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An Aggregate Computing Approach to Self-Stabilizing Leader Election
An Aggregate Computing Approach to Self-Stabilizing Leader Election Yuanqiu Mo Jacob Beal Soura Dasgupta University of Iowa Raytheon BBN Technologies University of Iowa⇤ Iowa City, Iowa 52242 Cambridge, MA, USA 02138 Iowa City, Iowa 52242 Email: [email protected] Email: [email protected] Email: [email protected] Abstract—Leader election is one of the core coordination 4" problems of distributed systems, and has been addressed in 3" 1" many different ways suitable for different classes of systems. It is unclear, however, whether existing methods will be effective 7" 1" for resilient device coordination in open, complex, networked 3" distributed systems like smart cities, tactical networks, personal 3" 0" networks and the Internet of Things (IoT). Aggregate computing 2" provides a layered approach to developing such systems, in which resilience is provided by a layer comprising a set of (a) G block (b) C block (c) T block adaptive algorithms whose compositions have been shown to cover a large class of coordination activities. In this paper, Fig. 1. Illustration of three basis block operators: (a) information-spreading (G), (b) information aggregation (C), and (c) temporary state (T) we show how a feedback interconnection of these basis set algorithms can perform distributed leader election resilient to device topology and position changes. We also characterize a key design parameter that defines some important performance a separation of concerns into multiple abstraction layers, much attributes: Too large a value impairs resilience to loss of existing like the OSI model for communication [2], factoring the leaders, while too small a value leads to multiple leaders. -
A Theory of Fault Recovery for Component-Based Models⋆
A Theory of Fault Recovery for Component-based Models? Borzoo Bonakdarpour1, Marius Bozga2, and Gregor G¨ossler3 1 School of Computer Science, University of Waterloo Email: [email protected] 2 VERIMAG/CNRS, Gieres, France Email: [email protected] 3 INRIA-Grenoble, Montbonnot, France Email: [email protected] Abstract. This paper introduces a theory of fault recovery for component- based models. We specify a model in terms of a set of atomic components incrementally composed and synchronized by a set of glue operators. We define what it means for such models to provide a recovery mechanism, so that the model converges to its normal behavior in the presence of faults (e.g., in self-stabilizing systems). We present a sufficient condition for in- crementally composing components to obtain models that provide fault recovery. We identify corrector components whose presence in a model is essential to guarantee recovery after the occurrence of faults. We also for- malize component-based models that effectively separate recovery from functional concerns. We also show that any model that provides fault recovery can be transformed into an equivalent model, where functional and recovery tasks are modularized in different components. Keywords: Fault-tolerance; Transformation; Separation of concerns; BIP 1 Introduction Fault-tolerance has always been an active line of research in design and imple- mentation of dependable systems. Intuitively, tolerating faults involves providing a system with the means to handle unexpected defects, so that the system meets its specification even in the presence of faults. In this context, the notion of spec- ification may vary depending upon the guarantees that the system must deliver in the presence of faults. -
(Perry & Wolf 92) Software Architecture (Garlan & Shaw
ICS 221, Winter 2001 Software Architecture Software Architecture (Perry & Wolf 92) “Architecture is concerned with the selection of architectural elements, their interactions, and the Software Architecture constraints on those elements and their interactions necessary to provide a framework in which to satisfy the requirements and serve as a basis for the design.” David S. Rosenblum ICS 221 “Design is concerned with the modularization and detailed interfaces of the design elements, their Winter 2001 algorithms and procedures, and the data types needed to support the architecture and to satisfy the requirements.” Software Architecture Software Architecture (Garlan & Shaw 93) (Shaw & Garlan 96) “Software architecture is a level of design that goes “The architecture of