A O (log n ) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems. Volker Turau. November 6th, 2018

Size: px
Start display at page:

Download "A O (log n ) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems. Volker Turau. November 6th, 2018"

Transcription

1 A O (log n ) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems Volker Turau 20th Int. Symposium on Stabilization, Safety, and Security of Distributed Systems November 6th, 2018 Institute of Telematics Hamburg University of Technology TUHH

2 Publish/Subscribe 1

3 Publish/Subscribe Publish/Subscribe Paradigm Loosely coupled distributed information dissemination middleware Subscribers declare interest in topics by subscribing to topic Highly scalable Data is distributed asynchronously and anonymously Senders are unaware of number and addresses of subscribers Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 3

4 Publish/Subscribe Publish/Subscribe Paradigm Interface subscribe(t) unsubscribe(t) publish(m, t) Publish/subscribe middleware forwards publications to subscribers Focus of our work: Low-power wireless networks with limited resources IIoT Challenge: Low memory routing structure Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 4

5 Publish/Subscribe Memory Constrained Routing Fact 1: Shortest path routing requires routing tables of size Ω(n) Path stretch of protocol P: Ratio of path length achieved by P, divided by shortest path length Fact 2: Protocols with path stretch below 3 require Ω(n) size routing tables [Gavoille et al.] Goal: Routing tables of poly-logarithmic size, e.g., O(log 2 n) Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 5

6 Publish/Subscribe Memory Constrained Routing Virtual Ring: Closed path involving each node at least once Routing Publisher forwards message around ring and subscribers read it Upon return to sender message is discarded Routing table of each node only contains id of next node May incur a linear path stretch, i.e. length of cycle To lessen stretch additional edges a.k.a. fibers are used as shortcuts at cost of increased routing tables Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 6

7 Publish/Subscribe Virtual Ring Routing with Fibers r e 1 r 0 9 e e 1 r 0 9 e e c 2 8 d c 2 8 d c a d b a 3 c 4 5 e 6 7 d b a 3 c 4 5 e 6 7 d b Communication Graph Virtual Ring Virtual Ring with Fibers Fibers are used as short cuts A node skips a ring segment if does not contain a subscriber Concurrent forwarding into disjoint segments PSVR is a pub/sub middleware that uses virtual rings Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 7

8 Publish/Subscribe New Routing Structure Observations Goal Shorter rings reduce path stretch More fibers reduce path stretch but may enlarge routing table Ring structure with smaller stretch and smaller routing tables such that PSVR can be executed without changes New concepts Not all nodes need to be on ring shorter rings Nodes on ring act as proxies for nodes outside ring (NAT) Systematic approach to select fibers Compromise between path stretch and routing table size Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 8

9 Publish/Subscribe New Routing Structure c 13 c 14 c 15 c 0 c1 c 2 c 3 c 12 c 4 c 11 c 5 c 10 c 9 c8 c 7 c 6 Nodes outside ring are homogeneously attached to proxies Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 9

10 Publish/Subscribe Optimal Fiber Structure c 15 c 0 c1 c 2 c 13 c 14 c 3 c 12 c 4 c 11 c 5 c 10 c 9 c8 c 7 c 6 Homogeneous structure of fibers inside smaller ring Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 10

11 Publish/Subscribe Contribution Efficient distributed algorithm to construct the new routing structure Time complexity O(log n) rounds Analysis of algorithm for random graphs Path stretch is w.h.p. in O(log n) Nodes outside ring attach w.h.p. homogeneously to proxies Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 11

12 Publish/Subscribe Assumptions Synchronous CON GEST model of the distributed message passing model each message contains at most O(log n) bits Unique identifiers Nodes have only limited local memory Each node knows size n of network A dedicated starting node v 0 Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 12

13 Informal Description of Algorithm A Fiber 2

14 Informal Description of Algorithm A Fiber Algorithm A Fiber A Fiber works in phases Phase 0 (O(log n) rounds) Starting in v 0 a path P of length 2 r 1 is built (2 r O(log n)) Phase 1 (O(log n) rounds) Path P is closed to a ring C of length 2 r Middle phases Concurrently edges of C are replaced by two edges Replaced edges become fibers After k middle phases, C has up to 2 k log n nodes Final phase Each node in v V \ C randomly selects a neighbor u on C Node u is the proxy for v Each middle phase and the final phase lasts O(1) rounds Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 14

15 Informal Description of Algorithm A Fiber Phases v 0 Phase 0 Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 15

16 Informal Description of Algorithm A Fiber Phases v 0 Phase 0 Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 15

17 Informal Description of Algorithm A Fiber Phases v 0 Phase 0 Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 15

18 Informal Description of Algorithm A Fiber Phases v 0 After O(log n) rounds Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 15

19 Informal Description of Algorithm A Fiber Phases v 0 Phase 1, after another O(log n) rounds Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 15

20 Informal Description of Algorithm A Fiber Phases v 0 Middle Phase Step 1 Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 15

21 Informal Description of Algorithm A Fiber Phases v 0 Middle Phase Step 2 Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 15

22 Informal Description of Algorithm A Fiber Phases v 0 After k Middle Phases: C has at most 2 k log n nodes Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 15

23 Informal Description of Algorithm A Fiber Phases c 13 c 14 c 15 c 0 c1 c 2 c 3 c 12 c 4 c 11 c 5 c 10 c 9 c8 Final Phase c 7 c 6 Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 15

24 Details of A Fiber 3

25 Details of A Fiber Middle Phases v 1 v 2... w 1 w 2 w 3 w 4 w5 {v 1 }... Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 17

26 Details of A Fiber Middle Phases {w 3 } v v 2 1 I 1 I 1 {w 3, w 4 } I 1 I 1 I 1 I 1... w 1 w 2 w 3 w 4 w5 {v 1 }... Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 17

