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 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 Can Multipath TCP Be Robust to Cyber Attacks? A Measuring Study of MPTCP with Active Queue Management Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuanlong Cao ◽  
Ruiwen Ji ◽  
Lejun Ji ◽  
Mengshuang Bao ◽  
Lei Tao ◽  
...  
Keyword(s):  
Packet Loss ◽  
Loss Rate ◽  
Active Queue Management ◽  
Cyber Attacks ◽  
Packet Loss Rate ◽  
Queue Management ◽  
Transmission Performance ◽  
Management Algorithm ◽  
Multipath Tcp ◽  
Management Algorithms

With the development of social networks, more and more mobile social network devices have multiple interfaces. Multipath TCP (MPTCP), as an emerging transmission protocol, can fit multiple link bandwidths to improve data transmission performance and improve user experience quality. At the same time, due to the large-scale deployment and application of emerging technologies such as the Internet of Things and cloud computing, cyber attacks against MPTCP have gradually increased. More and more network security research studies point out that low-rate distributed denial of service (LDDoS) attacks are relatively popular and difficult to detect and are recognized as one of the most severe threats to network services. This article introduces six classic queue management algorithms: DropTail, RED, FRED, REM, BLUE, and FQ. In a multihomed network environment, we perform the performance evaluation of MPTCP under LDDoS attacks in terms of throughput, delay, and packet loss rate when using the six algorithms, respectively, by simulations. The results show that in an MPTCP-enabled multihomed network, different queue management algorithms have different throughput, delay, and packet loss rate performance when subjected to LDDoS attacks. Considering these three performance indicators comprehensively, the FRED algorithm has better performance. By adopting an effective active queue management (AQM) algorithm, the MPTCP transmission system can enhance its robustness capability, thus improving transmission performance. We suggest that when designing and improving the queue management algorithm, the antiattack performance of the algorithm should be considered: (1) it can adjust the traffic speed by optimizing the congestion control mechanism; (2) the fairness of different types of data streams sharing bandwidth is taken into consideration; and (3) it has the ability to adjust the parameters of the queue management algorithm in a timely and accurate manner.

scholarly journals Performance Analysis of Active Queue Management Algorithm Based on Reinforcement Learning

2020 ◽  
Vol 53 (5) ◽  
pp. 637-644
Author(s):  
Fuchun Jiang ◽  
Chenwei Feng ◽  
Chen Zhu ◽  
Yu Sun
Keyword(s):  
Reinforcement Learning ◽  
Network Performance ◽  
Task Force ◽  
Active Queue Management ◽  
Queue Management ◽  
Core Network ◽  
Optimal Control Strategy ◽  
Management Algorithm ◽  
Service Load ◽  
And Performance

In the information society, data explosion has led to more congestion in the core network, dampening the network performance. Random early detection (RED) is currently the standard algorithm for active queue management (AQM) recommended by the Internet Engineering Task Force (IETF). However, RED is particularly sensitive to both service load and algorithm parameters. The algorithm cannot fully utilize the bandwidth at a low service load, and might suffer a long delay at a high service load. This paper designs the reinforcement learning AQM (RLAQM), a simple and practical variant of RED, which controls the average queue length to the predictable value under various network loads, such that the queue size is no longer sensitive to the level of congestion. Q-learning was adopted to adjust the maximum discarding probability, and derive the optimal control strategy. Simulation results indicate that RLAQM can effectively overcome the deficiency of RED and achieve better congestion control; RLAQM improves the network stability and performance in complex environment; it is very easy to migrate from RED to RLAQM on Internet routers: the only operation is to adjust the discarding probability.

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.

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