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

Misra, Sudip; Oommen, B. John; Yanamandra, Sreekeerthy; Obaidat, Mohammad S.

doi:10.1109/tsmcb.2009.2032363

Cited by 80 publications

(34 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Convergence to fairness has been a topic that attracted significant attention from the networking research community, for several network types ranging from the internet in general (Tan and Yang, 2009;Misra et al, 2010) to machine-to-machine (Ferdouse et al, 2015;Teja et al, 2015), healthcare (Misra et al, 2009b;Misra and Chatterjee, 2014) and wireless networks (Dhurandhe et al, 2011;Chatterjee and Misra, 2014;Dhurandher et al, 2009;Yadav et al, 2014). The focus of the present study is the fair allocation of bandwidth to internet TCP flows.…”

Section: Related Workmentioning

confidence: 99%

Fast-fair handling of flows

Tsioliaridou

Zhang

Liaskos

et al. 2017

IJCNDS

View full text Add to dashboard Cite

Allocating fairly the maximum available bandwidth among competing flows is an important problem as it allows maximising system throughput and fairness. Given the dynamic nature of traffic load in current internet and the short duration of most modern applications, fast convergence to fairness is necessitated. In this paper, we use fairness as the major criterion to adjust traffic and present a novel congestion control scheme which allows each flow independently to estimate efficiently: 1) its deviation from the fair-share; 2) adjust its window to its fair-share. In our model, no flow is aware of the number of competitors in the channel, no flow is aware of the bandwidth and buffer capacity; however, the system reaches to equilibrium state fast, based on throughput measurements. Simulation results confirm that the proposed congestion scheme promotes fast system fairness without damaging system efficiency and responsiveness.

show abstract

Section: Related Workmentioning

confidence: 99%

Fast-fair handling of flows

Tsioliaridou

Zhang

Liaskos

et al. 2017

IJCNDS

View full text Add to dashboard Cite

show abstract

“…The authors have compared their work with respect to voice and data traffic and showed that it can control congestion based on the available delay information. Misra et al [20] has presented a unique technique based on automata theory for managing the congestion over wired network. The author have also applied stochastic-learning based mechanism and cellular automata for managing an effective queue size.…”

Section: Related Workmentioning

confidence: 99%

Congestion Control using Cross layer and Stochastic Approach in Distributed Networks

Selvarani¹,

Vinodha²

2017

ijacsa

View full text Add to dashboard Cite

Abstract-In recent past, the current Internet architecture has many challenges in supporting the magnificent network traffic. Among the various that affect the quality of communication in the massive architecture the challenge in maintaining congestion free flow of traffic is one of the major concerns. In this paper, we propose a novel technique to address this issue using cross layer paradigm based on stochastic approach with extended markovian model. The cross layer approach will bridge the physical layer, link layer, network layer and transport layer to control congestion. The resource provisioning operation will be carried out over link layer and the mechanism of exploring the congestion using stochastic approach will be implemented over the network layer. The Markov modeling is adopted to identify the best routes amidst of highly congested paths and it is carried out at the transport layer. An analytical research methodology will be adopted to prove that it is feasible to develop a technique that can identify the origination point of congestion and share the same with the entire network. It is found that this approach for congestion control is effective with respect to end to end delay, packet delivery ratio and processing time.

show abstract

“…There is a large number of research works related to the subject. For example, [5][6][7] focused on the parameter settings to improve the service performance. In order to enhance the robustness of RED, researchers developed some algorithms based on RED such as Adaptive RED [8], Blue [9], and Weighted RED [10].…”

Section: Related Workmentioning

confidence: 99%

Modeling and Analysis on Congestion Control for Data Transmission in Sensor Clouds

Huang

Duan

et al. 2014

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) and Cloud computing are two emerging technologies, respectively, in the networking and computing fields. Sensor Cloud integrates WSNs and Clouds into an extended form of cloud infrastructure that manages sensors scattered throughout WSNs. Networking plays a crucial role in Cloud computing and becomes a particularly challenging issue in sensor Clouds due to resource limitation of WSNs as the underlying network of the Cloud infrastructure. Therefore, data transmission from sensors to servers in sensor Clouds must be carefully controlled to avoid network congestion. In this paper, we study Random Early Detection-Based (RED-based) congestion control for data transmission in sensor Clouds. Specifically we propose an improved RED (IRED) algorithm that meets sensor Cloud networking requirements better and develop a queueing model coupled with analysis technique to evaluate the performance of the proposed congestion control scheme. We also conduct extensive simulations to evaluate performance of IRED compared with the regular RED algorithm. Our analysis and simulation results demonstrate that the proposed IRED congestion control achieves comparable delay performance and preferable throughput performance in comparison with the standard RED for data transmission in sensor Clouds. The easily implemented control mechanism and low complexity of our proposed algorithm make it more suitable for sensor Clouds.

show abstract

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

Cited by 80 publications

References 15 publications

Fast-fair handling of flows

Fast-fair handling of flows

Congestion Control using Cross layer and Stochastic Approach in Distributed Networks

Modeling and Analysis on Congestion Control for Data Transmission in Sensor Clouds

Contact Info

Product

Resources

About