a software system defines beyond the algorithms and data structures of the that system in terms of computational computation; designing and specifying the overall components and interactions among those system structure emerges as a new kind of problem. Structural issues include gross organization and components. … In addition to specifying the global control structure; protocols for communication, structure and topology of the system, the synchronization, and data access; assignment of architecture shows the correspondence functionality to design elements; physical between the requirements and elements of distribution; composition of design elements; scaling the constructed system, thereby providing and performance; and selection among design some rationale for the design decisions.” alternatives.” Analogies with Differences Between Civil and Civil Architecture Software Architecture Civil Engineering and Civil Architecture “Software systems are like cathedrals—first we are concerned with the engineering and design of build them and then we pray.” civic structures (roads, buildings, bridges, etc.) — Sam Redwine ! Multiple views ! Civil: Artist renderings, elevations, floor plans, blueprints ! Physical vs. -
Probabilistic Models for the Guarded Command Language
Science of Computer Programming ELSEVIER Science of Computer Programming 28 (1997) 171-192 Probabilistic models for the guarded command language He Jifeng *, K. Seidel, A. McIver Oxford University Computing Laboratory, Programming Research Group, 11 Keble. Road, Oxford OXI 3QD, UK Abstract The two models presented in this paper provide two different semantics for an extension of Dijkstra’s language of guarded commands. The extended language has an additional operator, namely probabilistic choice, which makes it possible to express randomized algorithms. An earlier model by Claire Jones included probabilistic choice but not non-determinism, which meant that it could not be used for the development of algorithms from specifications. Our second model is built on top of Claire Jones’ model, using a general method of extending a probabilistic cpo to one which abo contains non-determinism. The first model was constructed from scratch, as it were, guided only by the desire for certain algebraic properties of the language constructs, which we found lacking in the second model. We compare and contrast the properties of the two models both by giving examples and by constructing mappings between them and the non-probabilistic model. On the basis of this comparison we argue that, in general, the first model is preferable to the second. @ 1997 Elsevier Science B.V. Keywords: Probabilistic programming language; Semantics; Algebraic laws 1. Introduction Dijkstra’s language of guarded commands with its weakest precondition semantics [l] put reasoning about sequential imperative programs on a secure footing. The aim of this paper is to extend this language, its semantics, and formal reasoning to sequential randomized algorithms. -
7. Control Flow First?
Copyright (C) R.A. van Engelen, FSU Department of Computer Science, 2000-2004 Ordering Program Execution: What is Done 7. Control Flow First? Overview Categories for specifying ordering in programming languages: Expressions 1. Sequencing: the execution of statements and evaluation of Evaluation order expressions is usually in the order in which they appear in a Assignments program text Structured and unstructured flow constructs 2. Selection (or alternation): a run-time condition determines the Goto's choice among two or more statements or expressions Sequencing 3. Iteration: a statement is repeated a number of times or until a Selection run-time condition is met Iteration and iterators 4. Procedural abstraction: subroutines encapsulate collections of Recursion statements and subroutine calls can be treated as single Nondeterminacy statements 5. Recursion: subroutines which call themselves directly or indirectly to solve a problem, where the problem is typically defined in terms of simpler versions of itself 6. Concurrency: two or more program fragments executed in parallel, either on separate processors or interleaved on a single processor Note: Study Chapter 6 of the textbook except Section 7. Nondeterminacy: the execution order among alternative 6.6.2. constructs is deliberately left unspecified, indicating that any alternative will lead to a correct result Expression Syntax Expression Evaluation Ordering: Precedence An expression consists of and Associativity An atomic object, e.g. number or variable The use of infix, prefix, and postfix notation leads to ambiguity An operator applied to a collection of operands (or as to what is an operand of what arguments) which are expressions Fortran example: a+b*c**d**e/f Common syntactic forms for operators: The choice among alternative evaluation orders depends on Function call notation, e.g. -
