Random early detection gateways for congestion avoidance
IEEE/ACM Transactions on Networking (TON)
Dynamics of IP traffic: a study of the role of variability and the impact of control
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
A case for relative differentiated services and the proportional differentiation model
IEEE Network: The Magazine of Global Internetworking
Rate allocation and buffer management for differentiated services
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Towards a new internet architecture
Scaled time priority: an efficient approximation to waiting time priority
Computer Networks: The International Journal of Computer and Telecommunications Networking
A simple FIFO-based scheme for differentiated loss guarantees
Computer Networks: The International Journal of Computer and Telecommunications Networking
Enhancing class-based service architectures with adaptive rate allocation and dropping mechanisms
IEEE/ACM Transactions on Networking (TON)
Achieving proportional loss rate differentiation in a wireless network with a multi-state link
Computer Communications
Achieving Proportional Delay and Loss Differentiation in a Wireless Network with a Multi-state Link
Information Networking. Towards Ubiquitous Networking and Services
ISWPC'09 Proceedings of the 4th international conference on Wireless pervasive computing
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Recent extensions to the Internet architecture allow assignment of different levels of drop precedence to IP packets. This paper examines differentiation predictability and implementation complexity in creation of proportional lossrate (PLR) differentiation between drop precedence levels. PLR differentiation means that fixed loss-rate ratios between different traffic aggregates are provided independent of traffic loads. To provide such differentiation, running estimates of loss-rates can be used as feedback to keep loss-rate ratios fixed at varying traffic loads. In this paper, we define a loss-rate estimator based on average drop distances (ADDs). The ADD estimator is compared with an estimator that uses a loss history table (LHT) to calculate loss-rates. We show, through simulations, that the ADD estimator gives more predictable PLR differentiation than the LHT estimator. In addition, we show that a PLR dropper using the ADD estimator can be implemented efficiently.