When distributed systems first appeared, they were programmed in traditional sequential languages, usually with the addition of a few library procedures for sending and receiving messages. As distributed applications became more commonplace and more sophisticated, this ad hoc approach became less satisfactory. Researchers all over the world began designing new programming languages specifically for implementing distributed applications. These languages and their history, their underlying principles, their design, and their use are the subject of this paper. We begin by giving our view of what a distributed system is, illustrating with examples to avoid confusion on this important and controversial point. We then describe the three main characteristics that distinguish distributed programming languages from traditional sequential languages, namely, how they deal with parallelism, communication, and partial failures. Finally, we discuss 15 representative distributed languages to give the flavor of each. These examples include languages based on message passing, rendezvous, remote procedure call, objects, and atomic transactions, as well as functional languages, logic languages, and distributed data structure languages. The paper concludes with a comprehensive bibliography listing over 200 papers on nearly 100 distributed programming languages.

We present an actor language which is an extension of a simple functional language, and provide a precise operational semantics for this extension. Actor configurations represent open distributed systems, by which we mean that the specification of an actor system explicitly takes into account the interface with external components. We study the composability of such systems. We define and study various notions of testing equivalence on actor expressions and configurations. The model we develop provides fairness. An important result is that the three forms of equivalence, namely, convex, must, and may equivalences, collapse to two in the presence of fairness. We further develop methods for proving laws of equivalence and provide example proofs to illustrate our methodology.

Abstract — Two forms of cooperation in distributed problem solving are considered: task-sharing and result-sharing. In the former, nodes assist each other by sharing the computational load for the execution of subtasks of the overall problem. In the latter, nodes assist each other by sharing partial results which are based on somewhat different perspectives on the overall problem. Different perspectives arise because the nodes use different knowledge sources (KS’s) (e.g., syntax versus acoustics in the case of a speech-understanding system) or different data (e.g., data that is sensed at different locations in the case of a distributed sensing system). Particular attention is given to control and to internode communication for the two forms of cooperation. For each, the basic methodology is presented and systems in which it has been used are described. The two forms are then compared and the types of applications for which they are suitable are considered. I. DISTRIBUTED PROBLEM SOLVING

Introduction Cooperative Distributed Problem-Solving (CDPS) studies how a loosely-coupled network of problem solvers can work together to solve problems that are beyond their individual capabilities. Each problem-solving node in the network is capable of sophisticated problem solving and can work independently, but the problems faced by the nodes cannot be completed without cooperation. Cooperation is necessary because no single node has sufficient expertise, resources, and information to solve a problem, and different nodes might have expertise for solving different parts of the problem. For example, if the problem is to design a house, one node might have expertise on the strength of structural materials, another on the space requirements for different types of rooms, another on plumbing, another on electrical wiring, and so on. Different nodes might have different resources: some might be very fast at computation, others might have connections that speed communication, whil

This paper proposes a concurrent model that takes into account such important concerns. We insist on concept unifications: the underlying reasons that make object-oriented programming adapted to concurrency. The model characteristics, especially reusability, permit us to define a concurrent object-oriented design method.

In this article we discuss the potential uses of mobile agents in network management and define software agents and a navigation model that determines agent mobility. We list a number of potential advantages and disadvantages of mobile agents and include a short commentary on the ongoing standardization activity. The core of this article comprises descriptions of several actual and potential applications of mobile agents in the five OSI functional areas of network management. A brief review of other research activity in the area and prospects for the future conclude the presentation. SURVEYS IEEE# COMMUNICATIONS www.comsoc.org/pubs/surveys IEEE Communications Surveys . http://www.comsoc.org/pubs/surveys . Fourth Quarter 1998 . Vol. 1 No. 1 IEEE Communications Surveys . http://www.comsoc.org/pubs/surveys . Fourth Quarter 1998 . Vol. 1 No. 1 3 [13]. Examples of the former approach include AgentTCL [14, 15] and Telescript [16], and the latter, Aglets [17]. To make use of mo...

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Polyphonic C# is an extension of the C# language with new asynchronous concurrency constructs, based on the join calculus. We describe the design and implementation of the language and give examples of its use in addressing a range of concurrent programming problems.

Traditionally the view has been that direct expression of control and store mechanisms and clear mathematical semantics are incompatible requirements. This paper shows that adding objects with memory to the call-by-value lambda calculus results in a language with a rich equational theory, satisfying many of the usual laws. Combined with other recent work this provides evidence that expressive, mathematically clean programming languages are indeed possible. 1. Overview Real programs have effects---creating new structures, examining and modifying existing structures, altering flow of control, etc. Such facilities are important not only for optimization, but also for communication, clarity, and simplicity in programming. Thus it is important to be able to reason both informally and formally about programs with effects, and not to sweep effects either to the side or under the store parameter rug. Recent work of Talcott, Mason, Felleisen, and Moggi establishes a mathematical foundation for...

A new approach for structuring distributed processing systems, called functionally accurate, cooperative (FA/C), is proposed. The approach differs from conventional ones in its emphasis on handling distribution-caused uncertainty and errors as an integral part of the network problem-solving process. In this approach nodes cooperatively problem solve by exchanging partial tentative results (at various levels of abstraction) within the context of common goals. The approach is especially suited to applications in which the data necessary to achieve a solution cannot be partitioned in such a way that a node can complete a task without seeing the intermediate state of task processing at other nodes. Much of the inspiration for the FA/C approach comes from the mechanisms used in knowledge-based artificial intelligence (AI) systems for resolving uncertainty caused by noisy input data and the use of approximate knowledge. The appropriateness of the FA/C approach is explored in three application domains: distributed interpretation, distributed network traffic-light control, and distributed planning. Additionally, the relationship between the approach and the structure of management organizations is developed. Finally, a number of current research directions necessary to more fully develop the FA/C approach are outlined. These research directions include distributed search, the integration of implicit and explicit forms of control, and distributed planning and organizational self-design. I.