Structured Programming - Retrospect and Prospect Harlan D
University of Tennessee, Knoxville Trace: Tennessee Research and Creative Exchange The aH rlan D. Mills Collection Science Alliance 11-1986 Structured Programming - Retrospect and Prospect Harlan D. Mills Follow this and additional works at: http://trace.tennessee.edu/utk_harlan Part of the Software Engineering Commons Recommended Citation Mills, Harlan D., "Structured Programming - Retrospect and Prospect" (1986). The Harlan D. Mills Collection. http://trace.tennessee.edu/utk_harlan/20 This Article is brought to you for free and open access by the Science Alliance at Trace: Tennessee Research and Creative Exchange. It has been accepted for inclusion in The aH rlan D. Mills Collection by an authorized administrator of Trace: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. mJNDAMNTL9JNNEPTS IN SOFTWARE ENGINEERING Structured Programming. Retrospect and Prospect Harlan D. Mills, IBM Corp. Stnuctured program- 2 ' dsger W. Dijkstra's 1969 "Struc- mon wisdom that no sizable program Ste red .tured Programming" articlel could be error-free. After, many sizable ming haxs changed ho w precipitated a decade of intense programs have run a year or more with no programs are written focus on programming techniques that has errors detected. since its introduction fundamentally alteredhumanexpectations and achievements in software devel- Impact of structured programming. two decades ago. opment. These expectations and achievements are However, it still has a Before this decade of intense focus, pro- not universal because of the inertia of lot of potentialfor gramming was regarded as a private, industrial practices. But they are well- lot of fo puzzle-solving activity ofwriting computer enough established to herald fundamental more change. -
Synchronization Spinlocks - Semaphores
CS 4410 Operating Systems Synchronization Spinlocks - Semaphores Summer 2013 Cornell University 1 Today ● How can I synchronize the execution of multiple threads of the same process? ● Example ● Race condition ● Critical-Section Problem ● Spinlocks ● Semaphors ● Usage 2 Problem Context ● Multiple threads of the same process have: ● Private registers and stack memory ● Shared access to the remainder of the process “state” ● Preemptive CPU Scheduling: ● The execution of a thread is interrupted unexpectedly. ● Multiple cores executing multiple threads of the same process. 3 Share Counting ● Mr Skroutz wants to count his $1-bills. ● Initially, he uses one thread that increases a variable bills_counter for every $1-bill. ● Then he thought to accelerate the counting by using two threads and keeping the variable bills_counter shared. 4 Share Counting bills_counter = 0 ● Thread A ● Thread B while (machine_A_has_bills) while (machine_B_has_bills) bills_counter++ bills_counter++ print bills_counter ● What it might go wrong? 5 Share Counting ● Thread A ● Thread B r1 = bills_counter r2 = bills_counter r1 = r1 +1 r2 = r2 +1 bills_counter = r1 bills_counter = r2 ● If bills_counter = 42, what are its possible values after the execution of one A/B loop ? 6 Shared counters ● One possible result: everything works! ● Another possible result: lost update! ● Called a “race condition”. 7 Race conditions ● Def: a timing dependent error involving shared state ● It depends on how threads are scheduled. ● Hard to detect 8 Critical-Section Problem bills_counter = 0 ● Thread A ● Thread B while (my_machine_has_bills) while (my_machine_has_bills) – enter critical section – enter critical section bills_counter++ bills_counter++ – exit critical section – exit critical section print bills_counter 9 Critical-Section Problem ● The solution should ● enter section satisfy: ● critical section ● Mutual exclusion ● exit section ● Progress ● remainder section ● Bounded waiting 10 General Solution ● LOCK ● A process must acquire a lock to enter a critical section. -
Edsger W. Dijkstra: a Commemoration
