Browsing by Subject "Multicast"
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Item B-RPM: An Efficient One-to-Many Communication Framework for On-Chip Networks(2012-10-19) Shaukat, NomanThe prevalence of multicore architectures has accentuated the need for scalable on-chip communication media. Various parallel applications and programming paradigms use a mix of unicast (one-to-one) and multicast (one-to-many) to maintain data coherence and consistency. Providing efficient support for these communication patterns becomes a critical design point for on-chip networks (OCN). High performance on-chip networks design advocates balanced traffic across the whole network, which makes adaptive routing appealing. Adaptive routing explores the path diversity of the network, increases throughput, and reduces network latency compared with oblivious routing. In this work, we propose an adaptive multicast routing, Balanced Recursive Partitioning Multicast (B-RPM), to achieve balanced one-to-many on-chip communication. The algorithm derives its functionality from previously proposed algorithm Recursive Partitioning Multicast (RPM). Unlike RPM which uses fixed set of directional priorities and position of destination nodes, B-RPM replicates packet based on the local congestion information and position of destination nodes with respect to current node. B-RPM employs a new deadlock avoidance technique Dynamically Sized Virtual Networks (DSVN). Built upon the traditional virtual networks, DSVN dynamically allocates the network resources to different VNs according to the run-time traffic status, which delivers better resources utilization. We also propose a new scheme for representing multiple destinations in packet head. The scheme works simply by differentiating multicast and unicast packets. The algorithm combined with dynamically sized virtual networks enables us to improve network performance at high load on average by 20% (up to 50%) and saturation throughput of network on average by 10% (up to 18%) over the most recent multicast algorithm. Also the new header representation scheme enables us to save 24% of dynamic link power.Item Enabling information-centric networking : architecture, protocols, and applications(2010-08) Cho, Tae Won, 1978-; Zhang, Yin, doctor of computer science; Gouda, Mohamed; Mooney, Raymond; Ramakrishnan, K.K.; Qiu, LiliAs the Internet is becoming information-centric, network services increasingly demand scalable and efficient communication of information between a multitude of information producers and large groups of interested information consumers. Such information-centric services are growing rapidly in use and deployment. Examples of deployed services that are information-centric include: IPTV, MMORPG, VoD, video conferencing, file sharing, software updates, RSS dissemination, online markets, and grid computing. To effectively support future information-centric services, the network infrastructure for multi-point communication has to address a number of significant challenges: (i) how to understand massive information-centric groups in a scalable manner, (ii) how to analyze and predict the evolution of those groups in an accurate and efficient way, and (iii) how to disseminate content from information producers to a vast number of groups with potentially long-lived membership and highly diverse, dynamic group activity levels? This dissertation proposes novel architecture and protocols that effectively address the above challenges in supporting multi-point communication for future information-centric network services. In doing so, we make the following three major contributions: (1) We develop a novel technique called Proximity Embedding (PE) that can approximate a family of path-ensembled based proximity measures for information-centric groups. We develop Clustered Spectral Graph Embedding (SCGE) that captures the essential structure of large graphs in a highly efficient and scalable manner. Our techniques help to explain the proximity (closeness) of users in information-centric groups, and can be applied to a variety of analysis tasks of complex network structures. (2) Based on SCGE, we develop new supervision based link prediction techniques called Clustered Spectral Learning and Clustered Polynomial Learning that enable us to predict the evolution of massive and complex network structures in an accurate and efficient way. By exploiting supervised information from past snapshots of network structures, our methods yield up to 20% improvement in link prediction accuracy when compared to existing state-of-the-art methods. (3) Finally, we develop a novel multicast infrastructure called Multicast with Adaptive Dual-state (MAD). MAD supports large number of group and group membership, and efficient content dissemination in a presence of dynamic group activity. We demonstrate the effectiveness of our approach in extensive simulation, analysis, and emulation through the real system implementation.Item Improvements in distribution of meteorological data using application layer multicast(Texas A&M University, 2007-04-25) Shah, Saurin BipinThe Unidata Program Center is an organization working with the