We propose a distributed data replenishment mechanism for some distributed peer-to-peer-based storage systems that automates the process of maintaining a sufficient level of data redundancy to ensure the availability of data in presence of peer departures and failures. The dynamics of peers entering and leaving the network are modeled as a stochastic process. A novel analytical time-backward technique is proposed to bound the expected time for a piece of data to remain in P2P systems. Both theoretical and simulation results are in agreement, indicating that the data replenishment via random linear network coding (RLNC) outperforms other popular strategies. Specifically, we show that the expected time for a piece of data to remain in a P2P system, the longer the better, is exponential in the number of peers used to store the data for the RLNC-based strategy, while they are quadratic for other strategies.

We investigate a class of randomized peer-to-peer (P2P) approach to Internet-wide distributed data storage systems that promises to reduce the coordination complexity and increases performance scalability. The core of these randomized P2P data storage systems is the data replenishment mechanism. The data replenishment automates the process of maintaining a sufficient level of data redundancy to ensure the availability of data in presence of peer departures and failures. The dynamics of peers entering and leaving the network is modeled as a stochastic process. A novel analytical time-backward technique is proposed to bound the expected time for a piece of data to remain in P2P systems. Both theoretical and simulation results are in agreement, indicating that a proposed data replenishment via random linear network coding (RLNC) outperforms other strategies that employ popular repetition and channel coding techniques. Specifically, we show that the expected time for a piece of data to remain in a P2P system, the longer the better, is exponential in the redundancy amount for the RLNC-based strategy, while they are quadratic for other strategies.

Delivering live media content over best-effort Internet is a challenging task due to a number of factors such as packet loss, delay, and bandwidth fluctuation. As a result, many approaches have been proposed ranging from auto repeat request-typed solutions, forward error correcting code to routing and scheduling protocols and network architecture design. Multiple path streaming is an important solution which exploits the path diversity to satisfy the high transmission bandwidth requirement of streaming applications. Recently, network coding has been proposed for efficient information dissemination over packet-switched networks. In this paper, we propose a network coding framework for multiple path streaming over the Internet. In particular, we use a number of random network coding schemes in order to both exploit the benefits of path diversity and prevent packet loss. Our framework works at the application layer using the UDP service and is independent of the current network infrastructure.

Historically, Back End of Line (BEOL) or interconnect resistance and capacitance have been viewed as parasitic components. They have now become key parameters with significant impact on circuit performance and signal integrity. This paper examines the types of BEOL variations and their impact on RC extraction. The importance of modeling systematic effects in RC extraction is discussed. The need for minimizing the computational error in RC extraction before incorporating random process variations is emphasized.