27 Details of A Fiber Middle Phases {w 3 } v 1 v 2 {w 3, w 4 }... w 1 w 2 w 3 w 4 w5 {v 1 }... Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 17

28 Details of A Fiber Middle Phases v 1 v 2 I 2 I 2... w 1 w 2 w 3 w 4 w5 {v 1, v 2 }... Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 17

29 Details of A Fiber Middle Phases v 1 v 2... w 1 w 2 w 3 w 4 w5 {v 1, v 2 }... Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 17

30 Details of A Fiber Middle Phases v 1 v 2 I 3... w 1 w 2 w 3 w 4 w5 {v 1 }... Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 17

31 Details of A Fiber Middle Phases v 1 v 2... w 1 w 2 w 3 w 4 w5 {v 1 }... Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 17

32 Details of A Fiber Observations Individual extensions do not interfere with each other because Each node outside C sends in each middle phase at most one request to integrate and each edge of C accepts at most one integration request Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 18

33 Analysis of A Fiber 4

34 Analysis of A Fiber Analysis of A Fiber for Random Graphs We analyze algorithm A Fiber for the class of random graphs G(n, p) with p O(log n/ n) Claim: In each middle phase w.h.p. size of C is increased by constant factor Random variable Y Number of nodes that are integrated into C in a middle phase. Goal: Lower bound for Y that holds w.h.p. First we compute number of nodes outside C that send an invitation I 2? Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 20

35 Analysis of A Fiber Analysis of Middle Phases How many nodes outside C send an invitation I 2? v 1 v 2 I 2 I 2... w 1 w 2 w 3 w 4 w5 {v 1, v 2 }... Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 21

36 Analysis of A Fiber Analysis of Middle Phases A node v V \ C sends an invitation if it is connected to at least one pair of consecutive nodes on C This event has probability p 2, but these events are not independent Event π v : v V \ C forms a triangle with at least one evenly numbered edge of C π v has probability 1 (1 p 2 ) c/2 and π v s are independent Random Variable X X is the number of nodes v where π v occurs. X is a lower bound for number of nodes that send invitation I 2. E[X] = (n c)(1 (1 p 2 ) c/2 ) (1) Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 22

37 Analysis of A Fiber Analysis of Middle Phases How many nodes on C send an accept message I 3? v 1 v 2 I 3... w 1 w 2 w 3 w 4 w5 {v 1 }... Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 23

38 Analysis of A Fiber Analysis of Middle Phases Computation of Y can be reduced to bins and balls model X number of balls; c number of bins Each ball is thrown randomly in any of the c bins Probability that v C is connected to w in V \ C is independently of v and w equal to p. Y is equal to number of nonempty bins ( ( E[Y X = x] = c ) x ) c (2) Use Chernoff bound to find upper bound for Y that holds w.h.p. Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 24

39 Analysis of A Fiber Result Theorem For a random graph G(n, p) with p O(log n/ n) the following holds w.h.p. 1. After phase 1 ring C consist of O(log n) nodes 2. After O(log n) middle phases C has size O(log n n) and the fiber graph has diameter O(log n) 3. After final phase the maximum number of nodes attached to a single proxy is less than e(n/c 1) with probability 1 1/c Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 25

40 Conclusion & Extensions 5

41 Conclusion & Extensions Conclusion Proposal for a new routing structure for pub/sub systems in wireless networks O(log n) distributed algorithm to build this structure Analysis for random graphs G(n, p) with p O(log n/ n) Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 27

42 Conclusion & Extensions Extension I c 13 c 14 c 15 c 0 c1 c 2 c 3 c 12 c 4 c 11 c 5 c 10 c 9 c8 c 7 c 6 Nodes outside ring need not to be connected directly to proxies. This allows to have even shorter rings Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 28

43 Conclusion & Extensions Extension II c 13 c 14 c 15 c 0 c1 c 2 c 3 c 12 c 4 c 11 c 5 c 10 c 9 c8 c 7 c 6 The double ring structure allows PSVR to send more messages concurrently Volker Turau A O(log n) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems 29

44 A O (log n ) Distributed Algorithm to Construct Routing Structures for Pub/Sub Systems Volker Turau lker Turau VoSafety, and Security of Distributed Systems 20th Int. Symposium on Stabilization, Professor th November 6, 2018 Phone +49 / (0) turau@tuhh.de e/staff/turau Institute of Telematics Hamburg University of Technology TUHH

Distributed Computing over Communication Networks: Leader Election

Distributed Computing over Communication Networks: Leader Election Distributed Computing over Communication Networks: Leader Election Motivation Reasons for electing a leader? Reasons for not electing a leader? Motivation Reasons for electing a leader? Once elected, coordination

More information

Balls Into Bins Model Occupancy Problems

Balls Into Bins Model Occupancy Problems Balls Into Bins Model Occupancy Problems Assume that m balls are thrown randomly is n bins, i.e., the location of each ball is independently and uniformly chosen at random from the n possibilities. How

More information

Distributed Algorithms. Partha Sarathi Mandal Department of Mathematics IIT Guwahati

Distributed Algorithms. Partha Sarathi Mandal Department of Mathematics IIT Guwahati Distributed Algorithms Partha Sarathi Mandal Department of Mathematics IIT Guwahati Thanks to Dr. Sukumar Ghosh for the slides Distributed Algorithms Distributed algorithms for various graph theoretic

More information

Mutually Exclusive Data Dissemination in the Mobile Publish/Subscribe System

Mutually Exclusive Data Dissemination in the Mobile Publish/Subscribe System Mutually Exclusive Data Dissemination in the Mobile Publish/Subscribe System Ning Wang and Jie Wu Dept. of Computer and Info. Sciences Temple University Road Map Introduction Problem and challenge Centralized

