Additive increase rate accelerator

Abstract. We propose AIRA, an Additive Increase Rate Accelerator. AIRA extends AIMD functionality towards adaptive increase rates, depending on the level of network contention and bandwidth availability. In this context, acceleration grows when resource availability is detected by goodput/throughput measurements and slows down when increased throughput does not translate into increased goodput as well. Thus, the gap between throughput and goodput determines the behavior of the rate accelerator. We study the properties of the extended model and propose, based on analysis and simulation, appropriate rate decrease and increase rules. Furthermore, we study conditional rules to guarantee operational success even in the presence of symptomatic, extra-ordinary events. We show that analytical rules can be derived for accelerating, either positively or negatively, the increase rate of AIMD in accordance with network dynamics. Indeed, we find that the "blind", fixed Additive Increase rule can become an obstacle for the performance of TCP, especially when contention increases. Instead, sophisticated, contention-aware additive increase rates may preserve system stability and reduce retransmission effort, without reducing the goodput performance of TCP

TCP-friendly SIMD Congestion Control and Its Convergence Behavior

The increased diversity of Internet application requirements has spurred recent interests in flexible congestion control mechanisms. Window-based congestion control schemes use increase rules to probe available bandwidth, and decrease rules to back off when congestion is detected. The control rules are parameterized so as to ensure that the resulting protocol is TCP-friendly in terms of the relationship between throughput and packet loss rate. In this paper, we propose a novel window-based congestion control algorithm called SIMD (Square-Increase/Multiplicative-Decrease). Contrary to previous memory-less controls, SIMD utilizes history information in its control rules. It uses multiplicative decrease but the increase in window size is in proportion to the square of the time elapsed since the detection of the last loss event. Thus, SIMD can efficiently probe available bandwidth. Nevertheless, SIMD is TCP-friendly as well as TCP-compatible through RED routers. Furthermore, SIMD has much better convergence behavior than TCP-friendly AIMD and binomial algorithms proposed recently

A study of the coexistence of heterogeneous flows on data networks.

Tam Sai-Wah.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves [103]-104) and index.Abstracts in English and Chinese.Abstract --- p.x摘要 --- p.xiAbbreviations --- p.xiiSymbols --- p.xivChapter Part I --- BackgroundChapter 1 --- Background on coexistence --- p.2Chapter 1.1 --- Data network --- p.2Chapter 1.1.1 --- Telephone network vs. data network --- p.2Chapter 1.1.2 --- Bandwidth in networks --- p.3Chapter 1.2 --- Taxonomy of flows --- p.4Chapter 1.3 --- Effect of heterogeneity and proposed solution --- p.4Chapter 1.3.1 --- Cause and effect of heterogeneity --- p.4Chapter 1.3.2 --- TCP-friendly congestion control as a solution --- p.5Chapter 1.3.3 --- Distributed admission control as a solution --- p.6Chapter 1.3.4 --- Evaluation methodology and organisation of this thesis --- p.6Chapter 2 --- Model of Heterogeneous Flows --- p.8Chapter 2.1 --- The network --- p.8Chapter 2.2 --- Elastic flows --- p.8Chapter 2.3 --- Inelastic flows --- p.10Chapter 2.4 --- Stochastic Flows --- p.11Chapter 2.5 --- Controls --- p.12Chapter 2.5.1 --- Congestion control for elastic flows --- p.12Chapter 2.5.2 --- No control for inelastic flows --- p.13Chapter 2.5.3 --- Congestion control for inelastic flows --- p.14Chapter 2.5.4 --- Admission control for inelastic flows --- p.15Chapter 2.5.5 --- Admission control for inelastic flows with continuous assurance --- p.16Chapter 2.6 --- Markov chain model of control schemes --- p.17Chapter 2.6.1 --- Normalisation --- p.17Chapter 2.6.2 --- Control schemes and Markov chains --- p.18Chapter Part II --- EvaluationChapter 3 --- Stability of network under different controls --- p.29Chapter 3.1 --- Stability of queues --- p.29Chapter 3.2 --- Stability of the Markov chain models --- p.30Chapter 3.2.1 --- Observation of stability from simulation --- p.30Chapter 3.3 --- Informal discussion of stability --- p.33Chapter 4 --- Bandwidth allocation --- p.35Chapter 4.1 --- Aggregated bandwidth --- p.35Chapter 4.2 --- Bandwidth per flow --- p.37Chapter 5 --- Evaluation based on utility functions --- p.40Chapter 5.1 --- Properties of utility function --- p.40Chapter 5.1.1 --- Utility for elastic flows --- p.40Chapter 5.1.2 --- Utility for inelastic flows --- p.41Chapter 5.1.3 --- Utility throughput --- p.41Chapter 5.1.4 --- Choice of utility function --- p.43Chapter 5.2 --- Degree of elasticity --- p.45Chapter 5.3 --- Homogeneous environment --- p.46Chapter 5.4 --- Heterogeneous environment --- p.49Chapter 5.4.1 --- Comparison for different offered load --- p.50Chapter 5.4.2 --- Effect of scaling --- p.52Chapter 5.4.3 --- Sensitivity to α and ε --- p.57Chapter 6 --- Blocking probability --- p.62Chapter 6.1 --- Formulating admission behaviour into PCDSDE --- p.62Chapter 6.2 --- Evaluation of the blocking probability --- p.64Chapter 6.3 --- Verification by simulation --- p.66Chapter 6.3.1 --- Comparison for different offered load --- p.66Chapter 6.3.2 --- Effect of scaling --- p.68Chapter 6.3.3 --- Sensitivity to α and ε --- p.68Chapter 7 --- Population --- p.74Chapter 7.1 --- Mean number of inelastic flows --- p.74Chapter 7.2 --- Mean number of elastic flows --- p.75Chapter 7.2.1 --- Elastic population after scaling --- p.79Chapter 7.2.2 --- Effect of aggressiveness --- p.79Chapter 7.2.3 --- Effect of α --- p.82Chapter Part III --- ConclusionChapter 8 --- Conclusion --- p.85Chapter 8.1 --- Summary --- p.85Chapter 8.2 --- Implication --- p.87Chapter 8.3 --- Future Work --- p.88AppendicesChapter A --- Glossary --- p.91Chapter B --- Introduction to Poisson counter driven stochastic differential equations --- p.97Chapter C --- Simulation --- p.101References --- p.103Index --- p.10

