scholarly journals Fuzzy-Logic Based Active Queue Management Using Performance Metrics Mapping into Multi-Congestion Indicators

2021 ◽  
Vol 21 (2) ◽  
pp. 29-44
Author(s):  
Mosleh M. Abualhaj ◽  
Mayy M. Al-Tahrawi ◽  
Abdelrahman H. Hussein ◽  
Sumaya N. Al-Khatib
Keyword(s):  
Fuzzy Logic ◽  
Network Performance ◽  
Performance Metrics ◽  
Early Stage ◽  
Active Queue Management ◽  
Traffic Load ◽  
Performance Criteria ◽  
Queue Management ◽  
Method Show

Abstract The congestion problem at the router buffer leads to serious consequences on network performance. Active Queue Management (AQM) has been developed to react to any possible congestion at the router buffer at an early stage. The limitation of the existing fuzzy-based AQM is the utilization of indicators that do not address all the performance criteria and quality of services required. In this paper, a new method for active queue management is proposed based on using the fuzzy logic and multiple performance indicators that are extracted from the network performance metrics. These indicators are queue length, delta queue and expected loss. The simulation of the proposed method show that in high traffic load, the proposed method preserves packet loss, drop packet only when it is necessary and produce a satisfactory delay that outperformed the state-of-the-art AQM methods.

scholarly journals Weight Queue Dynamic Active Queue Management Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2077
Author(s):  
Mahmoud Baklizi
Keyword(s):  
Queue Length ◽  
Network Performance ◽  
Active Queue Management ◽  
Traffic Load ◽  
Dynamic Monitoring ◽  
Queue Management ◽  
Queuing Delay ◽  
Management Algorithm ◽  
Mean Queue Length ◽  
Management Algorithms

The current problem of packets generation and transformation around the world is router congestion, which then leads to a decline in the network performance in term of queuing delay (D) and packet loss (PL). The existing active queue management (AQM) algorithms do not optimize the network performance because these algorithms use static techniques for detecting and reacting to congestion at the router buffer. In this paper, a weight queue active queue management (WQDAQM) based on dynamic monitoring and reacting is proposed. Queue weight and the thresholds are dynamically adjusted based on the traffic load. WQDAQM controls the queue within the router buffer by stabilizing the queue weight between two thresholds dynamically. The WQDAQM algorithm is simulated and compared with the existing active queue management algorithms. The results reveal that the proposed method demonstrates better performance in terms mean queue length, D, PL, and dropping probability, compared to gentle random early detection (GRED), dynamic GRED, and stabilized dynamic GRED in both heavy or no-congestion cases. In detail, in a heavy congestion status, the proposed algorithm overperformed dynamic GRED (DGRED) by 13.3%, GRED by 19.2%, stabilized dynamic GRED (SDGRED) by 6.7% in term of mean queue length (mql). In terms of D in a heavy congestion status, the proposed algorithm overperformed DGRED by 13.3%, GRED by 19.3%, SDGRED by 6.3%. As for PL, the proposed algorithm overperformed DGRED by 15.5%, SDGRED by 19.8%, GRED by 86.3% in term of PL.

scholarly journals Active Queue Management Techniques for Congestion Control in TCP Communication Networks: New Prospective

2020 ◽  
Vol 9 (2S5) ◽  
pp. 73-77
Keyword(s):  
Communication Networks ◽  
Bandwidth Allocation ◽  
Network Performance ◽  
Computer Network ◽  
Active Queue Management ◽  
Traffic Load ◽  
Queue Management ◽  
Queuing Delay ◽  
Management Techniques ◽  
Overall Performance

the computer network area has grown very fast from previous years, as a result of which the control of traffic load in the network is at a higher priority. In network, congestion occurs if numbers of coming packets exceed, like bandwidth allocation along with buffer space. This might be due to poor network performance in terms of throughput, packet loss rate, and average packet queuing delay. For enhancing the overall performance when this network will become congested, numerous exclusive aqm (active queue management) techniques were proposed and few are discussed in this research paper. Particularly, aqm strategies are analyzed in detail as well as their obstacles along with strengths are emphasized. There are several algorithms which are under the aqm like ared, fred, choke, red (random early detection), blue, stochastic fair blue (sfb), random exponential marking (rem), svb, raq, etc.

scholarly journals SARED: A Self-Adaptive Active Queue Management Scheme for Improving Quality of Service in Network Systems

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Aminu Adamu
Keyword(s):  
Quality Of Service ◽  
Load Condition ◽  
Active Queue Management ◽  
Traffic Load ◽  
Queue Management ◽  
Network Systems ◽  
Adaptation Mechanism ◽  
Linear Mode ◽  
Self Adaptive

Considering the phenomenal growth of network systems, congestion remains a threat to the quality of service provided in such systems, hence, research on congestion control is still relevant. Internet research community regards Active Queue Management (AQM) as an effective approach to address congestion in network systems. Most of the existing AQM schemes possess static drop patterns and lack self-adaptation mechanism, as such don’t work well for networks where traffic load fluctuates. This paper proposes Self-Adaptive Random Early Detection (SARED) scheme which smartly adapts its drop pattern based on current network’s traffic load in order to maintain better and stable performance. In light to moderate load conditions, SARED operates in nonlinear modes in order to maximize utilization and throughput, while in high load condition, it switches to linear mode in order to avoid forced drops and congestion. Experiments conducted have revealed that regardless of traffic load’s condition, SARED provides optimal performance.

