Data Structures in. Sean Cribbs. Friday, April 26, 13

Size: px
Start display at page:

Download "Data Structures in. Sean Cribbs. Friday, April 26, 13"

Transcription

1 Data Structures in Sean Cribbs

2 Riak is Eventually-Consistent

3 Eventual Consistency 1 2 Replicated Loose coordination 3 Convergence

4 Eventual is Good Fault-tolerant Highly available Low-latency

5 Consistency? 1 A 2 No clear winner! 3 Throw one out? Keep both? B

6 Consistency? 1 A 2 No clear winner! 3 Throw one out? Keep both? B Cassandra

7 Consistency? 1 A 2 No clear winner! 3 Throw one out? Keep both? B Cassandra Riak & Voldemort

8 Conflicts! B! A!

9 Siblings in Riak HTTP/ Multiple Choices X- Riak- Vclock: a85hygdgygdkbviszmk55zkyehnzwblkinio8kgf2tyvhyikf0ciszunmtbzh YVKbIhEUl +VK4spDFTPxhHzFyqhEoVQz7wkSAGLMGuz6FSocFIUijE3pt5HlsgCAA== Vary: Accept, Accept- Encoding Server: MochiWeb/1.1 WebMachine/1.9.0 (participate in the frantic) Date: Fri, 30 Sep :24:35 GMT Content- Type: text/plain Content- Length: 102 Siblings: 16vic4eU9ny46o4KPiDz1f 4v5xOg4bVwUYZdMkqf0d6I 6nr5tDTmhxnwuAFJDd2s6G 6zRSZFUJlHXZ15o9CG0BYl

10 Siblings in Riak HTTP/ Multiple Choices X- Riak- Vclock: a85hygdgygdkbviszmk55zkyehnzwblkinio8kgf2tyvhyikf0ciszunmtbzh YVKbIhEUl +VK4spDFTPxhHzFyqhEoVQz7wkSAGLMGuz6FSocFIUijE3pt5HlsgCAA== Vary: Accept, Accept- Encoding Server: MochiWeb/1.1 WebMachine/1.9.0 (participate in the frantic) Date: Fri, 30 Sep :24:35 GMT Content- Type: text/plain Content- Length: 102 Siblings: 16vic4eU9ny46o4KPiDz1f 4v5xOg4bVwUYZdMkqf0d6I 6nr5tDTmhxnwuAFJDd2s6G 6zRSZFUJlHXZ15o9CG0BYl list of siblings

11 Siblings in Riak HTTP/ Multiple Choices X- Riak- Vclock: a85hygdgygdkbviszmk55zkyehnzwblkinio8kgf2tyvhyikf0ciszunmtbzhyvkbiheul +VK4spDFTPxhHzFyqhEoVQz7wkSAGLMGuz6FSocFIUijE3pt5HlsgCAA== Vary: Accept, Accept- Encoding Server: MochiWeb/1.1 WebMachine/1.9.0 (participate in the frantic) Date: Fri, 30 Sep :24:35 GMT Content- Type: multipart/mixed; boundary=yinlmzyur9feb17okmytgksylvh Content- Length: YinLMzyUR9feB17okMytgKsylvh Content- Type: application/x- www- form- urlencoded Link: </riak/test>; rel="up" Etag: 16vic4eU9ny46o4KPiDz1f Last- Modified: Wed, 10 Mar :01:06 GMT {"bar":"baz"} - - YinLMzyUR9feB17okMytgKsylvh Content- Type: application/json Link: </riak/test>; rel="up" Etag: 4v5xOg4bVwUYZdMkqf0d6I Last- Modified: Wed, 10 Mar :00:04 GMT {"bar":"baz"} - - YinLMzyUR9feB17okMytgKsylvh Content- Type: application/json Link: </riak/test>; rel="up"

12 Siblings in Riak HTTP/ Multiple Choices X- Riak- Vclock: a85hygdgygdkbviszmk55zkyehnzwblkinio8kgf2tyvhyikf0ciszunmtbzhyvkbiheul +VK4spDFTPxhHzFyqhEoVQz7wkSAGLMGuz6FSocFIUijE3pt5HlsgCAA== Vary: Accept, Accept- Encoding Server: MochiWeb/1.1 WebMachine/1.9.0 (participate in the frantic) Date: Fri, 30 Sep :24:35 GMT Content- Type: multipart/mixed; boundary=yinlmzyur9feb17okmytgksylvh Content- Length: YinLMzyUR9feB17okMytgKsylvh Content- Type: application/x- www- form- urlencoded Link: </riak/test>; rel="up" Etag: 16vic4eU9ny46o4KPiDz1f Last- Modified: Wed, 10 Mar :01:06 GMT {"bar":"baz"} - - YinLMzyUR9feB17okMytgKsylvh Content- Type: application/json Link: </riak/test>; rel="up" Etag: 4v5xOg4bVwUYZdMkqf0d6I Last- Modified: Wed, 10 Mar :00:04 GMT all the values {"bar":"baz"} - - YinLMzyUR9feB17okMytgKsylvh Content- Type: application/json Link: </riak/test>; rel="up"

13 Semantic Resolution Your app knows the domain - use business rules to resolve Amazon Dynamo s shopping cart

14 Semantic Resolution BAD Your app knows the domain - use business rules to resolve Amazon Dynamo s shopping cart

15 Semantic Resolution BAD Your app knows the domain - use business rules to resolve Amazon Dynamo s shopping cart Ad hoc approaches have proven brittle and error-prone

16 Goals Meaningful values Automatic resolution Transparent to user

17 WARNING This is a lot of math. Side effects may include dry mouth, itchy rash, and a desire to go back for a PhD.

18 Monotonic Functions Change in strictly a single direction Consecutive values may be equal Monotonic: Linear, Exponential Non-monotonic: Quadratic, Sinusoidal