Edsger W. Dijkstra: a Commemoration Krzysztof R. Apt1 and Tony Hoare2 (editors) 1 CWI, Amsterdam, The Netherlands and MIMUW, University of Warsaw, Poland 2 Department of Computer Science and Technology, University of Cambridge and Microsoft Research Ltd, Cambridge, UK Abstract This article is a multiauthored portrait of Edsger Wybe Dijkstra that consists of testimo- nials written by several friends, colleagues, and students of his. It provides unique insights into his personality, working style and habits, and his influence on other computer scientists, as a researcher, teacher, and mentor. Contents Preface 3 Tony Hoare 4 Donald Knuth 9 Christian Lengauer 11 K. Mani Chandy 13 Eric C.R. Hehner 15 Mark Scheevel 17 Krzysztof R. Apt 18 arXiv:2104.03392v1 [cs.GL] 7 Apr 2021 Niklaus Wirth 20 Lex Bijlsma 23 Manfred Broy 24 David Gries 26 Ted Herman 28 Alain J. Martin 29 J Strother Moore 31 Vladimir Lifschitz 33 Wim H. Hesselink 34 1 Hamilton Richards 36 Ken Calvert 38 David Naumann 40 David Turner 42 J.R. Rao 44 Jayadev Misra 47 Rajeev Joshi 50 Maarten van Emden 52 Two Tuesday Afternoon Clubs 54 2 Preface Edsger Dijkstra was perhaps the best known, and certainly the most discussed, computer scientist of the seventies and eighties. We both knew Dijkstra |though each of us in different ways| and we both were aware that his influence on computer science was not limited to his pioneering software projects and research articles. He interacted with his colleagues by way of numerous discussions, extensive letter correspondence, and hundreds of so-called EWD reports that he used to send to a select group of researchers. -
Cs8603-Distributed Systems Syllabus Unit 3 Distributed Mutex & Deadlock Distributed Mutual Exclusion Algorithms: Introducti
CS8603-DISTRIBUTED SYSTEMS SYLLABUS UNIT 3 DISTRIBUTED MUTEX & DEADLOCK Distributed mutual exclusion algorithms: Introduction – Preliminaries – Lamport‘s algorithm –Ricart-Agrawala algorithm – Maekawa‘s algorithm – Suzuki–Kasami‘s broadcast algorithm. Deadlock detection in distributed systems: Introduction – System model – Preliminaries –Models of deadlocks – Knapp‘s classification – Algorithms for the single resource model, the AND model and the OR Model DISTRIBUTED MUTUAL EXCLUSION ALGORITHMS: INTRODUCTION INTRODUCTION MUTUAL EXCLUSION : Mutual exclusion is a fundamental problem in distributed computing systems. Mutual exclusion ensures that concurrent access of processes to a shared resource or data is serialized, that is, executed in a mutually exclusive manner. Mutual exclusion in a distributed system states that only one process is allowed to execute the critical section (CS) at any given time. In a distributed system, shared variables (semaphores) or a local kernel cannot be used to implement mutual exclusion. There are three basic approaches for implementing distributed mutual exclusion: 1. Token-based approach. 2. Non-token-based approach. 3. Quorum-based approach. In the token-based approach, a unique token (also known as the PRIVILEGE message) is shared among the sites. A site is allowed to enter its CS if it possesses the token and it continues to hold the token until the execution of the CS is over. Mutual exclusion is ensured because the token is unique. In the non-token-based approach, two or more successive rounds of messages are exchanged among the sites to determine which site will enter the CS next. Mutual exclusion is enforced because the assertion becomes true only at one site at any given time. -
An Efficient Implementation of Guard-Based Synchronization for an Object-Oriented Programming Language an Efficient Implementation of Guard-Based
AN EFFICIENT IMPLEMENTATION OF GUARD-BASED SYNCHRONIZATION FOR AN OBJECT-ORIENTED PROGRAMMING LANGUAGE AN EFFICIENT IMPLEMENTATION OF GUARD-BASED SYNCHRONIZATION FOR AN OBJECT-ORIENTED PROGRAMMING LANGUAGE By SHUCAI YAO, M.Sc., B.Sc. A Thesis Submitted to the Department of Computing and Software and the School of Graduate Studies of McMaster University in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy McMaster University c Copyright by Shucai Yao, July 2020 Doctor of Philosophy (2020) McMaster University (Computing and Software) Hamilton, Ontario, Canada TITLE: An Efficient Implementation of Guard-Based Synchro- nization for an Object-Oriented Programming Language AUTHOR: Shucai Yao M.Sc., (Computer Science) University of Science and Technology Beijing B.Sc., (Computer Science) University of Science and Technology Beijing SUPERVISOR: Dr. Emil