Voice and Video over Gigabit Ethernet and ATM Backbone Networks

2014 ◽  
pp. 174-184
Author(s):  
Nurul I. Sarkar ◽  
Yash Dole
Keyword(s):  
Quality Of Service ◽  
Network Performance ◽  
Performance Metrics ◽  
Asynchronous Transfer Mode ◽  
Atm Networks ◽  
Gigabit Ethernet ◽  
Corporate Networks ◽  
Backbone Networks ◽  
Network Technologies

This chapter aims to report on the performance of voice and video traffic over two popular backbone network technologies, namely Gigabit Ethernet (GbE) and Asynchronous Transfer Mode (ATM). ATM networks are being used by many universities and organizations for their unique characteristics such as scalability and guaranteed Quality of Service (QoS), especially for voice and video applications. Gigabit Ethernet matches ATM functionality by providing higher bandwidth at much lower cost, less complexity, and easier integration into the existing Ethernet technologies. It is useful to be able to compare these two technologies against various network performance metrics to find out which technology performs better for transporting voice and video conferencing. This chapter provides an in-depth performance analysis and comparison of GbE and ATM networks by extensive OPNET-based simulation. The authors measure the Quality of Service (QoS) parameters, such as voice and video throughput, end-to-end delay, and voice jitter. The analysis and simulation results reported in this chapter provide some insights into the performance of GbE and ATM backbone networks. This chapter may help network researchers and engineers in selecting the best technology for the deployment of backbone campus and corporate networks.

Admission Control for QoS Provision in Mobile Wireless Networks

2010 ◽  
pp. 427-457
Author(s):  
Georgios I. Tsiropoulos ◽  
Dimitrios G. Stratogiannis ◽  
John D. Kanellopoulos ◽  
Panayotis G. Cottis
Keyword(s):  
Wireless Networks ◽  
Admission Control ◽  
Network Performance ◽  
Radio Resource Management ◽  
Performance Criterion ◽  
Mobile Wireless Networks ◽  
Performance Criteria ◽  
Mobile Wireless ◽  
Efficiency Estimation

Admission control is one of the key elements for ensuring the quality of service (QoS) in modern mobile wireless networks. Since such networks are resource constrained, supporting multimedia traffic guaranteeing its QoS levels is excessively challenging for call admission control (CAC) design. CAC is the most important radio resource management (RRM) function in wireless networks as its efficiency has a direct impact on network performance and QoS provision to end users. The goal of this chapter is to provide a thorough study of the basic concepts considering CAC design and a comprehensive analysis of the fundamental CAC schemes employed in wireless networks. The basic performance criterion considering CAC schemes is the probability of denying the access to the network for an arriving call, which is extensively studied in this chapter. Moreover, additional performance criteria are presented and discussed, which may help to provide an overall efficiency estimation of the available CAC schemes.

scholarly journals A Fuzzy Logic Based Network Congestion Control Using Active Queue Management Techniques

2010 ◽  
Vol 2 (2) ◽  
pp. 273-284 ◽  
Author(s):  
I. K. Tabash ◽  
M. A. Mamun ◽  
A. Negi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Probability Function ◽  
Active Queue Management ◽  
Buffer Size ◽  
Queue Management ◽  
Passive Devices ◽  
Better Regulation ◽  
Ip Routers ◽  
Intelligent Approach

Conventional IP routers are passive devices that accept packets and perform the routing function on any input. Usually the tail-drop (TD) strategy is used where the input which exceeds the buffer capacity are simply dropped. In active queue management (AQM) methods routers manage their buffers by dropping packets selectively. We study one of the AQM methods called as random exponential marking (REM). We propose an intelligent approach to AQM based on fuzzy logic controller (FLC) to drop packets dynamically, keep the buffer size around desired level and also prevent buffer overflow. Our proposed approach is based on REM algorithm, which drops the packets by drop probability function. In our proposal we replace the drop probability function by a FLC to drop the packets, stabilize the buffer around the desired size and reduce delay. Simulation results show a better regulation of the buffer.  Keywords: Random exponential marking; Active queue management; Fuzzy logic controller; Pro-active queue management. © 2010 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. DOI: 10.3329/jsr.v2i2.2786               J. Sci. Res. 2 (2), 273-284 (2010) 

scholarly journals Network Performance Criteria for Telecommunication Traffic Types driven by Quality of Experience

2019 ◽  
Vol 15 (3) ◽  
pp. 233-244 ◽  
Author(s):  
Ines Ramadža ◽  
Vesna Pekić ◽  
Julije Ožegović
Keyword(s):  
Network Performance ◽  
Quality Of Experience ◽  
Performance Criteria ◽  
Live Streaming ◽  
Traffic Routing ◽  
Different Types ◽  
Telecom Operator ◽  
The Impact ◽  
The Given

A common reason for changing the chosen service provider is the users' perception of service. Quality of Experience (QoE) describes the end user's perception of service while using it. A frequent cause of QoE degradation is inadequate traffic routing, where, other than throughput, selected routes do not satisfy minimum network requirements for the given service or services. In order to enable QoE-driven routing, per traffic type defined routing criteria are required. Our goal was to obtain those criteria for relevant services of a telecom operator. For the purpose of identifying services of interest, we first provide short results of user traffic analysis within the telecom operator network. Next, our work presents testbed measurements which explore the impact of packet loss and delay on user QoE for video, voice, and management traffic. For video services, we investigated separately multicast delivery, unicast HTTP Live Streaming (HLS), and unicast Real Time Streaming Protocol (RTSP) traffic. Applying a threshold to QoE values, from the measured dependencies we extracted minimum network performance criteria for the investigated different types of traffic. Finally, we provide a comparison with results available in the literature on the topic.

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