More information

Think Global Act Local

Think Global Act Local Think Global Act Local Roger Wattenhofer ETH Zurich Distributed Computing www.disco.ethz.ch Town Planning Patrick Geddes Architecture Buckminster Fuller Computer Architecture Caching Robot Gathering e.g.,

More information

The Round Complexity of Distributed Sorting

The Round Complexity of Distributed Sorting The Round Complexity of Distributed Sorting by Boaz Patt-Shamir Marat Teplitsky Tel Aviv University 1 Motivation Distributed sorting Infrastructure is more and more distributed Cloud Smartphones 2 CONGEST

More information

DS 2009: middleware. David Evans

DS 2009: middleware. David Evans DS 2009: middleware David Evans de239@cl.cam.ac.uk What is middleware? distributed applications middleware remote calls, method invocations, messages,... OS comms. interface sockets, IP,... layer between

More information

NSFA: Nested Scale-Free Architecture for Scalable Publish/Subscribe over P2P Networks

NSFA: Nested Scale-Free Architecture for Scalable Publish/Subscribe over P2P Networks NSFA: Nested Scale-Free Architecture for Scalable Publish/Subscribe over P2P Networks Huanyang Zheng and Jie Wu Dept. of Computer and Info. Sciences Temple University Road Map Introduction Nested Scale-Free

More information

Lecture 5. Figure 1: The colored edges indicate the member edges of several maximal matchings of the given graph.

Lecture 5. Figure 1: The colored edges indicate the member edges of several maximal matchings of the given graph. 5.859-Z Algorithmic Superpower Randomization September 25, 204 Lecture 5 Lecturer: Bernhard Haeupler Scribe: Neil Shah Overview The next 2 lectures are on the topic of distributed computing there are lots

More information

Advanced routing topics. Tuomas Launiainen

Advanced routing topics. Tuomas Launiainen Advanced routing topics Tuomas Launiainen Suboptimal routing Routing trees Measurement of routing trees Adaptive routing Problems Fault-tolerant tables Point-of-failure rerouting Point-of-failure shortest

More information

Exam Principles of Distributed Computing

Exam Principles of Distributed Computing Distributed Computing FS 2015 Prof. Dr. Roger Wattenhofer P. Bissig, P. Brandes, S. Brandt, K.T. Förster, B. Keller, M. König, L. Peer, J. Seidel, D. Stolz, J. Uitto Exam Principles of Distributed Computing

More information

The Visibility Problem and Binary Space Partition. (slides by Nati Srebro)

The Visibility Problem and Binary Space Partition. (slides by Nati Srebro) The Visibility Problem and Binary Space Partition (slides by Nati Srebro) The Visibility Problem b a c d e Algorithms Z-buffer: Draw objects in arbitrary order For each pixel, maintain distance to the

More information

Distributed Coloring in. Kishore Kothapalli Department of Computer Science Johns Hopkins University Baltimore, MD USA

Distributed Coloring in. Kishore Kothapalli Department of Computer Science Johns Hopkins University Baltimore, MD USA Distributed Coloring in Bit Rounds Kishore Kothapalli Department of Computer Science Johns Hopkins University Baltimore, MD USA Vertex Coloring Proper coloring Not a Proper coloring 1 Coloring a Cycle

More information

02 - Distributed Systems

02 - Distributed Systems 02 - Distributed Systems Definition Coulouris 1 (Dis)advantages Coulouris 2 Challenges Saltzer_84.pdf Models Physical Architectural Fundamental 2/58 Definition Distributed Systems Distributed System is

More information

Fractional Cascading in Wireless. Jie Gao Computer Science Department Stony Brook University

Fractional Cascading in Wireless. Jie Gao Computer Science Department Stony Brook University Fractional Cascading in Wireless Sensor Networks Jie Gao Computer Science Department Stony Brook University 1 Sensor Networks Large number of small devices for environment monitoring 2 My recent work Lightweight,

More information

02 - Distributed Systems

02 - Distributed Systems 02 - Distributed Systems Definition Coulouris 1 (Dis)advantages Coulouris 2 Challenges Saltzer_84.pdf Models Physical Architectural Fundamental 2/60 Definition Distributed Systems Distributed System is

More information

Randomized Algorithms 2017A - Lecture 10 Metric Embeddings into Random Trees

Randomized Algorithms 2017A - Lecture 10 Metric Embeddings into Random Trees Randomized Algorithms 2017A - Lecture 10 Metric Embeddings into Random Trees Lior Kamma 1 Introduction Embeddings and Distortion An embedding of a metric space (X, d X ) into a metric space (Y, d Y ) is

More information

Introduction to Distributed Systems

Introduction to Distributed Systems Introduction to Distributed Systems Other matters: review of the Bakery Algorithm: why can t we simply keep track of the last ticket taken and the next ticvket to be called? Ref: [Coulouris&al Ch 1, 2]

More information

The alternator. Mohamed G. Gouda F. Furman Haddix

The alternator. Mohamed G. Gouda F. Furman Haddix Distrib. Comput. (2007) 20:21 28 DOI 10.1007/s00446-007-0033-1 The alternator Mohamed G. Gouda F. Furman Haddix Received: 28 August 1999 / Accepted: 5 July 2000 / Published online: 12 June 2007 Springer-Verlag

More information

Chapter 8 DOMINATING SETS

Chapter 8 DOMINATING SETS Chapter 8 DOMINATING SETS Distributed Computing Group Mobile Computing Summer 2004 Overview Motivation Dominating Set Connected Dominating Set The Greedy Algorithm The Tree Growing Algorithm The Marking

More information

Chapter 8 DOMINATING SETS

Chapter 8 DOMINATING SETS Distributed Computing Group Chapter 8 DOMINATING SETS Mobile Computing Summer 2004 Overview Motivation Dominating Set Connected Dominating Set The Greedy Algorithm The Tree Growing Algorithm The Marking

