scholarly journals An Active Queue Management Algorithm for Reducing Packet Loss Rate

2009 ◽  
Vol 14 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Babek Abbasov ◽  
Serdar Korukoğlu
Keyword(s):  
Packet Loss ◽  
Loss Rate ◽  
Active Queue Management ◽  
Packet Loss Rate ◽  
Queue Management ◽  
Management Algorithm

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.

An Improved Active Queue Management Algorithm Based on Nonlinear Smoothing

2011 ◽  
Vol 295-297 ◽  
pp. 1823-1828 ◽  
Author(s):  
Jing Jun Zhang ◽  
Wen Long Xu ◽  
Li Guo Wang
Keyword(s):  
Packet Loss ◽  
Loss Rate ◽  
Rate Function ◽  
Packet Loss Rate ◽  
Random Early Detection ◽  
Management Algorithm ◽  
Nonlinear Smoothing ◽  
The Stability ◽  
Nonlinear Smooth ◽  
Maximum Threshold

According to the limitations of calculation of the original random early detection (RED) algorithm in linear packet loss rate. This paper proposes an improved algorithm which imposes nonlinear smooth for packet loss rate function of RED algorithm. The speed of growth of packet loss rate is relatively slow near the minimum threshold, while near the maximum threshold the speed of growth of packet loss rate is relatively faster. In this case, using the trend of the average queue length to dynamically adjust the parameters of the RED algorithm, it reduces the dependence on the parameters of the RED algorithm and enhances the stability of the algorithm. NS simulation shows that this algorithm has been significantly improved for packet loss rate, throughput and other performance.

scholarly journals The Yellow Active Queue Management Algorithm in ICN Routers Based on the Monitoring of Bandwidth Competition

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 806
Author(s):  
Li Zeng ◽  
Hong Ni ◽  
Rui Han
Keyword(s):  
Packet Loss ◽  
Service Providers ◽  
Exclusion Process ◽  
Active Queue Management ◽  
Existence Condition ◽  
Queue Management ◽  
Limited Bandwidth ◽  
Management Algorithm ◽  
Asymmetric Simple Exclusion

Deploying the active queue management (AQM) algorithm on a router is an effective way to avoid packet loss caused by congestion. In an information-centric network (ICN), routers not only play a role of packets forwarding but are also content service providers. Congestion in ICN routers can be further summarized as the competition between the external forwarding traffic and the internal cache response traffic for limited bandwidth resources. This indicates that the traditional AQM needs to be redesigned to adapt to ICN. In this paper, we first demonstrated mathematically that allocating more bandwidth for the upstream forwarding flow could improve the quality of service (QoS) of the whole network. Secondly, we propose a novel AQM algorithm, YELLOW, which predicts the bandwidth competition event and adjusts the input rate of request and the marking probability adaptively. Afterwards, we model YELLOW through the totally asymmetric simple exclusion process (TASEP) and deduce the approximate solution of the existence condition for each stationary phase. Finally, we evaluated the performance of YELLOW by NS-3 simulator, and verified the accuracy of modeling results by Monte Carlo. The simulation results showed that the queue of YELLOW could converge to the expected value, and the significant gains of the router with low packet loss rate, robustness and high throughput.

Packet loss rate monitoring model of IOT based on differential evolution algorithm

2021 ◽  
pp. 1-12
Author(s):  
Yinghua Feng ◽  
Wei Yang
Keyword(s):  
Internet Of Things ◽  
Differential Evolution ◽  
Packet Loss ◽  
Loss Rate ◽  
Differential Evolution Algorithm ◽  
Packet Loss Rate ◽  
Data Space ◽  
Monitoring Model ◽  
Evolution Algorithm ◽  
The Internet Of Things

In order to overcome the problems of high energy consumption and low execution efficiency of traditional Internet of things (IOT) packet loss rate monitoring model, a new packet loss rate monitoring model based on differential evolution algorithm is proposed. The similarity between each data point in the data space of the Internet of things is set as the data gravity. On the basis of the data gravity, combined with the law of gravity in the data space, the gravity of different data is calculated. At the same time, the size of the data gravity is compared, and the data are classified. Through the classification results, the packet loss rate monitoring model of the Internet of things is established. Differential evolution algorithm is used to solve the model to obtain the best monitoring scheme to ensure the security of network data transmission. The experimental results show that the proposed model can effectively reduce the data acquisition overhead and energy consumption, and improve the execution efficiency of the model. The maximum monitoring efficiency is 99.74%.

scholarly journals Sensing Coverage Algorithm of Sparse Mobile Sensor Node with Trade-Off between Packet Loss Rate and Transmission Delay

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Kehua Zhao ◽  
Yourong Chen ◽  
Siyi Lu ◽  
Banteng Liu ◽  
Tiaojuan Ren ◽  
...  
Keyword(s):  
Packet Loss ◽  
Data Transmission ◽  
Loss Rate ◽  
Packet Loss Rate ◽  
Transmission Delay ◽  
Sensor Nodes ◽  
Mobile Sensor ◽  
Sensing Coverage ◽  
Mobile Sensor Node ◽  
Monitoring Area

To solve the problem of sensing coverage of sparse wireless sensor networks, the movement of sensor nodes is considered and a sensing coverage algorithm of sparse mobile sensor node with trade-off between packet loss rate and transmission delay (SCA_SM) is proposed. Firstly, SCA_SM divides the monitoring area into several grids of same size and establishes a path planning model of multisensor nodes’ movement. Secondly, the social foraging behavior of Escherichia coli in bacterial foraging is used. A fitness function formula of sensor nodes’ moving paths is proposed. The optimal moving paths of all mobile sensor nodes which can cover the entire monitoring area are obtained through the operations of chemotaxis, replication, and migration. The simulation results show that SCA_SM can fully cover the monitoring area and reduce the packet loss rate and data transmission delay in the process of data transmission. Under certain conditions, SCA_SM is better than RAND_D, HILBERT, and TCM.

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