Variable bit rate (VBR) compressed video traffic, such as live video news, is expected to account for a large portion of traffic in future integrated networks. This real-time video traffic has strict delay and loss requirements, and exhibits burstiness over multiple time scales, thus imposing a challenge on network resource allocation and management. The renegotiated VBR (R-VBR) scheme, dynamically allocating resources to capture the burstiness of VBR traffic, substantially increases network utilization while satisfying any desired quality of service (QoS) requirements. This thesis focuses on the performance evaluation of R-VBR in the context of different R-VBR approaches. The renegotiated deterministic VBR (RED-VBR) scheme, proposed by Dr. H. Zhang et al., is thoroughly investigated in this research using a variety of real-world videos, with both high quality and low quality. A new Virtual-Queue-Based RED-VBR is then developed to reduce the implementation complexity of RED-VBR. Simulation results show that this approach obtains a comparable network performance as RED-VBR: relatively high network utilization and a very low drop rate. A Prediction-Based R-VBR based on a multiresolution learning neural network traffic predictor, developed by Dr. Y. Liang, is studied and the use of binary exponential backoff (BEB) algorithm is introduced to efficiently decrease the renegotiation frequency. Compared with RED-VBR, Prediction-Based R-VBR obtains significantly improved network utilization at a little expense of the drop rate. This work provides evaluations of the advantages and disadvantages of several R-VBR approaches, and thus provides a clearer big picture on the performance of the studied R-VBR approaches, which can be used as the basis to choose an appropriate R-VBR scheme to optimize network utilization while enabling QoS for the application tasks. / Master of Science

Though erasure codes are widely adopted in high fault tolerance storage systems, there exists a serious small-write problem. Many algorithms are proposed to improve small-write performance in RAID systems, without considering the network bandwidth usage. However, the network bandwidth is expensive in cloud systems. In this thesis, we proposed an ECL-based (E-MBR codes, Caching and Logging-based) technique to improve the small-write performance without using extra network bandwidth. In addition, the ECL-based technique also reduces the delayed parity update and data recovery latency compared with the competing algorithm.

No description available.

Multicast has long been considered an attractive service for the Internet for the provision of multiparty applications. For over a decade now multicast has been a proposed IETF standard. Though there is a strong industry push towards deploying multicast, there has been little multicast deployment by commercial Internet Service Providers (ISPs) and more importantly most end-users still lack multicast capabilities. Depending on the underlying network infrastructure, the ISP has several options of implementing his multicast capabilities. With significantly faster and more sophisticated protocols being designed and prototyped, it is expected that a whole new gamut of applications that are delay sensitive will come into being. However, the incentives to resolve the conflicting interests of the ISPs and the end-users have to be provided for successful implementation of these protocols. Thus we arrive at the following economic questions: What is the strategy that will enable the ISP recover his costs ? How can the end-user be made aware of the cost of his actions ? Naturally, the strategies of the ISP and the end-user depend on each other and form an economic game. The research problems addressed in this thesis are: A pricing model that is independent of the underlying transmission protocols is prefered. We have proposed such a pricing scheme for multicast independent of the underlying protocols, by introducing the concept of pricing points* These pricing points provide a range of prices that the users can expect during a particular time period and tune their usage accordingly. Our pricing scheme makes both the sender and receiver accountable. Our scheme also provides for catering to heterogeneous users and gives incentive for differential pricing. We explore a number of formulations of resource allocation problems arising in communication networks as optimization models. Optimization-based methods were only employed for unicast congestion control. We have extended this method for single rate multicast. We have also devised an optimization-based approach for multicast congestion control that finds an allocation rate to maximize the social welfare. Finally we also show that the session-splitting problem can also be cast as an optimization problem. The commonly used "max-min" fairness criteria suffers from serious limitations like discriminating sessions that traverse large number of links and poor network utilization. We provide an allocation scheme that reduces discrimination towards multicast sessions that traverse many links and also improves network utilization.