More information

Maximal Independent Set

Maximal Independent Set Chapter 4 Maximal Independent Set In this chapter we present a first highlight of this course, a fast maximal independent set (MIS) algorithm. The algorithm is the first randomized algorithm that we study

More information

Networking Wireless Sensors: New Challenges

Networking Wireless Sensors: New Challenges Networking Wireless Sensors: New Challenges Bhaskar Krishnamachari Autonomous Networks Research Group Department of Electrical Engineering-Systems USC Viterbi School of Engineering http://ceng.usc.edu/~anrg

More information

Welcome to the course Algorithm Design

Welcome to the course Algorithm Design Welcome to the course Algorithm Design Summer Term 2011 Friedhelm Meyer auf der Heide Lecture 13, 15.7.2011 Friedhelm Meyer auf der Heide 1 Topics - Divide & conquer - Dynamic programming - Greedy Algorithms

More information

Throughout this course, we use the terms vertex and node interchangeably.

Throughout this course, we use the terms vertex and node interchangeably. Chapter Vertex Coloring. Introduction Vertex coloring is an infamous graph theory problem. It is also a useful toy example to see the style of this course already in the first lecture. Vertex coloring

More information

Leader Election in Rings

Leader Election in Rings Leader Election Arvind Krishnamurthy Fall 2003 Leader Election in Rings Under different models: Synchronous vs. asynchronous Anonymous vs. non-anonymous (knowledge of unique id) Knowledge of n (non-uniform)

More information

Introduction. Chapter 1

Introduction. Chapter 1 Introduction Chapter 1 Definition of a Distributed System (1) A distributed system is: A collection of independent computers that appears to its users as a single coherent system. Definition of a Distributed

More information

Fast Distributed Algorithms for Connectivity and MST in Large Graphs

Fast Distributed Algorithms for Connectivity and MST in Large Graphs Fast Distributed Algorithms for Connectivity and MST in Large Graphs Gopal Pandurangan Dept. of Computer Science University of Houston gopalpandurangan@gmail.com Peter Robinson Royal Holloway University

More information

Enterprise Application Integration (EAI) Chapter 7. An Introduction to EAI and Middleware

Enterprise Application Integration (EAI) Chapter 7. An Introduction to EAI and Middleware Enterprise Application Integration (EAI) Chapter 7. An Introduction to EAI and Middleware All programmers are playwrights and all computers are lousy actors. Chapter 7 An Introduction to EAI and Middleware

More information

Evaluation of Cartesian-based Routing Metrics for Wireless Sensor Networks

Evaluation of Cartesian-based Routing Metrics for Wireless Sensor Networks Evaluation of Cartesian-based Routing Metrics for Wireless Sensor Networks Ayad Salhieh Department of Electrical and Computer Engineering Wayne State University Detroit, MI 48202 ai4874@wayne.edu Loren

More information

MAC Theory. Chapter 7. Ad Hoc and Sensor Networks Roger Wattenhofer

MAC Theory. Chapter 7. Ad Hoc and Sensor Networks Roger Wattenhofer MAC Theory Chapter 7 7/1 Seeing Through Walls! [Wilson, Patwari, U. Utah] Schoolboy s dream, now reality thank to sensor networks... 7/2 Rating Area maturity First steps Text book Practical importance

More information

MAXIMAL PLANAR SUBGRAPHS OF FIXED GIRTH IN RANDOM GRAPHS

MAXIMAL PLANAR SUBGRAPHS OF FIXED GIRTH IN RANDOM GRAPHS MAXIMAL PLANAR SUBGRAPHS OF FIXED GIRTH IN RANDOM GRAPHS MANUEL FERNÁNDEZ, NICHOLAS SIEGER, AND MICHAEL TAIT Abstract. In 99, Bollobás and Frieze showed that the threshold for G n,p to contain a spanning

More information

Sketching Asynchronous Streams Over a Sliding Window

Sketching Asynchronous Streams Over a Sliding Window Sketching Asynchronous Streams Over a Sliding Window Srikanta Tirthapura (Iowa State University) Bojian Xu (Iowa State University) Costas Busch (Rensselaer Polytechnic Institute) 1/32 Data Stream Processing

More information

Introduction to Distributed Systems

Introduction to Distributed Systems Introduction to Distributed Systems Minsoo Ryu Department of Computer Science and Engineering 2 Definition A distributed system is a collection of independent computers that appears to its users as a single

More information

Graph Algorithms. Many problems in networks can be modeled as graph problems.

Graph Algorithms. Many problems in networks can be modeled as graph problems. Graph Algorithms Graph Algorithms Many problems in networks can be modeled as graph problems. - The topology of a distributed system is a graph. - Routing table computation uses the shortest path algorithm

More information

Concepts of Distributed Systems 2006/2007

Concepts of Distributed Systems 2006/2007 Concepts of Distributed Systems 2006/2007 Introduction & overview Johan Lukkien 1 Introduction & overview Communication Distributed OS & Processes Synchronization Security Consistency & replication Programme

More information

Maximal Independent Set

Maximal Independent Set Chapter 0 Maximal Independent Set In this chapter we present a highlight of this course, a fast maximal independent set (MIS) algorithm. The algorithm is the first randomized algorithm that we study in

More information

Fast Clustering using MapReduce

Fast Clustering using MapReduce Fast Clustering using MapReduce Alina Ene Sungjin Im Benjamin Moseley September 6, 2011 Abstract Clustering problems have numerous applications and are becoming more challenging as the size of the data

More information

Probabilistic Modeling of Leach Protocol and Computing Sensor Energy Consumption Rate in Sensor Networks

Probabilistic Modeling of Leach Protocol and Computing Sensor Energy Consumption Rate in Sensor Networks Probabilistic Modeling of Leach Protocol and Computing Sensor Energy Consumption Rate in Sensor Networks Dezhen Song CS Department, Texas A&M University Technical Report: TR 2005-2-2 Email: dzsong@cs.tamu.edu