Sekerinski, Dr. William M. Farmer NUMBER OF PAGES: xvii,167 ii To my beloved family Abstract Object-oriented programming has had a significant impact on software development because it provides programmers with a clear structure of a large system. It encap- sulates data and operations into objects, groups objects into classes and dynamically binds operations to program code. With the emergence of multi-core processors, application developers have to explore concurrent programming to take full advan- tage of multi-core technology. However, when it comes to concurrent programming, object-oriented programming remains elusive as a useful programming tool. Most object-oriented programming languages do have some extensions for con- currency, but concurrency is implemented independently of objects: for example, concurrency in Java is managed separately with the Thread object. We employ a programming model called Lime that combines action systems tightly with object- oriented programming and implements concurrency by extending classes with actions and guarded methods. -
The Deadlock Problem
The deadlock problem More deadlocks mutex_t m1, m2; Same problem with condition variables void p1 (void *ignored) { • lock (m1); - Suppose resource 1 managed by c1, resource 2 by c2 lock (m2); - A has 1, waits on 2, B has 2, waits on 1 /* critical section */ c c unlock (m2); Or have combined mutex/condition variable unlock (m1); • } deadlock: - lock (a); lock (b); while (!ready) wait (b, c); void p2 (void *ignored) { unlock (b); unlock (a); lock (m2); - lock (a); lock (b); ready = true; signal (c); lock (m1); unlock (b); unlock (a); /* critical section */ unlock (m1); One lesson: Dangerous to hold locks when crossing unlock (m2); • } abstraction barriers! This program can cease to make progress – how? - I.e., lock (a) then call function that uses condition variable • Can you have deadlock w/o mutexes? • 1/15 2/15 Deadlocks w/o computers Deadlock conditions 1. Limited access (mutual exclusion): - Resource can only be shared with finite users. 2. No preemption: - once resource granted, cannot be taken away. Real issue is resources & how required • 3. Multiple independent requests (hold and wait): E.g., bridge only allows traffic in one direction - don’t ask all at once (wait for next resource while holding • - Each section of a bridge can be viewed as a resource. current one) - If a deadlock occurs, it can be resolved if one car backs up 4. Circularity in graph of requests (preempt resources and rollback). All of 1–4 necessary for deadlock to occur - Several cars may have to be backed up if a deadlock occurs. • Two approaches to dealing with deadlock: - Starvation is possible. -
Computing the Stabilization Times of Self-Stabilizing Systems
IEICE TRANS. FUNDAMENTALS, VOL.E83–A, NO.11 NOVEMBER 2000 2245 PAPER Special Section on Concurrent Systems Technology Computing the Stabilization Times of Self-Stabilizing Systems Tatsuhiro TSUCHIYA†, Regular Member, Yusuke TOKUDA†, Nonmember, and Tohru KIKUNO†, Regular Member SUMMARY A distributed system is said to be self- state very fast. Research in this direction includes [1], stabilizing if it converges to some legitimate state from an ar- [8], [16]. bitrary state in a finite number of steps. The number of steps In this paper, we propose an automated method required for convergence is usually referred to as the רabiÐiÞaØiÓÒ for computing the worst stabilization time, aiming at ØiÑe, and its reduction is one of the main performance issues in the design of self-stabilizing systems. In this paper, we propose supporting algorithm designers. In [6], Dolev et al. pro- an automated method for computing the stabilization time. The posed a theoretical method, called the scheduler luck method uses Boolean functions to represent the state space in game, for stabilization time analysis. This method can order to assuage the state explosion problem, and computes the stabilization time by manipulating the Boolean functions. To deal with randomized algorithms only, and it is not au- demonstrate the usefulness of the method, we apply it to the tomated. Some work addresses the issues of automatic analysis of existing self-stabilizing algorithms. The results show verification of self-stabilizing systems (e.g., [11], [12], that the method can perform stabilization time analysis very fast, [14]). To the best of our knowledge, however, there has even when an underlying state space is very huge.