaluate the effect of varying packet capacities, namely throughput and average queuing delay. Plasticity and efficacy of varying capacities of queue nodes was examined, and varying packet capacities of queue nodes were compared to assess performance. The first queue node performed more effectively than the second queue node in terms of throughput, while the second queue node outperformed the third queue node on both measures.

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Performance Analysis of Network with Different Queuing Mechanisms in TCP/FTP and UDP/FTP Scenario

Cited by 3 publications

References 2 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 Machine-Learning-Based Action Recommender for Network Operation Centers

Influencing Performance Measurements through Varying Packet Capacities of Queue Nodes - DRED

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