More information

CS224W: Social and Information Network Analysis Jure Leskovec, Stanford University, y http://cs224w.stanford.edu Due in 1 week: Oct 4 in class! The idea of the reaction papers is: To familiarize yourselves

More information

Common Architectural Styles & Patterns

Common Architectural Styles & Patterns Common Architectural Styles & Patterns some we ve already kind of discussed model view controller blackboard client/server layered pipe-and-filter Lots of ways to classify all these Application Domain

More information

Load Balanced Short Path Routing in Wireless Networks Jie Gao, Stanford University Li Zhang, Hewlett-Packard Labs

Load Balanced Short Path Routing in Wireless Networks Jie Gao, Stanford University Li Zhang, Hewlett-Packard Labs Load Balanced Short Path Routing in Wireless Networks Jie Gao, Stanford University Li Zhang, Hewlett-Packard Labs Aravind Ranganathan University of Cincinnati February 22, 2007 Motivation Routing in wireless

More information

Resource Discovery in Networks under Bandwidth Limitations

Resource Discovery in Networks under Bandwidth Limitations Resource Discovery in Networks under Bandwidth Limitations Kishori M. Konwar Department of Computer Science and Engg University of Connecticut 371 Fairfield Rd., Unit 2155 Storrs, CT 06269, USA kishori@cse.uconn.edu

More information

DISTRIBUTED SYSTEMS Principles and Paradigms Second Edition ANDREW S. TANENBAUM MAARTEN VAN STEEN. Chapter 1. Introduction

DISTRIBUTED SYSTEMS Principles and Paradigms Second Edition ANDREW S. TANENBAUM MAARTEN VAN STEEN. Chapter 1. Introduction DISTRIBUTED SYSTEMS Principles and Paradigms Second Edition ANDREW S. TANENBAUM MAARTEN VAN STEEN Chapter 1 Introduction Modified by: Dr. Ramzi Saifan Definition of a Distributed System (1) A distributed

More information

Coloring 3-Colorable Graphs

Coloring 3-Colorable Graphs Coloring -Colorable Graphs Charles Jin April, 015 1 Introduction Graph coloring in general is an etremely easy-to-understand yet powerful tool. It has wide-ranging applications from register allocation

More information

The Touring Polygons Problem (TPP)

The Touring Polygons Problem (TPP) The Touring Polygons Problem (TPP) [Dror-Efrat-Lubiw-M]: Given a sequence of k polygons in the plane, a start point s, and a target point, t, we seek a shortest path that starts at s, visits in order each

More information

A Holistic Solution for Reliable Over-the-Air Software Updates in Large Industrial Plants. Florian Kauer, Florian Meyer, Volker Turau

A Holistic Solution for Reliable Over-the-Air Software Updates in Large Industrial Plants. Florian Kauer, Florian Meyer, Volker Turau A Holistic Solution for Reliable Over-the-Air Software Updates in Large Industrial Plants Florian Kauer, Florian Meyer, Volker Turau June 13 th, 2017 Institute of Telematics Hamburg University of Technology

More information

6.852 Lecture 10. Minimum spanning tree. Reading: Chapter 15.5, Gallager-Humblet-Spira paper. Gallager-Humblet-Spira algorithm

6.852 Lecture 10. Minimum spanning tree. Reading: Chapter 15.5, Gallager-Humblet-Spira paper. Gallager-Humblet-Spira algorithm 6.852 Lecture 10 Gallager-Humblet-Spira algorithm Reading: Chapter 15.5, Gallager-Humblet-Spira paper Assume undirected graph (i.e., bidirectional communication) distinct edge weights size and diameter

More information

In this paper we consider probabilistic algorithms for that task. Each processor is equipped with a perfect source of randomness, and the processor's

In this paper we consider probabilistic algorithms for that task. Each processor is equipped with a perfect source of randomness, and the processor's A lower bound on probabilistic algorithms for distributive ring coloring Moni Naor IBM Research Division Almaden Research Center San Jose, CA 9510 Abstract Suppose that n processors are arranged in a ring

More information

A Remote Interface for Live Interaction with OMNeT++ Simulations. Maximilian Köstler and Florian Kauer. OMNeT++ Community Summit 2017

A Remote Interface for Live Interaction with OMNeT++ Simulations. Maximilian Köstler and Florian Kauer. OMNeT++ Community Summit 2017 A Remote Interface for Live Interaction with OMNeT++ Simulations Maximilian Köstler and Florian Kauer OMNeT++ Community Summit 2017 September 8 th, 2017 Institute of Telematics Hamburg University of Technology

More information

The Capacity of Wireless Networks

The Capacity of Wireless Networks The Capacity of Wireless Networks Piyush Gupta & P.R. Kumar Rahul Tandra --- EE228 Presentation Introduction We consider wireless networks without any centralized control. Try to analyze the capacity of

More information

Randomized Rumor Spreading in Social Networks

Randomized Rumor Spreading in Social Networks Randomized Rumor Spreading in Social Networks Benjamin Doerr (MPI Informatics / Saarland U) Summary: We study how fast rumors spread in social networks. For the preferential attachment network model and

More information

Algorithm 23 works. Instead of a spanning tree, one can use routing.

