scholarly journals An Exponential Active Queue Management Method Based on Random Early Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hussein Abdel-Jaber
Keyword(s):  
Early Detection ◽  
Performance Metrics ◽  
Active Queue Management ◽  
Queue Management ◽  
Random Early Detection ◽  
Evaluation Performance ◽  
Queuing Delay ◽  
Packet Dropping ◽  
Dropping Probability ◽  
Évaluation Performance

Congestion is a key topic in computer networks that has been studied extensively by scholars due to its direct impact on a network’s performance. One of the extensively investigated congestion control techniques is random early detection (RED). To sustain RED’s performance to obtain the desired results, scholars usually tune the input parameters, especially the maximum packet dropping probability, into specific value(s). Unfortunately, setting up this parameter into these values leads to good, yet biased, performance results. In this paper, the RED-Exponential Technique (RED_E) is proposed to deal with this issue by dropping arriving packets in an exponential manner without utilizing the maximum packet dropping probability. Simulation tests aiming to contrast E_RED with other Active Queue Management (AQM) methods were conducted using different evaluation performance metrics including mean queue length (mql), throughput (T), average queuing delay (D), overflow packet loss probability (PL), and packet dropping probability (DP). The reported results showed that E_RED offered a marginally higher satisfactory performance with reference to mql and D than that found in common AQM methods in cases of heavy congestion. Moreover, RED_E compares well with the considered AQM methods with reference to the above evaluation performance measures using minimum threshold position (min threshold) at a router buffer.

IGRED: An Improved Gentle Random Early Detection Method for Management of Congested Networks

2019 ◽  
Vol 19 (02) ◽  
pp. 1950004
Author(s):  
HUSSEIN ABDEL-JABER ◽  
ABDULAZIZ SHEHAB ◽  
MOHAMED BARAKAT ◽  
MAGDI RASHAD
Keyword(s):  
Early Detection ◽  
Queue Length ◽  
Detection Method ◽  
Random Early Detection ◽  
Queuing Delay ◽  
Packet Dropping ◽  
Dropping Probability ◽  
Maximum Value ◽  
Congested Networks ◽  
Mean Queue Length

Controlling congested router buffers of a network has a crucial role in improving network’s performance. This paper proposes a novel Active Queue Management (AQM) method named Improved Gentle Random Early Detection (IGRED) that based on GRED algorithm, which counted as one of the popular AQM methods. The proposed is mainly developed to overcome the problems faced with classic GRED. The initial packet-dropping probability depends on several parameters such as the average queue length, maximum value of packet dropping probability, minimum and maximum thresholds, etc. IGRED reduces its reliance on the GRED’s parameters through shrinking these parameters. The results shows, when congestion is taken place, the proposed IGRED provides more satisfactory performance with reference to mean queue length, average queuing delay, and overflow packet loss probability.

scholarly journals Active Queue Management (AQM) Performance Analysis Based On Controled Delay (CoDel) Against Bufferbloat On Real-Time Aplication

2017 ◽  
Vol 2 (1) ◽  
pp. 119
Author(s):  
Muhammad Noer Iskandar
Keyword(s):  
Performance Analysis ◽  
Early Detection ◽  
Real Time ◽  
Video Streaming ◽  
Packet Loss ◽  
Active Queue Management ◽  
Queue Management ◽  
Random Early Detection

