'Distributed algorithm' diaporamas de présentation

DISTRIBUTED COMPUTING

DISTRIBUTED COMPUTING. Sunita Mahajan , Principal, Institute of Computer Science, MET League of Colleges, Mumbai Seema Shah , Principal, Vidyalankar Institute of Technology, Mumbai University. Chapter - 5 Synchronization. Topics. Introduction Clock synchronization Logical clocks

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Mutual exclusion

Mutual exclusion. Concurrent access of processes to a shared resource or data is executed in mutually exclusive manner Distributed mutual exclusion can be classified into two different categories. Token based solutions Permission based approach. Token based approach.

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Mutual exclusion

Mutual exclusion. Concurrent access of processes to a shared resource or data is executed in mutually exclusive manner Distributed mutual exclusion can be classified into two different categories. Token based solutions Permission based approach. Token based approach.

773 views • 62 slides

Cyber-Enabled Smart Distribution Systems and Micro Grids

Cyber-Enabled Smart Distribution Systems and Micro Grids. Bruce McMillin Department of Computer Science Missouri University of Science and Technology (Formerly the University of Missouri-Rolla) Rolla, MO 65409-0350. Introduction: CPS.

667 views • 48 slides

Mechanism Design for Interdomain Routing

Mechanism Design for Interdomain Routing. Rahul Sami. Joint work with Joan Feigenbaum, David Karger, Vahab Mirrokni, Christos Papadimitriou, and Scott Shenker. UUNET. UMich. Comcast. Qwest. Interdomain Routing. Finding routes between autonomous systems (ASes)

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15-440 Distributed Systems

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Constructing K -Connected M -Dominating Sets in Wireless Sensor Networks

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Synchronization

Synchronization. Chapter 5. Clock Synchronization. When each machine has its own clock, an event that occurred after another event may nevertheless be assigned an earlier time. Physical Clocks (1). Computation of the mean solar day. Physical Clocks (2).

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Maximization of lifetime in wireless sensor network using optimal routing

Maximization of lifetime in wireless sensor network using optimal routing. YongJin Kwon yjkwon@comis.kaist.ac.kr KyungSub Shin ksshin@comis.kaist.ac.kr. 01 Introduction 02 System Model 03 Problem formulation 04 Algorithm or analysis and results 05 Big Picture

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Distributed Algorithms for Guiding Navigation across a Sensor Network

Distributed Algorithms for Guiding Navigation across a Sensor Network. Qun Li, Michael De Rosa, and Daniela Rus Mobicom 2003. Outline. Introduction A distributed algorithm for guiding the navigation of a user Implementation Issues Experiments Conclusion. Introduction.

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Computer Networks

Computer Networks . Data link layer. Design issues Point-to-point links Local area Networks Data Link layer Switching. Multiple LANs? From 802.x to 802.y Transparent bridges Spanning Tree bridges Remote bridges Interconnections <> layers Virtual LANs. Overview. Multiple LANs?.

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Decomposition-based Constraint Optimization and Distributed Execution

Decomposition-based Constraint Optimization and Distributed Execution. John Stedl, Tsoline Mikaelian, Martin Sachenbacher September 2003. Distributed Execution. What is Distributed Execution? Each agent maintains a subset of the plan

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Achieving End-to-End Fairness in Wireless Networks

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Mutual Exclusion

Mutual Exclusion. Critical Section Problem. acquire(c) CriticalSection c release(c) Critical Section correctness Mutual Exclusion: safety (ME1) Deadlock-Freedom: progress (ME2a) Starvation-Freedom: fairness (ME2b) [Happens-Before Ordering (ME3)]. Algorithms. Centralized algorithm

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Spatio-temporal Load Curve Data Cleansing and Imputation via Sparsity and Low Rank

Spatio-temporal Load Curve Data Cleansing and Imputation via Sparsity and Low Rank. Gonzalo Mateos and Georgios B. Giannakis Dept. of ECE and Digital Technology Center University of Minnesota. Workshop on Architectures and Models for the Smart Grid. November 5, 2012. 1. Context.

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Spatio-temporal Load Curve Data Cleansing and Imputation via Sparsity and Low Rank

Spatio-temporal Load Curve Data Cleansing and Imputation via Sparsity and Low Rank. Gonzalo Mateos and Georgios B. Giannakis Dept. of ECE and Digital Technology Center University of Minnesota. Workshop on Architectures and Models for the Smart Grid. November 5, 2012. 1. Context.

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Unsupervised Scoring for Scalable Internet-based Collaborative Teleoperation

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Swarm School: Distributed Averaging

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Distributed Agreement Algorithms

Distributed Agreement Algorithms. Final MURI Review Meeting John N. Tsitsiklis December 2, 2005. The Problem. Each sensor has a number x i They wish to reach agreement on a common value: Some value in the range [Min x i , Max x i ], or The average of their values

228 views • 9 slides