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Distributed algorithm

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


Papers
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Proceedings ArticleDOI
01 Oct 2004
TL;DR: A new code partitioning algorithm is given to partition a BPEL program represented as a program dependence graph, with the goal of minimizing communication costs and maximizing the throughput of multiple concurrent instances of the input program.
Abstract: Distributed enterprise applications today are increasingly being built from services available over the web. A unit of functionality in this framework is a web service, a software application that exposes a set of "typed'' connections that can be accessed over the web using standard protocols. These units can then be composed into a composite web service. BPEL (Business Process Execution Language) is a high-level distributed programming language for creating composite web services. Although a BPEL program invokes services distributed over several servers, the orchestration of these services is typically under centralized control. Because performance and throughput are major concerns in enterprise applications, it is important to remove the inefficiencies introduced by the centralized control. In a distributed, or decentralized orchestration, the BPEL program is partitioned into independent sub-programs that interact with each other without any centralized control. Decentralization can increase parallelism and reduce the amount of network traffic required for an application. This paper presents a technique to partition a composite web service written as a single BPEL program into an equivalent set of decentralized processes. It gives a new code partitioning algorithm to partition a BPEL program represented as a program dependence graph, with the goal of minimizing communication costs and maximizing the throughput of multiple concurrent instances of the input program. In contrast, much of the past work on dependence-based partitioning and scheduling seeks to minimize the completion time of a single instance of a program running in isolation. The paper also gives a cost model to estimate the throughput of a given code partition.

203 citations

Proceedings ArticleDOI
09 Apr 1997
TL;DR: This work proves the DCUR's correctness by showing that it is always capable of constructing a loop-free delay-constrained path within finite time, if such a path exists.
Abstract: We study the NP-hard delay-constrained least-cost path problem, and propose a simple, distributed heuristic solution: the delay-constrained unicast routing (DCUR) algorithm. The DCUR requires limited network state information to be kept at each node: a cost vector and a delay vector. We prove the DCUR's correctness by showing that it is always capable of constructing a loop-free delay-constrained path within finite time, if such a path exists. The worst case message complexity of the DCUR is O(|V|/sup 3/) messages, where |V| is the number of nodes. However simulation results show that, on average, the DCUR requires much fewer messages. Therefore, the DCUR scales well to large networks. We also use simulation to compare the DCUR to the optimal algorithm, and to the least-delay path algorithm. Our results show that the DCUR's path costs are within 10% from those of the optimal solution.

203 citations

01 Jan 2004
TL;DR: It is argued that given the appropriate mechanisms, end-users can cooperate to arrive at an optimal resource allocation in spite of excess demand, and the design and early implementation of Bellagio, a distributed resource discov-based infrastructures is presented.
Abstract: We consider the problem of allocating combinations of heterogeneous, distributed resources among selfinterested parties. In particular, we consider this problem in the context of distributed computing infrastructures, where resources are shared among users from different administrative domains. Examples of such infrastructures include PlanetLab [15] and computational grids [7]. End-users derive utility from receiving a share of resources. When there is an excess demand for resources, it isn’t possible to completely satisfy all resource requests. Therefore, we argue that it is important for these infrastructures to allocate resources in a way that maximizes aggregate end-user utility. Such an allocation system is known as economically efficient. Because a user’s utility function for resources isn’t typically known a priori, determining an allocation policy to maximize utility is difficult in the presence of excess demand. As use of these infrastructures becomes more widespread, contention for resources will increase, and allocating resources in an economically efficient manner becomes more difficult. Figure 1 shows a snapshot of resource demand in PlanetLab. Due to the way resources are distributed in PlanetLab, the rise in contention decreases the portion of resources received by any individual user, thereby reducing the amount of useful work that can be completed in the system. We argue that given the appropriate mechanisms, end-users can cooperate to arrive at an optimal resource allocation in spite of excess demand. More specifically, by allowing users to express preferences for when and what resources are received, the system will be able to spread out some excess demand over time, and increase the efficiency of the system. To this end, we present the design and early implementation of Bellagio, a distributed resource discov-

203 citations

Journal ArticleDOI
He Chen1, Yonghui Li1, Yunxiang Jiang, Yuanye Ma1, Branka Vucetic1 
TL;DR: Simulation results show that the proposed game-theoretical approach can achieve a near-optimal network-wide performance on average, especially for the scenarios with relatively low and moderate interference.
Abstract: In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in relay interference channels, where multiple source-destination pairs communicate through their dedicated energy harvesting relays. Each relay needs to split its received signal from sources into two streams: one for information forwarding and the other for energy harvesting. We develop a distributed power splitting framework using game theory to derive a profile of power splitting ratios for all relays that can achieve a good network-wide performance. Specifically, non-cooperative games are respectively formulated for pure amplify-and-forward (AF) and decode-and-forward (DF) networks, in which each link is modeled as a strategic player who aims to maximize its own achievable rate. The existence and uniqueness for the Nash equilibriums (NEs) of the formulated games are analyzed and a distributed algorithm with provable convergence to achieve the NEs is also developed. Subsequently, the developed framework is extended to the more general network setting with mixed AF and DF relays. All the theoretical analyses are validated by extensive numerical results. Simulation results show that the proposed game-theoretical approach can achieve a near-optimal network-wide performance on average, especially for the scenarios with relatively low and moderate interference.

202 citations

Journal ArticleDOI
TL;DR: This paper overviews distributed approaches, all based on consensus +innovations, for three common energy management functions: state estimation, economic dispatch, and optimal power flow for the future electric power grid.
Abstract: This paper reviews signal processing research for applications in the future electric power grid, commonly referred to as smart grid. Generally, it is expected that the grid of the future would differ from the current system by the increased integration of distributed generation, distributed storage, demand response, power electronics, and communications and sensing technologies. The consequence is that the physical structure of the system becomes significantly more distributed. The existing centralized control structure is not suitable any more to operate such a highly distributed system. Hence, in this paper, we overview distributed approaches, all based on consensus ${+}$ innovations, for three common energy management functions: state estimation, economic dispatch, and optimal power flow. We survey the pertinent literature and summarize our work. Simulation results illustrate tradeoffs and the performance of consensus ${+}$ innovations for these three applications.

202 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202381
2022135
2021583
2020759
2019876
2018845