19 Monotonic Functions Change in strictly a single direction Consecutive values may be equal Monotonic: Linear, Exponential Non-monotonic: Quadratic, Sinusoidal

20 Monotonic Logic Existing facts are never refuted New facts can be added Knowledge only grows

21 Monotonic Logic Existing facts are never refuted New facts can be added Knowledge only grows monotonicity of entailment

22

23 Bounded Join Semi-Lattice S,,

24 Bounded Join Semi-Lattice S,, S is a set

25 Bounded Join Semi-Lattice S,, S is a set S (minimal element)

26 Bounded Join Semi-Lattice S,, is a least-upper bound function x, y S, z S: x y = z

27 Bounded Join Semi-Lattice S,, is a least-upper bound function x, y S, z S: x y = z x, y S: x S y x y = y partial order

28 Bounded Join Semi-Lattice S,, is a least-upper bound function x, y S, z S: x y = z x, y S: x S y x y = y partial order x S: x = x identity

29 Set Lattice S = all finite sets = set-union = {}

30 Set Lattice {a,b,c,d,e} Time {a,b,c,d} {a,b,c} {b,c} {a,b} {b,c,d} {b,c,d,e} {c,d,e} {c,d} {d,e} {a} {b} {c} {d} {e} S = all finite sets = set-union = {}

31 Vector Clock

32 Vector Clock Vector clock is a lattice...

33 Vector Clock Vector clock is a lattice... S = all vectors of (Actor, Count) pairs = All Actors, each with their max Count = [] (empty vector)

34 Vector Clock Vector clock is a lattice... S = all vectors of (Actor, Count) pairs = All Actors, each with their max Count = [] (empty vector)...but the associated Riak value is nonmonotonic,

35 Vector Clock Vector clock is a lattice... S = all vectors of (Actor, Count) pairs = All Actors, each with their max Count = [] (empty vector)...but the associated Riak value is nonmonotonic,...and the vclock is not meaningful to the client.

36

37 CRDT Flavors Convergent (state-based) One replica updates, then forwards entire state, downstream merges Commutative (operation-based) Only mutations (ops) communicated Needs a reliable broadcast channel

38 CRDT Types Registers LWW, MV Counters Positive, P/N Sets Grow only, Two-Phase, Observed-Remove Graphs 2P-2P Lists Growable-array Collaborative editing Treedoc

39 Garbage! Monotonic types are space-inefficient Garbage-collection is non-monotonic Strong consensus may be needed for GC Related: vector-clock pruning, key deletion

40 Garbage! Counters Dead actors Sets, Graphs Tombstones

41 Theory Into Practice

42 Introducing Riak DT

43 Riak DT Riak Core Application State-based CRDTS Independent storage engine HTTP API CRDT behaviour QuickCheck convergence property

44 CRDT Behaviour new/0 empty CRDT value/1 the resolved value update/3 mutate CRDT merge/2 converge two CRDTs equal/2 compare internal value

45 G-Counter Simple version vector (28 LoC) [{ActorId,Count}] Update: increment actor s count Merge: greatest value per Actor Value: sum of Counts

46 G-Counter new() - > []. value(gcnt) - > sum([cnt {_Act, Cnt} <- GCnt]). equal(va,vb) - > lists:sort(va) =:= lists:sort(vb).

47 PN-Counter { } P = [{a,10},{b,2}], N = [{a,1},{c,5}] 2 x G-Counter (10 + 2) - (1 + 5) = 12-6 = 6 P - N = value

48 Riak DT In Action Bitcask storage per vnode Value / Update FSM per request Webmachine resource(s) e.g. GET /counters/key Read-repair

49 Trade-Offs Update: Primary replica only Secondary/Fallbacks may Merge PW=DW=1 by default Read-before-Write in the request path

50 But wait...

51 Riak KV Heavily-tested Storage engines Multi-datacenter Active anti-entropy Secondary Indexes, MapReduce KV Object = MV-Register

52 MERGE ALL THE CODE!!!

53 CRDTs in KV Store CRDTs as values inside KV Objects Requires allow_mult = true Resolve CRDTs separately from opaque values Counters-only at first

54 KV Counters

55 KV Counters GET /buckets/b/counters/c => 5

56 KV Counters GET /buckets/b/counters/c => 5 POST /buckets/b/counters/c 10

57 KV Counters GET /buckets/b/counters/c => 5 POST /buckets/b/counters/c 10 GET /buckets/b/keys/c

58 KV Counters GET /buckets/b/counters/c => 5 POST /buckets/b/counters/c 10 GET /buckets/b/keys/c Stop doing that.

59 Demo!

60 #crdt #riak

Eventual Consistency for CRDTs

Eventual Consistency for CRDTs Eventual Consistency for CRDTs Radha Jagadeesan DePaul University Chicago, USA ESOP 2018 James Riely 1/22 CRDTs? 2/22 CRDTs? C = blah blah R = mumble DT = Data Type 2/22 Data Type An abstract data type

More information

Do we have a quorum?