Sync & Sense Enabled Adaptive Packetization VoIP

The quality and reliability problem of VoIP comes from the fact that VoIP relies on the network to transport the voice packets. The inherent problem of VoIP is that there is a mismatch between VoIP and the network. Namely, VoIP has a strict requirement of bandwidth, delay, and loss, but the network (particularly best-effort service networks) cannot guarantee such a requirement. A solution to deal with this problem is to enhance VoIP with an adaptive-rate control, called adaptive-rate VoIP. Adaptive-rate VoIP has the ability to detect the state of the network and adjust the transmission accordingly. Therefore, it gives VoIP the intelligence to optimize its performance, and making it resilient and robust to the service offered by the network. The objective of this dissertation is to develop an adaptive-rate VoIP system. We take a comprehensive approach in the study and development. Adaptive-rate VoIP is generally composed of three components: rate adaptation, network state detection, and adaptive-rate control. In the rate adaptation component, we study optimizing packetization, which can be used as an alternative means for rate adaptation. An advantage is that rate adaptation is independent of the speech coder. With this method, an adaptive-rate VoIP can be based on any constant bitrate speech coder. The study shows that the VoIP performance is primarily affected by three factors: packetization, network load, and significance of VoIP traffic; and, optimizing packetization allows us to ensure the highest possible performance. In the network state detection component, we propose a novel measurement methodology called Sync & Sense of periodic stream. Sync & Sense is unique in that it can virtually synchronize the transmission and reception timing of the VoIP session without requiring a synchronized clock. The simulation result shows that Sync & Sense can accurately measure one-way network delay. Other benefits of Sync & Sense include the ability to estimate the available network bandwidth and the full spectrum of the delays of the VoIP session. In the adaptive-rate control component, we consider the design choices and develop an adaptive-rate control that makes use of the first two components. The integration of the three components is a novel and unique adaptive-rate VoIP called Sync & Sense Enabled Adaptive Packetization VoIP. The simulation result shows that our adaptive VoIP can optimize the performance under any given network condition, and deliver a better performance than traditional VoIP. The simulation result also demonstrates that our adaptive VoIP possesses the desirable properties, which include fast response to network condition, aggressiveness to compete for the needed share of bandwidth, TCP-friendliness, and fair bandwidth allocation