University Center for Atmospheric Research (UCAR), in Colorado. It provides a broad variety of meteorological data, which is used by researchers in many real-world applications. This data is obtained from observation stations and distributed to various universities worldwide, using Unidata??????s own Internet Data Distribution (IDD) system, and software called the Local Data Manager (LDM). The existing solution for data distribution has many limitations, like high end-toend latency of data delivery, increased bandwidth usage at some nodes, poor scalability for future needs and manual intervention for adjusting to changes or faults in the network topology. Since the data is used in so many applications, the impact of these limitations is often substantial. This thesis removes these limitations by suggesting improvements in the IDD system and the LDM. We present new algorithms for constructing an application-layer data distribution network. This distribution network will form the basis of the improved LDM and the IDD system, and will remove most of the limitations given above. Finally, we perform simulations and show that our algorithms achieve better average end-to-end latency as compared to that of the existing solution. We also compare the performance of our algorithms with a randomized solution. We find that for smaller topologies (where the number of nodes in the system are less than 38) the randomized solution constructs efficient distribution networks. However, if the number of nodes in the system increases (more than 38), our solution constructs efficient distribution networks than the randomized solution. We also evaluate the performance of our algorithms as the number of nodes in the system increases and as the number of faults in the system increases. We find that even if the number of faults in the system increases, the average end-to-end latency decreases, thus showing that the distribution topology does not become inefficient.Item An information theoretic approach to structured high-dimensional problems(2013-12) Das, Abhik Kumar; Vishwanath, SriramA majority of the data transmitted and processed today has an inherent structured high-dimensional nature, either because of the process of encoding using high-dimensional codebooks for providing a systematic structure, or dependency of the data on a large number of agents or variables. As a result, many problem setups associated with transmission and processing of data have a structured high-dimensional aspect to them. This dissertation takes a look at two such problems, namely, communication over networks using network coding, and learning the structure of graphical representations like Markov networks using observed data, from an information-theoretic perspective. Such an approach yields intuition about good coding architectures as well as the limitations imposed by the high-dimensional framework. Th e dissertation studies the problem of network coding for networks having multiple transmission sessions, i.e., multiple users communicating with each other at the same time. The connection between such networks and the information-theoretic interference channel is examined, and the concept of interference alignment, derived from interference channel literature, is coupled with linear network coding to develop novel coding schemes off ering good guarantees on achievable throughput. In particular, two setups are analyzed – the first where each user requires data from only one user (multiple unicasts), and the second where each user requires data from potentially multiple users (multiple multicasts). It is demonstrated that one can achieve a rate equalling a signi ficant fraction of the maximal rate for each transmission session, provided certain constraints on the network topology are satisfi ed. Th e dissertation also analyzes the problem of learning the structure of Markov networks from observed samples – the learning problem is interpreted as a channel coding problem and its achievability and converse aspects are examined. A rate-distortion theoretic approach is taken for the converse aspect, and information-theoretic lower bounds on the number of samples, required for any algorithm to learn the Markov graph up to a pre-speci fied edit distance, are derived for ensembles of discrete and Gaussian Markov networks based on degree-bounded graphs. The problem of accurately learning the structure of discrete Markov networks, based on power-law graphs generated from the con figuration model, is also studied. The eff ect of power-law exponent value on the hardness of the learning problem is deduced from the converse aspect – it is shown that discrete Markov networks on power-law graphs with smaller exponent values require more number of samples to ensure accurate recovery of their underlying graphs for any learning algorithm. For the achievability aspect, an effi cient learning algorithm is designed for accurately reconstructing the structure of Ising model based on power-law graphs from the con figuration model; it is demonstrated that optimal number of samples su ffices for recovering the exact graph under certain constraints on the Ising model potential values.