Performance comparison of active queue management algorithms

2012 ◽  
Author(s):  
Afan Ceco ◽  
Novica Nosovic ◽  
Kenan Bradic
Keyword(s):  
Active Queue Management ◽  
Performance Comparison ◽  
Queue Management ◽  
Management Algorithms

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 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.

Adaptive fuzzy sliding mode active queue management algorithms

2007 ◽  
Vol 35 (1-2) ◽  
pp. 21-42 ◽  
Author(s):  
Xinping Guan ◽  
Bo Yang ◽  
Bin Zhao ◽  
Gang Feng ◽  
Cailian Chen
Keyword(s):  
Sliding Mode ◽  
Active Queue Management ◽  
Queue Management ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy Sliding Mode ◽  
Fuzzy Sliding Mode ◽  
Management Algorithms
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