Enhancing QoS provisioning and granularity in next generation internet

Next Generation IP technology has the potential to prevail, both in the access and in the core networks, as we are moving towards a multi-service, multimedia and high-speed networking environment. Many new applications, including the multimedia applications, have been developed and deployed, and demand Quality of Service (QoS) support from the Internet, in addition to the current best effort service. Therefore, QoS provisioning techniques in the Internet to guarantee some specific QoS parameters are more a requirement than a desire. Due to the large amount of data flows and bandwidth demand, as well as the various QoS requirements, scalability and fine granularity in QoS provisioning are required. In this dissertation, the end-to-end QoS provisioning mechanisms are mainly studied, in order to provide scalable services with fine granularity to the users, so that both users and network service providers can achieve more benefits from the QoS provisioned in the network. To provide the end-to-end QoS guarantee, single-node QoS provisioning schemes have to be deployed at each router, and therefore, in this dissertation, such schemes are studied prior to the study of the end-to-end QoS provisioning mechanisms. Specifically, the effective sharing of the output bandwidth among the large amount of data flows is studied, so that fairness in the bandwidth allocation among the flows can be achieved in a scalable fashion. A dual-rate grouping architecture is proposed in this dissertation, in which the granularity in rate allocation can be enhanced, while the scalability of the one-rate grouping architecture is still maintained. It is demonstrated that the dual-rate grouping architecture approximates the ideal per-flow based PFQ architecture better than the one-rate grouping architecture, and provides better immunity capability. On the end-to-end QoS provisioning, a new Endpoint Admission Control scheme for Diffserv networks, referred to as Explicit Endpoint Admission Control (EEAC), is proposed, in which the admission control decision is made by the end hosts based on the end-to-end performance of the network. A novel concept, namely the service vector, is introduced, by which an end host can choose different services at different routers along its data path. Thus, the proposed service provisioning paradigm decouples the end-to-end QoS provisioning from the service provisioning at each router, and the end-to-end QoS granularity in the Diffserv networks can be enhanced, while the implementation complexity of the Diffserv model is maintained. Furthermore, several aspects of the implementation of the EEAC and service vector paradigm, referred to as EEAC-SV, in the Diffserv architecture are also investigated. The performance analysis and simulation results demonstrate that the proposed EEAC-SV scheme, not only increases the benefit to the service users, but also enhances the benefit to the network service provider in terms of network resource utilization. The study also indicates that the proposed EEAC-SV scheme can provide a compatible and friendly networking environment to the conventional TCP flows, and the scheme can be deployed in the current Internet in an incremental and gradual fashion

Methods of Congestion Control for Adaptive Continuous Media

Since the first exchange of data between machines in different locations in early 1960s, computer networks have grown exponentially with millions of people now using the Internet. With this, there has also been a rapid increase in different kinds of services offered over the World Wide Web from simple e-mails to streaming video. It is generally accepted that the commonly used protocol suite TCP/IP alone is not adequate for a number of modern applications with high bandwidth and minimal delay requirements. Many technologies are emerging such as IPv6, Diffserv, Intserv etc, which aim to replace the onesize-fits-all approach of the current lPv4. There is a consensus that the networks will have to be capable of multi-service and will have to isolate different classes of traffic through bandwidth partitioning such that, for example, low priority best-effort traffic does not cause delay for high priority video traffic. However, this research identifies that even within a class there may be delays or losses due to congestion and the problem will require different solutions in different classes. The focus of this research is on the requirements of the adaptive continuous media class. These are traffic flows that require a good Quality of Service but are also able to adapt to the network conditions by accepting some degradation in quality. It is potentially the most flexible traffic class and therefore, one of the most useful types for an increasing number of applications. This thesis discusses the QoS requirements of adaptive continuous media and identifies an ideal feedback based control system that would be suitable for this class. A number of current methods of congestion control have been investigated and two methods that have been shown to be successful with data traffic have been evaluated to ascertain if they could be adapted for adaptive continuous media. A novel method of control based on percentile monitoring of the queue occupancy is then proposed and developed. Simulation results demonstrate that the percentile monitoring based method is more appropriate to this type of flow. The problem of congestion control at aggregating nodes of the network hierarchy, where thousands of adaptive flows may be aggregated to a single flow, is then considered. A unique method of pricing mean and variance is developed such that each individual flow is charged fairly for its contribution to the congestion

Pricing and Resource Allocation in Caching Services With Multiple Levels of Quality of Service

Network caches are the storage centers in the supply chain for content delivery—the digital equivalent of warehouses. Operated by access networks and other operators, they provide benefits to content publishers in the forms of bandwidth cost reduction, response time improvement, and handling of flash crowds. Yet, caching has not been fully embraced by publishers, because its use can interfere with site personalization strategies and/or collection of visitor information for business intelligence purposes. While recent work has focused on technological solutions to these issues, this paper provides the first study of the managerial issues related to the design and provisioning of incentive-compatible caching services. Starting with a single class of caching service, we find conditions under which the profit-maximizing cache operator should offer the service for free. This occurs when the access networks’ bandwidth costs are high and a large fraction of content publishers value personalization and business intelligence. Some publishers will still opt out of the service, i.e., cache bust, as observed in practice. We next derive the conditions under which the profit-maximizing cache operator should provision two vertically differentiated service classes, namely, premium and best effort. Interestingly, caching service differentiation is different from traditional vertical differentiation models, in that the premium and best-effort market segments do not abut. Thus, optimal prices for the two service classes can be set independently and cannibalization does not occur. It is possible for the cache operator to continue to offer the best-effort service for free while charging for the premium service. Furthermore, consumers are better off because more content is cached and delivered faster to them. Finally, we find that declining bandwidth costs will put negative pressure on cache operator profits, unless consumer adoption of broadband connectivity and the availability of multimedia content provide the necessary increase in traffic volume for the caches

Dynamic Resource Management in Clouds: A Probabilistic Approach

Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this work we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. We show that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by "buzz/flash crowd effects" that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networkingComment: IEICE Transactions on Communications (2012). arXiv admin note: substantial text overlap with arXiv:1209.515