Do we have a quorum? Do we have a quorum? Quorum Systems Given a set U of servers, U = n: A quorum system is a set Q 2 U such that Q 1, Q 2 Q : Q 1 Q 2 Each Q in Q is a quorum How quorum systems work: A read/write shared register

More information

I Can t Believe It s Not Causal! Scalable Causal Consistency with No Slowdown Cascades

I Can t Believe It s Not Causal! Scalable Causal Consistency with No Slowdown Cascades I Can t Believe It s Not Causal! Scalable Causal Consistency with No Slowdown Cascades Syed Akbar Mehdi 1, Cody Littley 1, Natacha Crooks 1, Lorenzo Alvisi 1,4, Nathan Bronson 2, Wyatt Lloyd 3 1 UT Austin,

More information

Time. To do. q Physical clocks q Logical clocks

Time. To do. q Physical clocks q Logical clocks Time To do q Physical clocks q Logical clocks Events, process states and clocks A distributed system A collection P of N single-threaded processes (p i, i = 1,, N) without shared memory The processes in

More information

Causal Consistency for Geo-Replicated Cloud Storage under Partial Replication

Causal Consistency for Geo-Replicated Cloud Storage under Partial Replication Causal Consistency for Geo-Replicated Cloud Storage under Partial Replication Min Shen, Ajay D. Kshemkalyani, TaYuan Hsu University of Illinois at Chicago Min Shen, Ajay D. Kshemkalyani, TaYuan Causal

More information

CS 484 Data Mining. Association Rule Mining 2

CS 484 Data Mining. Association Rule Mining 2 CS 484 Data Mining Association Rule Mining 2 Review: Reducing Number of Candidates Apriori principle: If an itemset is frequent, then all of its subsets must also be frequent Apriori principle holds due

More information

Agreement. Today. l Coordination and agreement in group communication. l Consensus

Agreement. Today. l Coordination and agreement in group communication. l Consensus Agreement Today l Coordination and agreement in group communication l Consensus Events and process states " A distributed system a collection P of N singlethreaded processes w/o shared memory Each process

More information

Clocks in Asynchronous Systems

Clocks in Asynchronous Systems Clocks in Asynchronous Systems The Internet Network Time Protocol (NTP) 8 Goals provide the ability to externally synchronize clients across internet to UTC provide reliable service tolerating lengthy

More information

Warm-Up Problem. Is the following true or false? 1/35

Warm-Up Problem. Is the following true or false? 1/35 Warm-Up Problem Is the following true or false? 1/35 Propositional Logic: Resolution Carmen Bruni Lecture 6 Based on work by J Buss, A Gao, L Kari, A Lubiw, B Bonakdarpour, D Maftuleac, C Roberts, R Trefler,

More information

Time. Today. l Physical clocks l Logical clocks

Time. Today. l Physical clocks l Logical clocks Time Today l Physical clocks l Logical clocks Events, process states and clocks " A distributed system a collection P of N singlethreaded processes without shared memory Each process p i has a state s

More information

Random Number Generator Digital Design - Demo

Random Number Generator Digital Design - Demo Understanding Digital Design The Digital Electronics 2014 Digital Design - Demo This presentation will Review the oard Game Counter block diagram. Review the circuit design of the sequential logic section

More information

Distributed Systems. 06. Logical clocks. Paul Krzyzanowski. Rutgers University. Fall 2017

Distributed Systems. 06. Logical clocks. Paul Krzyzanowski. Rutgers University. Fall 2017 Distributed Systems 06. Logical clocks Paul Krzyzanowski Rutgers University Fall 2017 2014-2017 Paul Krzyzanowski 1 Logical clocks Assign sequence numbers to messages All cooperating processes can agree

More information

7. Queueing Systems. 8. Petri nets vs. State Automata

7. Queueing Systems. 8. Petri nets vs. State Automata Petri Nets 1. Finite State Automata 2. Petri net notation and definition (no dynamics) 3. Introducing State: Petri net marking 4. Petri net dynamics 5. Capacity Constrained Petri nets 6. Petri net models

More information

Lecture 13: Sequential Circuits, FSM

Lecture 13: Sequential Circuits, FSM Lecture 13: Sequential Circuits, FSM Today s topics: Sequential circuits Finite state machines Reminder: midterm on Tue 2/28 will cover Chapters 1-3, App A, B if you understand all slides, assignments,

More information

DATA MINING LECTURE 3. Frequent Itemsets Association Rules

DATA MINING LECTURE 3. Frequent Itemsets Association Rules DATA MINING LECTURE 3 Frequent Itemsets Association Rules This is how it all started Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases.

More information

CptS 464/564 Fall Prof. Dave Bakken. Cpt. S 464/564 Lecture January 26, 2014

CptS 464/564 Fall Prof. Dave Bakken. Cpt. S 464/564 Lecture January 26, 2014 Overview of Ordering and Logical Time Prof. Dave Bakken Cpt. S 464/564 Lecture January 26, 2014 Context This material is NOT in CDKB5 textbook Rather, from second text by Verissimo and Rodrigues, chapters

More information

Our Problem. Model. Clock Synchronization. Global Predicate Detection and Event Ordering

Our Problem. Model. Clock Synchronization. Global Predicate Detection and Event Ordering Our Problem Global Predicate Detection and Event Ordering To compute predicates over the state of a distributed application Model Clock Synchronization Message passing No failures Two possible timing assumptions:

More information

Geodatabase Best Practices. Dave Crawford Erik Hoel

Geodatabase Best Practices. Dave Crawford Erik Hoel Geodatabase Best Practices Dave Crawford Erik Hoel Geodatabase best practices - outline Geodatabase creation Data ownership Data model Data configuration Geodatabase behaviors Data integrity and validation

More information

CS 584 Data Mining. Association Rule Mining 2

CS 584 Data Mining. Association Rule Mining 2 CS 584 Data Mining Association Rule Mining 2 Recall from last time: Frequent Itemset Generation Strategies Reduce the number of candidates (M) Complete search: M=2 d Use pruning techniques to reduce M

More information

Propositional Resolution Part 1. Short Review Professor Anita Wasilewska CSE 352 Artificial Intelligence

