Random early detection gateways for congestion avoidance
IEEE/ACM Transactions on Networking (TON)
A control theoretic approach to active queue management
Computer Networks: The International Journal of Computer and Telecommunications Networking
Computer Networks and Systems: Queueing Theory and Performance Evaluation
Computer Networks and Systems: Queueing Theory and Performance Evaluation
Modelling Active Queue Management with Different Traffic Classes
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 02
Analysis of Queuing Networks with Blocking under Active Queue Management Scheme
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 2
A Discrete-time Queue Analytical Model based on Dynamic Random Early Drop
ITNG '07 Proceedings of the International Conference on Information Technology
A stochastic model for the throughput of non-persistent TCP flows
Performance Evaluation
System Content and Packet Delay in Discrete-Time Queues with Session-Based Arrivals
ITNG '08 Proceedings of the Fifth International Conference on Information Technology: New Generations
Performance evaluation for DRED discrete-time queueing network analytical model
Journal of Network and Computer Applications
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Design and analysis of multi-level active queue management mechanisms for emergency traffic
Computer Communications
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This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes (mQN) model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.