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 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 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 Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions

2019 ◽  
Vol 9 (1) ◽  
pp. 359 ◽  
Author(s):  
Okokpujie Kennedy ◽  
Emmanuel Chukwu ◽  
Olamilekan Shobayo ◽  
Etinosa Noma-Osaghae ◽  
Imhade Okokpujie ◽  
...  
Keyword(s):  
Wireless Network ◽  
Transmission Control Protocol ◽  
Active Queue Management ◽  
Propagation Delay ◽  
Queue Management ◽  
Link Capacity ◽  
Congestion Control Algorithm ◽  
Fixed Parameter ◽  
Self Tuning ◽  
Management Techniques

This paper demonstrates the robustness of active queue management techniques to varying load, link capacity and propagation delay in a wireless environment. The performances of four standard   controllers used in Transmission Control Protocol/Active Queue Management (TCP/AQM) systems were compared. The active queue management controllers were the Fixed-Parameter Proportional Integral (PI), Random Early Detection (RED), Self-Tuning Regulator (STR) and the Model Predictive Control (MPC). The robustness of the congestion control algorithm of each technique was documented by simulating the varying conditions using MATLAB® and Simulink® software. From the results obtained, the MPC controller gives the best result in terms of response time and controllability in a wireless network with varying link capacity and propagation delay. Thus, the MPC controller is the best bet when adaptive algorithms are to be employed in a wireless network environment. The MPC controller can also be recommended for heterogeneous networks where the network load cannot be estimated.

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 FA-LAR: an efficient flow aware based load adaptive routing scheme for optical communication networks

2018 ◽  
Vol 7 (2.27) ◽  
pp. 306
Author(s):  
S Madhu Mohan Swaminathan ◽  
N K. Sakthivel ◽  
S Subasree
Keyword(s):  
Communication Networks ◽  
Network Performance ◽  
Research Work ◽  
Network Models ◽  
Adaptive Routing ◽  
Experimental Results ◽  
Traffic Load ◽  
Queueing Delay ◽  
Flow Aggregation ◽  
Control Traffic

Routing Protocols have been proposed to enable the network to identify and suggest various routes to number of demanded flows. In the Flow-Aware Network Models, the routes are identified and selected with the help of Flow Tables or Flow Identifiers that proposed by Flow Aggregation Mechanism. That is, users can define a Flow Aggregation Model to suggest routes depend on their defined-demanded flows and this model effectively handles many flows, which helps core routers to profit aggregate routing. This is an efficient and effective approach to identify a best route to achieve required performance. It is noted from the literature survey that the Flow-Aware Multi-Topology Adaptive Routing (FAMTAR) was proposed for achieving higher Network performance through multipath solutions. This FAMTAR Model was implemented and studied thoroughly. From the experimental results, it was noticed that this model unable to i. detect and manage bulk flow, ii. Control Traffic Loss and iii. Maintain Deviation of Links Load against Traffic Load. To address the above mentioned issues, this research work is proposed an efficient Flow-Aware based Load Adaptive Routing (FA-LAR). This model is developed and implemented in ns3 and the simulation results are analysed carefully. From the experimental results, it is noticed that the prosed Model, FA-LAR is performing well as compared with the existing FAMTAR in terms of Queueing Delay, Throughput, Power Consumption (Energy Dissipation), and Load Deviation. It is also noticed that the proposed model unable to achieve higher Throughput for Low Load.  

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