Propositional Resolution Part 1. Short Review Professor Anita Wasilewska CSE 352 Artificial Intelligence Propositional Resolution Part 1 Short Review Professor Anita Wasilewska CSE 352 Artificial Intelligence SYNTAX dictionary Literal any propositional VARIABLE a or negation of a variable a, a VAR, Example

More information

Failure detectors Introduction CHAPTER

Failure detectors Introduction CHAPTER CHAPTER 15 Failure detectors 15.1 Introduction This chapter deals with the design of fault-tolerant distributed systems. It is widely known that the design and verification of fault-tolerent distributed

More information

Models for representing sequential circuits

Models for representing sequential circuits Sequential Circuits Models for representing sequential circuits Finite-state machines (Moore and Mealy) Representation of memory (states) Changes in state (transitions) Design procedure State diagrams

More information

Data Mining: Concepts and Techniques. (3 rd ed.) Chapter 6

Data Mining: Concepts and Techniques. (3 rd ed.) Chapter 6 Data Mining: Concepts and Techniques (3 rd ed.) Chapter 6 Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University 2013 Han, Kamber & Pei. All rights

More information

Experimental design (DOE) - Design

Experimental design (DOE) - Design Experimental design (DOE) - Design Menu: QCExpert Experimental Design Design Full Factorial Fract Factorial This module designs a two-level multifactorial orthogonal plan 2 n k and perform its analysis.

More information

Boolean Algebra. Digital Logic Appendix A. Postulates, Identities in Boolean Algebra How can I manipulate expressions?

Boolean Algebra. Digital Logic Appendix A. Postulates, Identities in Boolean Algebra How can I manipulate expressions? Digital Logic Appendix A Gates Combinatorial Circuits Sequential Circuits Other operations NAND A NAND B = NOT ( A ANDB) = AB NOR A NOR B = NOT ( A ORB) = A + B Truth tables What is the result of the operation

More information

Data Mining and Analysis: Fundamental Concepts and Algorithms

Data Mining and Analysis: Fundamental Concepts and Algorithms Data Mining and Analysis: Fundamental Concepts and Algorithms dataminingbook.info Mohammed J. Zaki 1 Wagner Meira Jr. 2 1 Department of Computer Science Rensselaer Polytechnic Institute, Troy, NY, USA

More information

D B M G Data Base and Data Mining Group of Politecnico di Torino

D B M G Data Base and Data Mining Group of Politecnico di Torino Data Base and Data Mining Group of Politecnico di Torino Politecnico di Torino Association rules Objective extraction of frequent correlations or pattern from a transactional database Tickets at a supermarket

More information

Exercises Stochastic Performance Modelling. Hamilton Institute, Summer 2010

Exercises Stochastic Performance Modelling. Hamilton Institute, Summer 2010 Exercises Stochastic Performance Modelling Hamilton Institute, Summer Instruction Exercise Let X be a non-negative random variable with E[X ]

More information

Formal Verification of Mobile Network Protocols

Formal Verification of Mobile Network Protocols Dipartimento di Informatica, Università di Pisa, Italy milazzo@di.unipi.it Pisa April 26, 2005 Introduction Modelling Systems Specifications Examples Algorithms Introduction Design validation ensuring

More information

Boolean Algebra. Digital Logic Appendix A. Boolean Algebra Other operations. Boolean Algebra. Postulates, Identities in Boolean Algebra

Boolean Algebra. Digital Logic Appendix A. Boolean Algebra Other operations. Boolean Algebra. Postulates, Identities in Boolean Algebra Digital Logic Appendix A Gates Combinatorial Circuits Sequential Circuits George Boole ideas 1854 Claude Shannon, apply to circuit design, 1938 (piirisuunnittelu) Describe digital circuitry function programming

More information

Association Rules. Fundamentals

Association Rules. Fundamentals Politecnico di Torino Politecnico di Torino 1 Association rules Objective extraction of frequent correlations or pattern from a transactional database Tickets at a supermarket counter Association rule

More information

Discrete-event simulations

Discrete-event simulations Discrete-event simulations Lecturer: Dmitri A. Moltchanov E-mail: moltchan@cs.tut.fi http://www.cs.tut.fi/kurssit/elt-53606/ OUTLINE: Why do we need simulations? Step-by-step simulations; Classifications;

More information

D B M G. Association Rules. Fundamentals. Fundamentals. Elena Baralis, Silvia Chiusano. Politecnico di Torino 1. Definitions.

D B M G. Association Rules. Fundamentals. Fundamentals. Elena Baralis, Silvia Chiusano. Politecnico di Torino 1. Definitions. Definitions Data Base and Data Mining Group of Politecnico di Torino Politecnico di Torino Itemset is a set including one or more items Example: {Beer, Diapers} k-itemset is an itemset that contains k

More information

Association Rules Information Retrieval and Data Mining. Prof. Matteo Matteucci

Association Rules Information Retrieval and Data Mining. Prof. Matteo Matteucci Association Rules Information Retrieval and Data Mining Prof. Matteo Matteucci Learning Unsupervised Rules!?! 2 Market-Basket Transactions 3 Bread Peanuts Milk Fruit Jam Bread Jam Soda Chips Milk Fruit

More information

Goal. Partially-ordered set. Game plan 2/2/2013. Solving fixpoint equations

Goal. Partially-ordered set. Game plan 2/2/2013. Solving fixpoint equations Goal Solving fixpoint equations Many problems in programming languages can be formulated as the solution of a set of mutually recursive equations: D: set, f,g:dxd D x = f(x,y) y = g(x,y) Examples Parsing:

More information

D B M G. Association Rules. Fundamentals. Fundamentals. Association rules. Association rule mining. Definitions. Rule quality metrics: example

