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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. -
INCOSE: the FAR Approach “Functional Analysis/Allocation and Requirements Flowdown Using Use Case Realizations”
in Proceedings of the 16th Intern. Symposium of the International Council on Systems Engineering (INCOSE'06), Orlando, FL, USA, Jul 2006. The FAR Approach – Functional Analysis/Allocation and Requirements Flowdown Using Use Case Realizations Magnus Eriksson1,2, Kjell Borg1, Jürgen Börstler2 1BAE Systems Hägglunds AB 2Umeå University SE-891 82 Örnsköldsvik SE-901 87 Umeå Sweden Sweden {magnus.eriksson, kjell.borg}@baesystems.se {magnuse, jubo}@cs.umu.se Copyright © 2006 by Magnus Eriksson, Kjell Borg and Jürgen Börstler. Published and used by INCOSE with permission. Abstract. This paper describes a use case driven approach for functional analysis/allocation and requirements flowdown. The approach utilizes use cases and use case realizations for functional architecture modeling, which in turn form the basis for design synthesis and requirements flowdown. We refer to this approach as the FAR (Functional Architecture by use case Realizations) approach. The FAR approach is currently applied in several large-scale defense projects within BAE Systems Hägglunds AB and the experience so far is quite positive. The approach is illustrated throughout the paper using the well known Automatic Teller Machine (ATM) example. INTRODUCTION Organizations developing software intensive defense systems, for example vehicles, are today faced with a number of challenges. These challenges are related to the characteristics of both the market place and the system domain. • Systems are growing ever more complex, consisting of tightly integrated mechanical, electrical/electronic and software components. • Systems have very long life spans, typically 30 years or longer. • Due to reduced acquisition budgets, these systems are often developed in relatively short series; ranging from only a few to several hundred units. -
(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. -
Chapter 1 Issues—The Software Crisis
Chapter 1 Issues—The Software Crisis 1. Introduction to Chapter This chapter describes some of the current issues and problems in system development that are caused The term "software crisis" has been used since the by software—software that is late, is over budget, late 1960s to describe those recurring system devel- and/or does not meet the customers' requirements or opment problems in which software development needs. problems cause the entire system to be late, over Software is the set of instructions that govern the budget, not responsive to the user and/or customer actions of a programmable machine. Software includes requirements, and difficult to use, maintain, and application programs, system software, utility soft- enhance. The late Dr. Winston Royce, in his paper ware, and firmware. Software does not include data, Current Problems [1], emphasized this situation when procedures, people, and documentation. In this tuto- he said in 1991: rial, "software" is synonymous with "computer pro- grams." The construction of new software that is both Because software is invisible, it is difficult to be pleasing to the user/buyer and without latent certain of development progress or of product com- errors is an unexpectedly hard problem. It is pleteness and quality. Software is not governed by the perhaps the most difficult problem in engi- physical laws of nature: there is no equivalent of neering today, and has been recognized as such Ohm's Law, which governs the flow of electricity in a for more than 15 years. It is often referred to as circuit; the laws of aerodynamics, which act to keep an the "software crisis". -
Static Analysis of a Concurrent Programming Language by Abstract Interpretation
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Concordia University Research Repository STATIC ANALYSIS OF A CONCURRENT PROGRAMMING LANGUAGE BY ABSTRACT INTERPRETATION Maryam Zakeryfar A thesis in The Department of Computer Science and Software Engineering Presented in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy (Computer Science) Concordia University Montreal,´ Quebec,´ Canada March 2014 © Maryam Zakeryfar, 2014 Concordia University School of Graduate Studies This is to certify that the thesis prepared By: Mrs. Maryam Zakeryfar Entitled: Static Analysis of a Concurrent Programming Language by Abstract Interpretation and submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Science) complies with the regulations of this University and meets the accepted standards with respect to originality and quality. Signed by the final examining committee: Dr. Deborah Dysart-Gale Chair Dr. Weichang Du External Examiner Dr. Mourad Debbabi Examiner Dr. Olga Ormandjieva Examiner Dr. Joey Paquet Examiner Dr. Peter Grogono Supervisor Approved by Dr. V. Haarslev, Graduate Program Director Christopher W. Trueman, Dean Faculty of Engineering and Computer Science Abstract Static Analysis of a Concurrent Programming Language by Abstract Interpretation Maryam Zakeryfar, Ph.D. Concordia University, 2014 Static analysis is an approach to determine information about the program without actually executing it. There has been much research in the static analysis of concurrent programs. However, very little academic research has been done on the formal analysis of message passing or process-oriented languages. We currently miss formal analysis tools and tech- niques for concurrent process-oriented languages such as Erasmus . -
The Roots of Software Engineering*
THE ROOTS OF SOFTWARE ENGINEERING* Michael S. Mahoney Princeton University (CWI Quarterly 3,4(1990), 325-334) At the International Conference on the History of Computing held in Los Alamos in 1976, R.W. Hamming placed his proposed agenda in the title of his paper: "We Would Know What They Thought When They Did It."1 He pleaded for a history of computing that pursued the contextual development of ideas, rather than merely listing names, dates, and places of "firsts". Moreover, he exhorted historians to go beyond the documents to "informed speculation" about the results of undocumented practice. What people actually did and what they thought they were doing may well not be accurately reflected in what they wrote and what they said they were thinking. His own experience had taught him that. Historians of science recognize in Hamming's point what they learned from Thomas Kuhn's Structure of Scientific Revolutions some time ago, namely that the practice of science and the literature of science do not necessarily coincide. Paradigms (or, if you prefer with Kuhn, disciplinary matrices) direct not so much what scientists say as what they do. Hence, to determine the paradigms of past science historians must watch scientists at work practicing their science. We have to reconstruct what they thought from the evidence of what they did, and that work of reconstruction in the history of science has often involved a certain amount of speculation informed by historians' own experience of science. That is all the more the case in the history of technology, where up to the present century the inventor and engineer have \*-as Derek Price once put it\*- "thought with their fingertips", leaving the record of their thinking in the artefacts they have designed rather than in texts they have written.