Distributed algorithm

About: Distributed algorithm is a research topic. Over the lifetime, 20416 publications have been published within this topic receiving 548109 citations.

Quality-Aware Streaming and Scheduling for Device-to-Device Video Delivery

Abstract: On-demand video streaming is becoming a killer application for wireless networks. Recent information-theoretic results have shown that a combination of caching on the users’ devices and device-to-device (D2D) communications yields throughput scalability for very dense networks, which represent critical bottlenecks for conventional cellular and wireless local area network (WLAN) technologies. In this paper, we consider the implementation of such caching D2D systems where each device pre-caches a subset of video files from a library, and users requesting a file that is not already in their library obtain it from neighboring devices through D2D communication. We develop centralized and distributed algorithms for the delivery phase, encompassing a link scheduling and a streaming component. The centralized scheduling is based on the max-weighted independent set (MWIS) principle and uses message-passing to determine max-weight independent sets. The distributed scheduling is based on a variant of the FlashLinQ link scheduling algorithm, enhanced by introducing video-streaming specific weights. In both cases, the streaming component is based on a quality-aware stochastic optimization approach, reminiscent of current Dynamic Adaptive Streaming over HTTP (DASH) technology, for which users sequentially request video “chunks” by choosing adaptively their quality level. The streaming and the scheduling components are coupled by the length of the users’ request queues. Through extensive system simulation, the proposed approaches are shown to provide sizeable gains with respect to baseline schemes formed by the concatenation of off-the-shelf FlashLinQ with proportional fair link scheduling and DASH at the application layer.

Inside MASSIVE-3: flexible support for data consistency and world structuring

Abstract: MASSIVE-3 is our third generation of Collaborative Virtual Environment (CVE) system. This paper describes the goals, design and implementation of key aspects of the MASSIVE-3 system, and in particular its support for data consistency, and world structuring and interest management. MASSIVE-3 adopts a distributed database model, in which all changes to items in the database are represented by explicit events that are themselves visible to the system. Networking is logically multicast, but physically client-server (the reasons for this are explained). MASSIVE-3 makes application behaviours explicitly visible within the database in the form of “Behaviour” data items. MASSIVE-3 implements and extends work on consistency by the University of Reading. In particular, it adds an explicit “Update Request” data item, which allows the system to support a number of different consistency mechanisms within a single virtual world. World structuring in MASSIVE-3 extends the notion of “Locales” from the SPLINE system to include distinctions based on functional class, organisational scope and fidelity. It also allows flexible and general replication and rendering policies to be specified and used for interest management.

A Distributed Auction-Based Algorithm for the Nonconvex Economic Dispatch Problem

Abstract: This paper presents a distributed algorithm based on auction techniques and consensus protocols to solve the nonconvex economic dispatch problem. The optimization problem of the nonconvex economic dispatch includes several constraints such as valve-point loading effect, multiple fuel option, and prohibited operating zones. Each generating unit locally evaluates quantities used as bids in the auction mechanism. These units send their bids to their neighbors in a communication graph that supports the power system and which provides the required information flow. A consensus procedure is used to share the bids among the network agents and resolves the auction. As a result, the power distribution of generating units is updated and the generation cost is minimized. The effectiveness of this approach is demonstrated by simulations on standard test systems.

SEECH: Scalable Energy Efficient Clustering Hierarchy Protocol in Wireless Sensor Networks

Abstract: The energy efficiency is an important issue for employ distributed wireless sensor networks in smart space and extreme environments. The cluster-based communication protocols play a considerable role for energy saving in hierarchical wireless sensor networks. In most of traditional clustering algorithms, a cluster head (CH) simultaneously serves as a relay sensor node to transmit its cluster/other clusters data packet(s) to the data sink. As a result, each node would have CH role as many as relay role during network lifetime. In our view, this is inefficient from an energy efficiency perspective because in lots of cases, a node due to its position in the network comparatively is more proper to work as a CH and/a relay. This paper proposes a new distributed algorithm named scalable energy efficient clustering hierarchy (SEECH), which selects CHs and relays separately and based on nodes eligibilities. In this way, high and low degree nodes are, respectively, employed as CHs and relays. In only a few past researches, CHs and relays are different, but their goal was mainly mitigation of CHs energy burden which is intrinsically satisfied by the proposed mechanism. To consider uniformity of CHs to balance clusters, SEECH uses a new distance-based algorithm. Comparisons with LEACH and TCAC protocols show obvious better performance of SEECH in term of lifetime. To evaluate the scalability of SEECH strategy, simulations are conducted in three different network size scenarios.

On the Learning Behavior of Adaptive Networks—Part I: Transient Analysis

Abstract: This paper carries out a detailed transient analysis of the learning behavior of multiagent networks, and reveals interesting results about the learning abilities of distributed strategies. Among other results, the analysis reveals how combination policies influence the learning process of networked agents, and how these policies can steer the convergence point toward any of many possible Pareto optimal solutions. The results also establish that the learning process of an adaptive network undergoes three (rather than two) well-defined stages of evolution with distinctive convergence rates during the first two stages, while attaining a finite mean-square-error level in the last stage. The analysis reveals what aspects of the network topology influence performance directly and suggests design procedures that can optimize performance by adjusting the relevant topology parameters. Interestingly, it is further shown that, in the adaptation regime, each agent in a sparsely connected network is able to achieve the same performance level as that of a centralized stochastic-gradient strategy even for left-stochastic combination strategies. These results lead to a deeper understanding and useful insights on the convergence behavior of coupled distributed learners. The results also lead to effective design mechanisms to help diffuse information more thoroughly over networks.