D B M G. Association Rules. Fundamentals. Fundamentals. Association rules. Association rule mining. Definitions. Rule quality metrics: example Association rules Data Base and Data Mining Group of Politecnico di Torino Politecnico di Torino Objective extraction of frequent correlations or pattern from a transactional database Tickets at a supermarket

More information

Portal for ArcGIS: An Introduction. Catherine Hynes and Derek Law

Portal for ArcGIS: An Introduction. Catherine Hynes and Derek Law Portal for ArcGIS: An Introduction Catherine Hynes and Derek Law Agenda Web GIS pattern Product overview Installation and deployment Configuration options Security options and groups Portal for ArcGIS

More information

ALU, Latches and Flip-Flops

ALU, Latches and Flip-Flops CSE14: Components and Design Techniques for Digital Systems ALU, Latches and Flip-Flops Tajana Simunic Rosing Where we are. Last time: ALUs Plan for today: ALU example, latches and flip flops Exam #1 grades

More information

Specification and Complexity of Collaborative Text Editing

Specification and Complexity of Collaborative Text Editing Specification and Complexity of Collaborative Text Editing Hagit Attiya Technion Adam Morrison Technion Sebastian Burckhardt Microsoft Research Hongseok Yang University of Oxford Alexey Gotsman IMDEA Software

More information

Lecture 2: Divide and conquer and Dynamic programming

Lecture 2: Divide and conquer and Dynamic programming Chapter 2 Lecture 2: Divide and conquer and Dynamic programming 2.1 Divide and Conquer Idea: - divide the problem into subproblems in linear time - solve subproblems recursively - combine the results in

More information

Distributed Systems Principles and Paradigms. Chapter 06: Synchronization

Distributed Systems Principles and Paradigms. Chapter 06: Synchronization Distributed Systems Principles and Paradigms Maarten van Steen VU Amsterdam, Dept. Computer Science Room R4.20, steen@cs.vu.nl Chapter 06: Synchronization Version: November 16, 2009 2 / 39 Contents Chapter

More information

Slides for Chapter 14: Time and Global States

Slides for Chapter 14: Time and Global States Slides for Chapter 14: Time and Global States From Coulouris, Dollimore, Kindberg and Blair Distributed Systems: Concepts and Design Edition 5, Addison-Wesley 2012 Overview of Chapter Introduction Clocks,

More information

Distributed Systems Principles and Paradigms

Distributed Systems Principles and Paradigms Distributed Systems Principles and Paradigms Chapter 6 (version April 7, 28) Maarten van Steen Vrije Universiteit Amsterdam, Faculty of Science Dept. Mathematics and Computer Science Room R4.2. Tel: (2)

More information

arxiv: v1 [cs.dc] 22 Oct 2018

arxiv: v1 [cs.dc] 22 Oct 2018 FANTOM: A SCALABLE FRAMEWORK FOR ASYNCHRONOUS DISTRIBUTED SYSTEMS A PREPRINT Sang-Min Choi, Jiho Park, Quan Nguyen, and Andre Cronje arxiv:1810.10360v1 [cs.dc] 22 Oct 2018 FANTOM Lab FANTOM Foundation

More information

Basics HTTP. requests. Ryan E. Freckleton. PySprings. May 24, 2016

Basics HTTP. requests. Ryan E. Freckleton. PySprings. May 24, 2016 PySprings May 24, 2016 >>> import r e q u e s t s >>> r = r e q u e s t s. g e t ( h t t p s : / / a p i. g i t h u b. com/ u s e r, auth=( u s e r, p a s s ) ) >>> r. status_code 401 >>> r. h e a d e

More information

The File Geodatabase API. Craig Gillgrass Lance Shipman

The File Geodatabase API. Craig Gillgrass Lance Shipman The File Geodatabase API Craig Gillgrass Lance Shipman Schedule Cell phones and pagers Please complete the session survey we take your feedback very seriously! Overview File Geodatabase API - Introduction

More information

Leveraging ArcGIS Online Elevation and Hydrology Services. Steve Kopp, Jian Lange

Leveraging ArcGIS Online Elevation and Hydrology Services. Steve Kopp, Jian Lange Leveraging ArcGIS Online Elevation and Hydrology Services Steve Kopp, Jian Lange Topics An overview of ArcGIS Online Elevation Analysis Using Elevation Analysis Services in ArcGIS for Desktop Using Elevation

More information

Propositional Resolution Introduction

Propositional Resolution Introduction Propositional Resolution Introduction (Nilsson Book Handout) Professor Anita Wasilewska CSE 352 Artificial Intelligence Propositional Resolution Part 1 SYNTAX dictionary Literal any propositional VARIABLE

More information

CS 425 / ECE 428 Distributed Systems Fall Indranil Gupta (Indy) Oct. 5, 2017 Lecture 12: Time and Ordering All slides IG

CS 425 / ECE 428 Distributed Systems Fall Indranil Gupta (Indy) Oct. 5, 2017 Lecture 12: Time and Ordering All slides IG CS 425 / ECE 428 Distributed Systems Fall 2017 Indranil Gupta (Indy) Oct. 5, 2017 Lecture 12: Time and Ordering All slides IG Why Synchronization? You want to catch a bus at 6.05 pm, but your watch is

More information

Digital Logic Appendix A

Digital Logic Appendix A Digital Logic Appendix A Boolean Algebra Gates Combinatorial Circuits Sequential Circuits 1 Boolean Algebra George Boole ideas 1854 Claude Shannon, apply to circuit design, 1938 Describe digital circuitry

More information

MAD. Models & Algorithms for Distributed systems -- 2/5 -- download slides at

MAD. Models & Algorithms for Distributed systems -- 2/5 -- download slides at MAD Models & Algorithms for Distributed systems -- /5 -- download slides at http://people.rennes.inria.fr/eric.fabre/ 1 Today Runs/executions of a distributed system are partial orders of events We introduce