Algorithm 23 works. Instead of a spanning tree, one can use routing. Chapter 5 Shared Objects 5.1 Introduction Assume that there is a common resource (e.g. a common variable or data structure), which different nodes in a network need to access from time to time. If the

More information

ANT-INSPIRED DENSITY ESTIMATION VIA RANDOM WALKS. Nancy Lynch, Cameron Musco, Hsin-Hao Su BDA 2016 July, 2016 Chicago, Illinois

ANT-INSPIRED DENSITY ESTIMATION VIA RANDOM WALKS. Nancy Lynch, Cameron Musco, Hsin-Hao Su BDA 2016 July, 2016 Chicago, Illinois ANT-INSPIRED DENSITY ESTIMATION VIA RANDOM WALKS Nancy Lynch, Cameron Musco, Hsin-Hao Su BDA 2016 July, 2016 Chicago, Illinois 1. Introduction Ants appear to use estimates of colony density (number of

More information

Streaming algorithms for embedding and computing edit distance

Streaming algorithms for embedding and computing edit distance Streaming algorithms for embedding and computing edit distance in the low distance regime Diptarka Chakraborty joint work with Elazar Goldenberg and Michal Koucký 7 November, 2015 Outline 1 Introduction

More information

LAMC Intermediate I & II February 8, Oleg Gleizer

LAMC Intermediate I & II February 8, Oleg Gleizer LAMC Intermediate I & II February 8, 2015 Oleg Gleizer prof1140g@math.ucla.edu Problem 1 The square P QRS is inscribed in the triangle ABC as shown on the picture below. The length of the side BC is 12

More information

Initial studies of SCI LAN topologies for local area clustering

Initial studies of SCI LAN topologies for local area clustering Prepared for the First International Workshop on SCI-Based Low-Cost/High-Performance Computing, Santa Clara University Initial studies of SCI LAN topologies for local area clustering Haakon Bryhni * and

More information

Hardware-Assisted IEEE Transmissions and Why to Avoid Them. Andreas Weigel, Volker Turau. IDCS 2015 September 2 nd, 2015

Hardware-Assisted IEEE Transmissions and Why to Avoid Them. Andreas Weigel, Volker Turau. IDCS 2015 September 2 nd, 2015 Hardware-Assisted IEEE 802.15.4 Transmissions and Why to Avoid Them Andreas Weigel, Volker Turau IDCS 2015 September 2 nd, 2015 Institute of Telematics Hamburg University of Technology TUHH Introduction

More information

Hardness of Approximation for the TSP. Michael Lampis LAMSADE Université Paris Dauphine

Hardness of Approximation for the TSP. Michael Lampis LAMSADE Université Paris Dauphine Hardness of Approximation for the TSP Michael Lampis LAMSADE Université Paris Dauphine Sep 2, 2015 Overview Hardness of Approximation What is it? How to do it? (Easy) Examples The PCP Theorem What is it?

More information

Models of distributed computing: port numbering and local algorithms

Models of distributed computing: port numbering and local algorithms Models of distributed computing: port numbering and local algorithms Jukka Suomela Adaptive Computing Group Helsinki Institute for Information Technology HIIT University of Helsinki FMT seminar, 26 February

More information

6 Distributed data management I Hashing

6 Distributed data management I Hashing 6 Distributed data management I Hashing There are two major approaches for the management of data in distributed systems: hashing and caching. The hashing approach tries to minimize the use of communication

More information

Draft Notes 1 : Scaling in Ad hoc Routing Protocols

Draft Notes 1 : Scaling in Ad hoc Routing Protocols Draft Notes 1 : Scaling in Ad hoc Routing Protocols Timothy X Brown University of Colorado April 2, 2008 2 Introduction What is the best network wireless network routing protocol? This question is a function

More information

Efficient Broadcasting and Gathering in Wireless Ad-Hoc Networks

Efficient Broadcasting and Gathering in Wireless Ad-Hoc Networks Efficient Broadcasting and Gathering in Wireless Ad-Hoc Networks Melih Onus and Andréa Richa Computer Science and Engineering Department Arizona State University PO Box 875406 Tempe, AZ 85287, USA {Melih.Onus,aricha}@asu.edu

More information

CCD: Efficient Customized Content Dissemination in Distributed Publish/Subscribe

CCD: Efficient Customized Content Dissemination in Distributed Publish/Subscribe Dissemination in Distributed Publish/Subscribe H. Jafarpour, B. Hore, S. Mehrotra and N. Venkatasubramanian Information Systems Group Dept. of Computer Science UC Irvine 1 Customized content dissemination

More information

COMP Online Algorithms. Online Bin Packing. Shahin Kamali. Lecture 20 - Nov. 16th, 2017 University of Manitoba

COMP Online Algorithms. Online Bin Packing. Shahin Kamali. Lecture 20 - Nov. 16th, 2017 University of Manitoba COMP 7720 - Online Algorithms Online Bin Packing Shahin Kamali Lecture 20 - Nov. 16th, 2017 University of Manitoba COMP 7720 - Online Algorithms Online Bin Packing 1 / 24 Review & Plan COMP 7720 - Online

More information

MHH: A Novel Protocol for Mobility Management in Publish/Subscribe Systems

MHH: A Novel Protocol for Mobility Management in Publish/Subscribe Systems MHH: A Novel Protocol for Mobility Management in Publish/Subscribe Systems Jinling Wang 1, 2, Jiannong Cao 1, Jing Li 2, Jie Wu 3 1 Department of Computing, Hong Kong Polytechnic University 2 Institute

More information

Peer-To-Peer Techniques

Peer-To-Peer Techniques PG DynaSearch Markus Benter 31th October, 2013 Introduction Centralized P2P-Networks Unstructured P2P-Networks Structured P2P-Networks Misc 1 What is a Peer-to-Peer System? Definition Peer-to-peer systems

More information

Sorting Based Data Centric Storage

Sorting Based Data Centric Storage Sorting Based Data Centric Storage Fenghui Zhang, Anxiao(Andrew) Jiang, and Jianer Chen Dept. of Computer Science, Texas A&M Univ. College Station, TX 77843. {fhzhang, ajiang, chen}@cs.tamu.edu. Abstract

More information

We noticed that the trouble is due to face routing. Can we ignore the real coordinates and use virtual coordinates for routing?

