Quality-of-service management in IP networks

Quality of Service (QoS) in Internet Protocol (IF) Networks has been the subject of active research over the past two decades. Integrated Services (IntServ) and Differentiated Services (DiffServ) QoS architectures have emerged as proposed standards for resource allocation in IF Networks. These two QoS architectures support the need for multiple traffic queuing systems to allow for resource partitioning for heterogeneous applications making use of the networks. There have been a number of specifications or proposals for the number of traffic queuing classes (Class of Service (CoS)) that will support integrated services in IF Networks, but none has provided verification in the form of analytical or empirical investigation to prove that its specification or proposal will be optimum. Despite the existence of the two standard QoS architectures and the large volume of research work that has been carried out on IF QoS, its deployment still remains elusive in the Internet. This is not unconnected with the complexities associated with some aspects of the standard QoS architectures. [Continues.

An adaptive active queue management algorithm in Internet

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Recent trends in IP/NGEO satellite communication systems: transport, routing, and mobility management concerns

科研費報告書収録論文(課題番号:17500030/研究代表者:加藤寧/インターネットと高親和性を有する次世代低軌道衛星ネットワークに関する基盤研究

QoS provisioning in multimedia streaming

Multimedia consists of voice, video, and data. Sample applications include video conferencing, video on demand, distance learning, distributed games, and movies on demand. Providing Quality of Service (QoS) for multimedia streaming has been a difficult and challenging problem. When multimedia traffic is transported over a network, video traffic, though usually compressed/encoded for bandwidth reduction, still consumes most of the bandwidth. In addition, compressed video streams typically exhibit highly variable bit rates as well as long range dependence properties, thus exacerbating the challenge in meeting the stringent QoS requirements of multimedia streaming with high network utilization. Dynamic bandwidth allocation in which video traffic prediction can play an important role is thus needed. Prediction of the variation of the I frame size using Least Mean Square (LMS) is first proposed. Owing to a smoother sequence, better prediction has been achieved as compared to the composite MPEG video traffic prediction scheme. One problem with this LMS algorithm is its slow convergence. In Variable Bit Rate (VBR) videos characterized by frequent scene changes, the LMS algorithm may result in an extended period of intractability, and thus may experience excessive cell loss during scene changes. A fast convergent non-linear predictor called Variable Step-size Algorithm (VSA) is subsequently proposed to overcome this drawback. The VSA algorithm not only incurs small prediction errors but more importantly achieves fast convergence. It tracks scene changes better than LMS. Bandwidth is then assigned based on the predicted I frame size which is usually the largest in a Group of Picture (GOP). Hence, the Cell Loss Ratio (CLR) can be kept small. By reserving bandwidth at least equal to the predicted one, only prediction errors need to be buffered. Since the prediction error was demonstrated to resemble white noise or exhibits at most short term memory, smaller buffers, less delay, and higher bandwidth utilization can be achieved. In order to further improve network bandwidth utilization, a QoS guaranteed on-line bandwidth allocation is proposed. This method allocates the bandwidth based on the predicted GOP and required QoS. Simulations and analytical results demonstrate that this scheme provides guaranteed delay and achieves higher bandwidth utilization. Network traffic is generally accepted to be self similar. Aggregating self similar traffic can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. Least Mean Kurtosis (LMK), which uses the negated kurtosis of the error signal as the cost function, is proposed to predict the self similar traffic. Simulation results show that the prediction performance is improved greatly as compared to the LMS algorithm. Thus, it can be used to effectively predict the real time network traffic. The Differentiated Service (DiffServ) model is a less complex and more scalable solution for providing QoS to IP as compared to the Integrated Service (IntServ) model. We propose to transport MPEG frames through various service classes of DiffServ according to the MPEG video characteristics. Performance analysis and simulation results show that our proposed approach can not only guarantee QoS but can also achieve high bandwidth utilization. As the end video quality is determined not only by the network QoS but also by the encoded video quality, we consider video quality from these two aspects and further propose to transport spatial scalable encoded videos over DiffServ. Performance analysis and simulation results show that this can provision QoS guarantees. The dropping policy we propose at the egress router can reduce the traffic load as well as the risk of congestion in other domains

Satellite Networks: Architectures, Applications, and Technologies

Since global satellite networks are moving to the forefront in enhancing the national and global information infrastructures due to communication satellites' unique networking characteristics, a workshop was organized to assess the progress made to date and chart the future. This workshop provided the forum to assess the current state-of-the-art, identify key issues, and highlight the emerging trends in the next-generation architectures, data protocol development, communication interoperability, and applications. Presentations on overview, state-of-the-art in research, development, deployment and applications and future trends on satellite networks are assembled

Improved learning automata applied to routing in multi-service networks

Multi-service communications networks are generally designed, provisioned and configured, based on source-destination user demands expected to occur over a recurring time period. However due to network users' actions being non-deterministic, actual user demands will vary from those expected, potentially causing some network resources to be under- provisioned, with others possibly over-provisioned. As actual user demands vary over the recurring time period from those expected, so the status of the various shared network resources may also vary. This high degree of uncertainty necessitates using adaptive resource allocation mechanisms to share the finite network resources more efficiently so that more of actual user demands may be accommodated onto the network. The overhead for these adaptive resource allocation mechanisms must be low in order to scale for use in large networks carrying many source-destination user demands. This thesis examines the use of stochastic learning automata for the adaptive routing problem (these being adaptive, distributed and simple in implementation and operation) and seeks to improve their weakness of slow convergence whilst maintaining their strength of subsequent near optimal performance. Firstly, current reinforcement algorithms (the part causing the automaton to learn) are examined for applicability, and contrary to the literature the discretised schemes are found in general to be unsuitable. Two algorithms are chosen (one with fast convergence, the other with good subsequent performance) and are improved through automatically adapting the learning rates and automatically switching between the two algorithms. Both novel methods use local entropy of action probabilities for determining convergence state. However when the convergence speed and blocking probability is compared to a bandwidth-based dynamic link-state shortest-path algorithm, the latter is found to be superior. A novel re-application of learning automata to the routing problem is therefore proposed: using link utilisation levels instead of call acceptance or packet delay. Learning automata now return a lower blocking probability than the dynamic shortest-path based scheme under realistic loading levels, but still suffer from a significant number of convergence iterations. Therefore the final improvement is to combine both learning automata and shortest-path concepts to form a hybrid algorithm. The resulting blocking probability of this novel routing algorithm is superior to either algorithm, even when using trend user demands