More information

Lecture 13: Sequential Circuits, FSM

Lecture 13: Sequential Circuits, FSM Lecture 13: Sequential Circuits, FSM Today s topics: Sequential circuits Finite state machines 1 Clocks A microprocessor is composed of many different circuits that are operating simultaneously if each

More information

CSE140: Components and Design Techniques for Digital Systems. Midterm Information. Instructor: Mohsen Imani. Sources: TSR, Katz, Boriello & Vahid

CSE140: Components and Design Techniques for Digital Systems. Midterm Information. Instructor: Mohsen Imani. Sources: TSR, Katz, Boriello & Vahid CSE140: Components and Design Techniques for Digital Systems Midterm Information Instructor: Mohsen Imani Midterm Topics In general: everything that was covered in homework 1 and 2 and related lectures,

More information

Leveraging Web GIS: An Introduction to the ArcGIS portal

Leveraging Web GIS: An Introduction to the ArcGIS portal Leveraging Web GIS: An Introduction to the ArcGIS portal Derek Law Product Management DLaw@esri.com Agenda Web GIS pattern Product overview Installation and deployment Configuration options Security options

More information

DATA MINING - 1DL360

DATA MINING - 1DL360 DATA MINING - 1DL36 Fall 212" An introductory class in data mining http://www.it.uu.se/edu/course/homepage/infoutv/ht12 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology, Uppsala

More information

Coordination. Failures and Consensus. Consensus. Consensus. Overview. Properties for Correct Consensus. Variant I: Consensus (C) P 1. v 1.

Coordination. Failures and Consensus. Consensus. Consensus. Overview. Properties for Correct Consensus. Variant I: Consensus (C) P 1. v 1. Coordination Failures and Consensus If the solution to availability and scalability is to decentralize and replicate functions and data, how do we coordinate the nodes? data consistency update propagation

More information

Distributed Algorithms Time, clocks and the ordering of events

Distributed Algorithms Time, clocks and the ordering of events Distributed Algorithms Time, clocks and the ordering of events Alberto Montresor University of Trento, Italy 2016/04/26 This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International

More information

By that, every path can be analyzed :-) A given program may admit several paths :-( For any given input, another path may be chosen :-((

By that, every path can be analyzed :-) A given program may admit several paths :-( For any given input, another path may be chosen :-(( [x = M[e];] A = (A {e})\expr x [M[e 1 ] = e 2 ;] A = A {e 1,e 2 } By that, every path can be analyzed :-) A given program may admit several paths :-( For any given input, another path may be chosen :-((

More information

Big Data Infrastructure CS 489/698 Big Data Infrastructure (Winter 2016)

Big Data Infrastructure CS 489/698 Big Data Infrastructure (Winter 2016) Big Data Infrastructure CS 489/698 Big Data Infrastructure (Winter 2016) Week 12: Real-Time Data Analytics (2/2) March 31, 2016 Jimmy Lin David R. Cheriton School of Computer Science University of Waterloo

More information

CS425: Algorithms for Web Scale Data

CS425: Algorithms for Web Scale Data CS425: Algorithms for Web Scale Data Most of the slides are from the Mining of Massive Datasets book. These slides have been modified for CS425. The original slides can be accessed at: www.mmds.org Challenges

More information

Sequential logic and design

Sequential logic and design Principles Of Digital Design Sequential logic and design Analysis State-based (Moore) Input-based (Mealy) FSM definition Synthesis State minimization Encoding Optimization and timing Copyright 20-20by

More information

Equivalence Checking of Sequential Circuits

Equivalence Checking of Sequential Circuits Equivalence Checking of Sequential Circuits Sanjit Seshia EECS UC Berkeley With thanks to K. Keutzer, R. Rutenbar 1 Today s Lecture What we know: How to check two combinational circuits for equivalence

More information

Scalar Optimisation Part 2

Scalar Optimisation Part 2 Scalar Optimisation Part 2 Michael O Boyle January 2014 1 Course Structure L1 Introduction and Recap 4-5 lectures on classical optimisation 2 lectures on scalar optimisation Last lecture on redundant expressions

More information

Lecture Notes for Chapter 6. Introduction to Data Mining

Lecture Notes for Chapter 6. Introduction to Data Mining Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004

More information

CS 5014: Research Methods in Computer Science

CS 5014: Research Methods in Computer Science Computer Science Clifford A. Shaffer Department of Computer Science Virginia Tech Blacksburg, Virginia Fall 2010 Copyright c 2010 by Clifford A. Shaffer Computer Science Fall 2010 1 / 254 Experimental

More information

Dataflow analysis. Theory and Applications. cs6463 1

Dataflow analysis. Theory and Applications. cs6463 1 Dataflow analysis Theory and Applications cs6463 1 Control-flow graph Graphical representation of runtime control-flow paths Nodes of graph: basic blocks (straight-line computations) Edges of graph: flows

More information

Section 6 Fault-Tolerant Consensus

Section 6 Fault-Tolerant Consensus Section 6 Fault-Tolerant Consensus CS586 - Panagiota Fatourou 1 Description of the Problem Consensus Each process starts with an individual input from a particular value set V. Processes may fail by crashing.