<span><em>Bufferbloat </em><span>merupakan salah satu kondisi buffer dengan ukuran besar yang cenderung<br /><span>selalu penuh dan menyebabkan antrian panjang didalam buffer, jika hal ini terjadi secara<br /><span>terus-menerus maka dapat menyebabkan jeda transmisi yang tinggi. <span><em>Bufferbloat </em><span>sering<br /><span>terjadi pada aplikasi berbasis real-time. <span><em>Active Queue Management </em><span>(AQM) merupakan<br /><span>salah satu cara untuk menangani terjadinya <span><em>bufferbloat., </em><span>AQM umumnya menggunakan<br /><span>algoritma Drop Tail untuk menangani kondisi antrian panjang dalam buffer router di<br /><span>jaringan. Namun demikian, performansi AQM berbasis Drop Tail kurang dapat<br /><span>diandalkan karena jeda transmisi dalam keadaan <span><em>bufferbloat </em><span>masih tinggi. Telah banyak<br /><span>studi dilakukan untuk menangani <span><em>bufferbloat</em><span>, seperti Drop Tail, Random Early Detection<br /><span>(RED) dan Controlled Delay (CoDel). Dari riset yang telah dilakukan tersebut masih sulit<br /><span>ditemukan performasi algoritma terbaik dalam menangani <span><em>bufferbloat</em><span>. Untuk hal tersebut,<br /><span>paper ini menyajikan studi performansi penanganan bufferbloat menggunakan ketiga<br /><span>algoritma diatas. Dalam studi ini, video streaming digunakan sebagai <span><em>traffic </em><span>uji untuk<br /><span>menentukan performansi algoritma terbaik dalam mengatasi <span><em>bufferbloat</em><span>. Sedangkan<br /><span>metriks uji yang digunakan dalam riset ini adalah <span><em>latency</em><span>, <span><em>throughput </em><span>dan <span><em>packet-loss</em><span>.<br /><span>Analisa hasil pengujian mengambil 3 hasil terbaik dalam setiap percobaan. Hasil<br /><span>pengujian menunjukan performansi algoritma CoDel jauh lebih baik dalam menangani<br /><span><em>latency </em><span>yang tinggi pada kondisi bufferbloat dibandingkan RED dan Drop Tail. Namun<br /><span>untuk <span><em>packet-loss </em><span>dan <span><em>throughput </em><span>performansi RED dan Drop Tail masih unggul<br /><span>dibanding algoritma CoDel</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span>

scholarly journals Pricing and Unresponsive Flows Purging for Global Rate Enhancement

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
G. Abbas ◽  
A. K. Nagar ◽  
H. Tawfik ◽  
J. Y. Goulermas
Keyword(s):  
Early Detection ◽  
Active Queue Management ◽  
Low Complexity ◽  
Rate Allocation ◽  
Queue Management ◽  
Random Early Detection ◽  
Rate Enhancement ◽  
Global Rate ◽  
High Utilization ◽  
Unresponsive Flows

Pricing-based Active Queue Management (AQM), such as Random Exponential Marking (REM), outperforms other probabilistic counterpart techniques, like Random Early Detection (RED), in terms of both high utilization and negligible loss and delay. However, the pricing-based protocols do not take account of unresponsive flows that can significantly alter the subsequent rate allocation. This letter presents Purge (Pricing and Un-Responsive flows purging for Global rate Enhancement) that extends the REM framework to regulate unresponsive flows. We show that Purge is effective at providing fairness and requires small memory and low-complexity operations.

scholarly journals Deep Reinforcement Learning based Active Queue Management for IoT Networks

2021 ◽  
Author(s):  
Minsu Kim
Keyword(s):  
Reinforcement Learning ◽  
Network Management ◽  
Network Architecture ◽  
Industrial Revolution ◽  
Active Queue Management ◽  
Smart Devices ◽  
Queue Management ◽  
Queuing Delay ◽  
Self Learning ◽  
Management Algorithms

Internet of Things (IoT) has pervaded most aspects of our life through the Fourth Industrial Revolution. It is expected that a typical family home could contain several hundreds of smart devices by 2022. Current network architecture has been moving to fog/edge architecture to have the capacity for IoT. However, in order to deal with the enormous amount of traffic generated by those devices and reduce queuing delay, novel self-learning network management algorithms are required on fog/edge nodes. For efficient network management, Active Queue Management (AQM) has been proposed which is the intelligent queuing discipline. In this paper, we propose a new AQM based on Deep Reinforcement Learning (DRL) to handle the latency as well as the trade-off between queuing delay and throughput. We choose Deep Q-Network (DQN) as a baseline of our scheme, and compare our approach with various AQM schemes by deploying them on the interface of fog/edge node in IoT infrastructure. We simulate the AQM schemes on the different bandwidth and round trip time (RTT) settings, and in the empirical results, our approach outperforms other AQM schemes in terms of delay and jitter maintaining above-average throughput and verifies that DRL applied AQM is an efficient network manager for congestion.

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.

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