We noticed that the trouble is due to face routing. Can we ignore the real coordinates and use virtual coordinates for routing? Geographical routing in practice We noticed that the trouble is due to face routing. Is greedy routing robust to localization noise? Can we ignore the real coordinates and use virtual coordinates for routing?

More information

Reliable Distributed System Approaches

Reliable Distributed System Approaches Reliable Distributed System Approaches Manuel Graber Seminar of Distributed Computing WS 03/04 The Papers The Process Group Approach to Reliable Distributed Computing K. Birman; Communications of the ACM,

More information

Detecting Sybil Nodes in Wireless Networks. *: SIS Dept., UNC Charlotte **: ECE Dept., WPI

Detecting Sybil Nodes in Wireless Networks. *: SIS Dept., UNC Charlotte **: ECE Dept., WPI Detecting Sybil Nodes in Wireless Networks with Physical Layer Network Coding Weichao Wang*, DiPu**, and Alex Wyglinski** *: SIS Dept., UNC Charlotte **: ECE Dept., WPI Motivation Network coding technique

More information

MIDDLEWARE SYSTEMS RESEARCH GROUP RESEARCH MSRG ORG.ORG

MIDDLEWARE SYSTEMS RESEARCH GROUP RESEARCH MSRG ORG.ORG MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Chen Chen joint work with Roman Vitenberg and Hans Arno Jacobsen 7/23/2013 / {b,c,d} V1 {a,c} [PODC 07] {b,c,d} cd} V1 {a} {a,c} {a} V5 V2 V5 V2 V4 V3 V4 V4 V3

More information

Frequently asked questions from the previous class survey

Frequently asked questions from the previous class survey CS 455: INTRODUCTION TO DISTRIBUTED SYSTEMS [DISTRIBUTED COORDINATION/MUTUAL EXCLUSION] Shrideep Pallickara Computer Science Colorado State University L22.1 Frequently asked questions from the previous

More information

CS455: Introduction to Distributed Systems [Spring 2018] Dept. Of Computer Science, Colorado State University

CS455: Introduction to Distributed Systems [Spring 2018] Dept. Of Computer Science, Colorado State University Frequently asked questions from the previous class survey CS 455: INTRODUCTION TO DISTRIBUTED SYSTEMS [DISTRIBUTED COORDINATION/MUTUAL EXCLUSION] Shrideep Pallickara Computer Science Colorado State University

More information

A Delay-Optimal Group Mutual Exclusion Algorithm for a Tree Network

A Delay-Optimal Group Mutual Exclusion Algorithm for a Tree Network JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 24, 573-583 (2008) Short Paper A Delay-Optimal Group Mutual Exclusion Algorithm for a Tree Network VINAY MADENUR AND NEERAJ MITTAL + Internet Services Qualcomm,

More information

CS 580: Algorithm Design and Analysis. Jeremiah Blocki Purdue University Spring 2018

CS 580: Algorithm Design and Analysis. Jeremiah Blocki Purdue University Spring 2018 CS 580: Algorithm Design and Analysis Jeremiah Blocki Purdue University Spring 2018 Recap: Stable Matching Problem Definition of a Stable Matching Stable Roomate Matching Problem Stable matching does not

More information

Telematics Chapter 9: Peer-to-Peer Networks

Telematics Chapter 9: Peer-to-Peer Networks Telematics Chapter 9: Peer-to-Peer Networks Beispielbild User watching video clip Server with video clips Application Layer Presentation Layer Application Layer Presentation Layer Session Layer Session

More information

WSN Routing Protocols

WSN Routing Protocols WSN Routing Protocols 1 Routing Challenges and Design Issues in WSNs 2 Overview The design of routing protocols in WSNs is influenced by many challenging factors. These factors must be overcome before

More information

All-to-All Communication

All-to-All Communication Network Algorithms All-to-All Communication Thanks to Thomas Locher from ABB Research for basis of slides! Stefan Schmid @ T-Labs, 2011 Repetition Tree: connected graph without cycles Spanning subgraph:

More information

On Veracious Search In Unsystematic Networks

On Veracious Search In Unsystematic Networks On Veracious Search In Unsystematic Networks K.Thushara #1, P.Venkata Narayana#2 #1 Student Of M.Tech(S.E) And Department Of Computer Science And Engineering, # 2 Department Of Computer Science And Engineering,

More information

The Limits of Sorting Divide-and-Conquer Comparison Sorts II

The Limits of Sorting Divide-and-Conquer Comparison Sorts II The Limits of Sorting Divide-and-Conquer Comparison Sorts II CS 311 Data Structures and Algorithms Lecture Slides Monday, October 12, 2009 Glenn G. Chappell Department of Computer Science University of

More information

Introduction to Graph Theory

Introduction to Graph Theory Introduction to Graph Theory Tandy Warnow January 20, 2017 Graphs Tandy Warnow Graphs A graph G = (V, E) is an object that contains a vertex set V and an edge set E. We also write V (G) to denote the vertex

More information

Big Data Analytics. Special Topics for Computer Science CSE CSE April 14

Big Data Analytics. Special Topics for Computer Science CSE CSE April 14 Big Data Analytics Special Topics for Computer Science CSE 4095-001 CSE 5095-005 April 14 Fei Wang Associate Professor Department of Computer Science and Engineering fei_wang@uconn.edu Scalability I K-d

More information

Low Diameter Graph Decompositions

Low Diameter Graph Decompositions Low Diameter Graph Decompositions N.Linial and M.Saks 1991 Seminar of Distributed Computing ETH Zürich 16. December 2003 Josias Thöny Contents Introduction Sequential algorithm