More information

CS505: Distributed Systems

CS505: Distributed Systems Department of Computer Science CS505: Distributed Systems Lecture 5: Time in Distributed Systems Overview Time and Synchronization Logical Clocks Vector Clocks Distributed Systems Asynchronous systems:

More information

Information-Theoretic Lower Bounds on the Storage Cost of Shared Memory Emulation

Information-Theoretic Lower Bounds on the Storage Cost of Shared Memory Emulation Information-Theoretic Lower Bounds on the Storage Cost of Shared Memory Emulation Viveck R. Cadambe EE Department, Pennsylvania State University, University Park, PA, USA viveck@engr.psu.edu Nancy Lynch

More information

Geodatabase: Best Practices. Robert LeClair, Senior Instructor

Geodatabase: Best Practices. Robert LeClair, Senior Instructor Geodatabase: Best Practices Robert LeClair, Senior Instructor Agenda Geodatabase Creation Data Ownership Data Model Data Configuration Geodatabase Behaviors Data Validation Extending Performance Geodatabase

More information

Making Geo-Replicated Systems Fast when Possible, Consistent if Necessary

Making Geo-Replicated Systems Fast when Possible, Consistent if Necessary Making Geo-Replicated Systems Fast when Possible, Consistent if Necessary Rodrigo Rodrigues NOVA University of Lisbon Joint work with Allen Clement, Johannes Gehrke, Cheng Li, Daniel Porto and Nuno Preguiça

More information

DATA MINING - 1DL360

DATA MINING - 1DL360 DATA MINING - DL360 Fall 200 An introductory class in data mining http://www.it.uu.se/edu/course/homepage/infoutv/ht0 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology, Uppsala

More information

Design of discrete-event simulations

Design of discrete-event simulations Design of discrete-event simulations Lecturer: Dmitri A. Moltchanov E-mail: moltchan@cs.tut.fi http://www.cs.tut.fi/kurssit/tlt-2707/ OUTLINE: Discrete event simulation; Event advance design; Unit-time

More information

Maximal Antichain Lattice Algorithms for Distributed Computatio

Maximal Antichain Lattice Algorithms for Distributed Computatio Maximal Antichain Lattice Algorithms for Distributed Computations Vijay K. Garg Parallel and Distributed Systems Lab, Department of Electrical and Computer Engineering, The University of Texas at Austin,

More information

Introduction to Randomized Algorithms III

Introduction to Randomized Algorithms III Introduction to Randomized Algorithms III Joaquim Madeira Version 0.1 November 2017 U. Aveiro, November 2017 1 Overview Probabilistic counters Counting with probability 1 / 2 Counting with probability

More information

The Weakest Failure Detector to Solve Mutual Exclusion

The Weakest Failure Detector to Solve Mutual Exclusion The Weakest Failure Detector to Solve Mutual Exclusion Vibhor Bhatt Nicholas Christman Prasad Jayanti Dartmouth College, Hanover, NH Dartmouth Computer Science Technical Report TR2008-618 April 17, 2008

More information

CSC9R6 Computer Design. Practical Digital Logic

CSC9R6 Computer Design. Practical Digital Logic CSC9R6 Computer Design Practical Digital Logic 1 References (for this part of CSC9R6) Hamacher et al: Computer Organization App A. In library Floyd: Digital Fundamentals Ch 1, 3-6, 8-10 web page: www.prenhall.com/floyd/

More information

Boolean Algebra and Digital Logic 2009, University of Colombo School of Computing

Boolean Algebra and Digital Logic 2009, University of Colombo School of Computing IT 204 Section 3.0 Boolean Algebra and Digital Logic Boolean Algebra 2 Logic Equations to Truth Tables X = A. B + A. B + AB A B X 0 0 0 0 3 Sum of Products The OR operation performed on the products of

More information

Data Mining Concepts & Techniques

Data Mining Concepts & Techniques Data Mining Concepts & Techniques Lecture No. 04 Association Analysis Naeem Ahmed Email: naeemmahoto@gmail.com Department of Software Engineering Mehran Univeristy of Engineering and Technology Jamshoro

More information

Functional Dependencies and Normalization

Functional Dependencies and Normalization Functional Dependencies and Normalization There are many forms of constraints on relational database schemata other than key dependencies. Undoubtedly most important is the functional dependency. A functional

More information

Temporal Data Mining

Temporal Data Mining Temporal Data Mining Christian Moewes cmoewes@ovgu.de Otto-von-Guericke University of Magdeburg Faculty of Computer Science Department of Knowledge Processing and Language Engineering Zittau Fuzzy Colloquium

More information

ESTIMATION METHODS FOR MISSING DATA IN UN-REPLICATED 2 FACTORIAL AND 2 FRACTIONAL FACTORIAL DESIGNS

ESTIMATION METHODS FOR MISSING DATA IN UN-REPLICATED 2 FACTORIAL AND 2 FRACTIONAL FACTORIAL DESIGNS Journal of Statistics: Advances in Theory and Applications Volume 5, Number 2, 2011, Pages 131-147 ESTIMATION METHODS FOR MISSING DATA IN k k p UN-REPLICATED 2 FACTORIAL AND 2 FRACTIONAL FACTORIAL DESIGNS

More information

ECEN 248: INTRODUCTION TO DIGITAL SYSTEMS DESIGN. Week 9 Dr. Srinivas Shakkottai Dept. of Electrical and Computer Engineering

ECEN 248: INTRODUCTION TO DIGITAL SYSTEMS DESIGN. Week 9 Dr. Srinivas Shakkottai Dept. of Electrical and Computer Engineering ECEN 248: INTRODUCTION TO DIGITAL SYSTEMS DESIGN Week 9 Dr. Srinivas Shakkottai Dept. of Electrical and Computer Engineering TIMING ANALYSIS Overview Circuits do not respond instantaneously to input changes

More information

Frequent Itemset Mining

Frequent Itemset Mining Frequent Itemset Mining prof. dr Arno Siebes Algorithmic Data Analysis Group Department of Information and Computing Sciences Universiteit Utrecht Battling Size The previous time we saw that Big Data has

More information

Midterm1 Review. Jan 24 Armita

Midterm1 Review. Jan 24 Armita Midterm1 Review Jan 24 Armita Outline Boolean Algebra Axioms closure, Identity elements, complements, commutativity, distributivity theorems Associativity, Duality, De Morgan, Consensus theorem Shannon

More information

Simplification of finite automata

Simplification of finite automata Simplification of finite automata Lorenzo Clemente (University of Warsaw) based on joint work with Richard Mayr (University of Edinburgh) Warsaw, November 2016 Nondeterministic finite automata We consider

More information

Section 3.3: Discrete-Event Simulation Examples

Section 3.3: Discrete-Event Simulation Examples Section 33: Discrete-Event Simulation Examples Discrete-Event Simulation: A First Course c 2006 Pearson Ed, Inc 0-13-142917-5 Discrete-Event Simulation: A First Course Section 33: Discrete-Event Simulation

More information

Integrating External and Internal Clock Synchronization. Christof Fetzer and Flaviu Cristian. Department of Computer Science & Engineering

Integrating External and Internal Clock Synchronization. Christof Fetzer and Flaviu Cristian. Department of Computer Science & Engineering Integrating External and Internal Clock Synchronization Christof Fetzer and Flaviu Cristian Department of Computer Science & Engineering University of California, San Diego La Jolla, CA 9093?0114 e-mail:

More information

Dynamic Group Communication

Dynamic Group Communication Dynamic Group Communication André Schiper Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne, Switzerland e-mail: andre.schiper@epfl.ch Abstract Group communication is the basic infrastructure

More information

Description of the ED library Basic Atoms

Description of the ED library Basic Atoms Description of the ED library Basic Atoms Simulation Software / Description of the ED library BASIC ATOMS Enterprise Dynamics Copyright 2010 Incontrol Simulation Software B.V. All rights reserved Papendorpseweg

More information

PRUNING PROCEDURE FOR INCOMPLETE-OBSERVATION DIAGNOSTIC MODEL SIMPLIFICATION

PRUNING PROCEDURE FOR INCOMPLETE-OBSERVATION DIAGNOSTIC MODEL SIMPLIFICATION PRUNING PROCEDURE FOR INCOMPLETE-OBSERVATION DIAGNOSTIC MODEL SIMPLIFICATION Ivan Havel Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University Karlovo náměstí 13, 121 35

More information

Enabling Web GIS. Dal Hunter Jeff Shaner

Enabling Web GIS. Dal Hunter Jeff Shaner Enabling Web GIS Dal Hunter Jeff Shaner Enabling Web GIS In Your Infrastructure Agenda Quick Overview Web GIS Deployment Server GIS Deployment Security and Identity Management Web GIS Operations Web GIS

More information

Better Living Through Statistics Monitoring Doesn't (Have To) Suck

Better Living Through Statistics Monitoring Doesn't (Have To) Suck Better Living Through Statistics Monitoring Doesn't (Have To) Suck Jamie Wilkinson Site Reliability Engineer, Google jaq@{spacepants.org,google.com} @jaqpants #monitoringsucks I love monitoring. This hashtag

More information

Chapter 11 Time and Global States

Chapter 11 Time and Global States CSD511 Distributed Systems 分散式系統 Chapter 11 Time and Global States 吳俊興 國立高雄大學資訊工程學系 Chapter 11 Time and Global States 11.1 Introduction 11.2 Clocks, events and process states 11.3 Synchronizing physical

More information

Asymptotic Analysis. Slides by Carl Kingsford. Jan. 27, AD Chapter 2

Asymptotic Analysis. Slides by Carl Kingsford. Jan. 27, AD Chapter 2 Asymptotic Analysis Slides by Carl Kingsford Jan. 27, 2014 AD Chapter 2 Independent Set Definition (Independent Set). Given a graph G = (V, E) an independent set is a set S V if no two nodes in S are joined

More information

Spatial information sharing technology based on Grid

Spatial information sharing technology based on Grid Spatial information sharing technology based on Grid Hong-bin ZHANG a,b,c,1, Bao-rui CHEN a,b,c,3,gang LI b,c,2, Xiao-ping XIN a,b,c,* a Hulunber Grassland Ecosystem Observation and Research Station, Beijing

More information

Basic Data Structures and Algorithms for Data Profiling Felix Naumann

Basic Data Structures and Algorithms for Data Profiling Felix Naumann Basic Data Structures and Algorithms for 8.5.2017 Overview 1. The lattice 2. Apriori lattice traversal 3. Position List Indices 4. Bloom filters Slides with Thorsten Papenbrock 2 Definitions Lattice Partially

More information

arxiv: v2 [cs.db] 24 Jul 2012

arxiv: v2 [cs.db] 24 Jul 2012 On the Relative Trust between Inconsistent Data and Inaccurate Constraints George Beskales 1 Ihab F. Ilyas 1 Lukasz Golab 2 Artur Galiullin 2 1 Qatar Computing Research Institute 2 University of Waterloo

More information

DIGITAL LOGIC CIRCUITS

DIGITAL LOGIC CIRCUITS DIGITAL LOGIC CIRCUITS Digital logic circuits BINARY NUMBER SYSTEM electronic circuits that handle information encoded in binary form (deal with signals that have only two values, and ) Digital. computers,

More information

Scalable Failure Recovery for Tree-based Overlay Networks

Scalable Failure Recovery for Tree-based Overlay Networks Scalable Failure Recovery for Tree-based Overlay Networks Dorian C. Arnold University of Wisconsin Paradyn/Condor Week April 30 May 3, 2007 Madison, WI Overview Motivation Address the likely frequent failures

More information