More information

Route planning / Search Movement Group behavior Decision making

Route planning / Search Movement Group behavior Decision making Game AI Where is the AI Route planning / Search Movement Group behavior Decision making General Search Algorithm Design Keep a pair of set of states: One, the set of states to explore, called the open

More information

Microservice-Based Agile Architectures:

Microservice-Based Agile Architectures: Microservice-Based Agile Architectures: An Opportunity for Specialized Niche Technologies Stefano Munari, Sebastiano Valle, Tullio Vardanega University of Padua, Department of Mathematics Ada-Europe 2018

More information

CS555: Distributed Systems [Fall 2017] Dept. Of Computer Science, Colorado State University

CS555: Distributed Systems [Fall 2017] Dept. Of Computer Science, Colorado State University CS 555: DISTRIBUTED SYSTEMS [MESSAGING SYSTEMS] Shrideep Pallickara Computer Science Colorado State University Frequently asked questions from the previous class survey Distributed Servers Security risks

More information

Introduction to Messaging using JMS

Introduction to Messaging using JMS Introduction to Messaging using JMS Evan Mamas emamas@ca.ibm.com IBM Toronto Lab Outline Basic Concepts API Architecture API Programming Model Advanced features Integration with J2EE Simple applications

More information

Distributed Leader Election Algorithms in Synchronous Networks

Distributed Leader Election Algorithms in Synchronous Networks Distributed Leader Election Algorithms in Synchronous Networks Mitsou Valia National Technical University of Athens School of Applied Mathematics and Physics 1/66 Distributed Computing Distributed computing

More information

CS 322: (Social and Information) Network Analysis Jure Leskovec Stanford University

CS 322: (Social and Information) Network Analysis Jure Leskovec Stanford University CS 322: (Social and Information) Network Analysis Jure Leskovec Stanford University Course website: http://snap.stanford.edu/na09 Slides will be available online Reading material will be posted online:

More information

Dynamics, Non-Cooperation, and Other Algorithmic Challenges in Peer-to-Peer Computing

Dynamics, Non-Cooperation, and Other Algorithmic Challenges in Peer-to-Peer Computing Dynamics, Non-Cooperation, and Other Algorithmic Challenges in Peer-to-Peer Computing Stefan Schmid Distributed Computing Group Oberseminar TU München, Germany December 2007 Networks Neuron Networks DISTRIBUTED

More information

Distributively Computing Random Walk Betweenness Centrality in Linear Time

Distributively Computing Random Walk Betweenness Centrality in Linear Time 2017 IEEE 37th International Conference on Distributed Computing Systems Distributively Computing Random Walk Betweenness Centrality in Linear Time Qiang-Sheng Hua, Ming Ai, Hai Jin, Dongxiao Yu, Xuanhua

More information

Software Architecture Patterns

Software Architecture Patterns Software Architecture Patterns *based on a tutorial of Michael Stal Harald Gall University of Zurich http://seal.ifi.uzh.ch/ase www.infosys.tuwien.ac.at Overview Goal Basic architectural understanding

More information

Interprocess Communication Tanenbaum, van Steen: Ch2 (Ch3) CoDoKi: Ch2, Ch3, Ch5

Interprocess Communication Tanenbaum, van Steen: Ch2 (Ch3) CoDoKi: Ch2, Ch3, Ch5 Interprocess Communication Tanenbaum, van Steen: Ch2 (Ch3) CoDoKi: Ch2, Ch3, Ch5 Fall 2008 Jussi Kangasharju Chapter Outline Overview of interprocess communication Remote invocations (RPC etc.) Message

More information

arxiv: v4 [cs.dc] 21 Oct 2013

arxiv: v4 [cs.dc] 21 Oct 2013 DEX: Self-healing Expanders Gopal Pandurangan Peter Robinson Amitabh Trehan Abstract arxiv:1206.1522v4 [cs.dc] 21 Oct 2013 We present a fully-distributed self-healing algorithm DEX that maintains a constant

More information

ESIR SR. Unit 10a: JGroups. François Taïani

ESIR SR. Unit 10a: JGroups. François Taïani ESIR SR Unit 10a: JGroups François Taïani Overview of the Session n What is JMS n Messages vs. RPC See lecture on indirect communication n Interaction Styles n Main JMS Classes n Advanced Features F. Taiani

More information

Efficient Aggregation Scheduling in Multihop Wireless Sensor Networks with SINR Constraints

Efficient Aggregation Scheduling in Multihop Wireless Sensor Networks with SINR Constraints 1 Efficient Aggregation Scheduling in Multihop Wireless Sensor Networks with SINR Constraints Xiaohua Xu, Xiang-Yang Li, Senior Member, IEEE, and Min Song, Senior Member, IEEE Abstract We study delay efficient

More information

Given a graph, find an embedding s.t. greedy routing works

Given a graph, find an embedding s.t. greedy routing works Given a graph, find an embedding s.t. greedy routing works Greedy embedding of a graph 99 Greedy embedding Given a graph G, find an embedding of the vertices in R d, s.t. for each pair of nodes s, t, there

More information

Today s Outline. CSE 326: Data Structures Asymptotic Analysis. Analyzing Algorithms. Analyzing Algorithms: Why Bother? Hannah Takes a Break

Today s Outline. CSE 326: Data Structures Asymptotic Analysis. Analyzing Algorithms. Analyzing Algorithms: Why Bother? Hannah Takes a Break Today s Outline CSE 326: Data Structures How s the project going? Finish up stacks, queues, lists, and bears, oh my! Math review and runtime analysis Pretty pictures Asymptotic analysis Hannah Tang and

More information

Scalable Routing for Networks of Dynamic Substrates. Boris Drazic

Scalable Routing for Networks of Dynamic Substrates. Boris Drazic Scalable Routing for Networks of Dynamic Substrates by Boris Drazic A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Graduate Department of Electrical

More information