Toward a New Computing Model. Mario Tokoro 3. Keio University Hiyoshi, Yokohama 223 JAPAN. November 26, Abstract

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1 Toward a New Computing Model for an Open Distributed Environment Mario Tokoro 3 Department of Computer Science Keio University Hiyoshi, Yokohama 223 JAPAN Tel: Telefax: mario@keioacjp November 26, 1991 Abstract This paper proposes a new computing model called computational eld model, or CFM for short, for solving a problem in an object-oriented open distributed environment In this model, we envisage an open-ended distributed environment as a continuous computational eld We introduce the notions of distance between objects and the mass of an object to the model Thus, solving a problem can be seen as a mutual eect between the computational eld and the problem 3 also with Sony Computer Science Laboratory Inc, Takanawa Muse Building, Higashi Gotanda, Shinagawa-ku, Tokyo, 141 JAPAN Tel: , Telefax: , mario@cslsonycojp 1

2 1 Introduction Recent demands on computer systems can be summarized as: 1 to solve larger and more complex problems, 2 to realize more reliable real time processing, and 3 to provide better user interfaces We have realized the necessity of following software-related items to answer these demands based on the advancement of hardware technology: raising the level of abstraction in describing problems, employing inherent parallelism of problems, accepting the notion of Open Systems [Hewitt 84], and introducing the notion of atomic transaction, time, and so forth In this paper, we pick up the rst demand as our primary concern and propose a new computing model called computational eld model, or CFM for short CFM is a computing model for solving a large complex problem on an object-oriented open-ended distributed environment In this model, we envisage an open-ended distributed environment as a continuous computational eld We introduce the notions of distance between objects and the mass of an object to the model Thus, solving a problem can be seen as mutual eects between the computational eld and the problem 2 Object-oriented Computing The adjective object-oriented emerged in early 1970's at almost the same time in the elds of programming languages, operating systems, articial intelligence, databases, and graphics The commonness and dierence in the usages were realized in 1980's The most common and important feature in those is that object-orientation is a technique of modularization or abstraction in programming and knowledge representation, where modularization is performed in analogy to objects in the real world, so that computation is modeled as a simulation of the world We dene object and object-oriented computing as follows: An object is a physical or logical entity that is self-contained and provided with a unied communication protocol Object-oriented computing is a method of computing in which objects request computation and receive answers from each other in terms of the unied communication protocol A unied communication protocol means that an object can communicate with any other objects through the communication protocol when it knows the addresses (or id's) of the objects 2

3 An object possesses a set of procedures which correspond to computable requests and a local storage to keep its state Object-oriented computing can be understood as the departure from the microscopic view of computing where computation proceeds by executing an algorithm of a procedure to the macroscopic view where computation proceeds as mutual eects among objects 3 Concurrent Objects A concurrent object [Tokoro 88] possesses a (virtual) processor in addition to its local storage and a set of procedures By incorporating a processor in an object, we can eliminate the notion of the locus of execution or allocation of a processor to an object from object-oriented computing Thus, we can employ concurrent objects as a simpler unit for concurrent and distributed computing Hewitt has been advocating this notion as the theory of Actor [Hewitt 73] Orient84/K [Tokoro 84], ConcurrentSmalltalk [Yokote 86] [Yokote 87], also employ this notion As a result, those languages have the following very important advantages: Objects in the real world exist in parallel and execute in parallel By using concurrent objects, it becomes very easy and natural to model computation in analogy to the real world The allocation of processors to objects becomes an implementation issue rather than language issue Therefore, a program becomes independent from the executing system architecture (ie, shared/distributed memory system, the number of processors in the system, and so forth) Most concurrent object-oriented programming languages provide a special object, usually called a future object, to receive an answer for an asynchronous request The notion of future object can easily be extended so that a future object receive more than one answer Thus, we can incorporate stream in object-oriented computing On the other hand, assignment in conventional languages is to generate a future object and to request the destination object to return the answer to the future object 4 Parallel Computing Parallel computing is one of the important methods for high-speed computing Parallel computation of a program is achieved in the following two steps taking balancing of load and reducing overhead for sharing information into account: 1 decomposing a problem into subproblems 2 allocating subproblems to parallel hardware 3

4 Sharing of information is achieved in either of the following schemes: 1 sharing a memory region, or 2 sending messages Sharing a memory region is advantageous when the amount of information shared among subproblems is large We can even take this approach to a system with distributed memory by using the technique of shared virtual memory [Li 86] However, there is a large possibility of processors being idle due to mutual exclusion among subproblems Using messages is advantageous when the amount of information shared among subproblems is small However, there is the overhead of message transmission and reception That is, sharing a memory region has no overhead of message transmission and reception, while it may suer from performance degradation due to processors being idle Using messages has a lower probability of processors being idle, while it may suer from the overhead of message transmission and reception It is dicult in general to determine the allocation of subproblems to processors/computers in advance of execution so that the load of each processor/computer balances for the entire course of execution Thus, we need dynamic allocation of subproblems to processors/computers In case of sharing a memory region for sharing information, it is dicult to allocate subproblems to processors/computers beyond their directly accessible regions It becomes dicult even to write a correct program and maintain it which uses a memory region for sharing information, when the problem becomes large and complex This is because we need a lower level of synchronization among subproblems than the object level Thus, sharing by message is advantageous when we need wide-range load averaging for a large problem If we have a large amount of information to share, we make it one or more objects so that we can share the information by messages We can employ the shared virtual memory technique as the base so that object migration can be easily achieved as well 5 Distributed Computing In order to perform parallel computing, we decompose a problem into subproblems and allocate them to parallel hardware This in fact yields distance between subproblems Distance manifests in communication delay And, this prevents an object from knowing the current status of other objects This is an essential characteristic of distributed systems We can now dene distributed computing as computation with more than one activity (or object) where distance, or communication delay, between activities (or objects) has to be considered In the other words, parallel computing can be dened as a sort of distributed computing where distance, or communication delay, between activities can be ignored If all the objects concerned with a computation have to have the unique view of an object in distributed computing, we need synchronization among all the objects for every event when 4

5 the object changes its state We call this complete sharing of an object Instead, the objects concerned with a computation can give up having the unique view of an object, so that we can reduce the frequency of synchronization We call this incomplete sharing of an object In a distributed system, it is impractical to do complete sharing, especially when distance among objects is long and the amount of shared information is large And it is not always necessary in spite of [Carriero 89][Birman 85] 6 Open Systems We started parallel computing by decomposing a problem into subproblems However, our recent computational environment consists of existing servers (ie, objects or agents) The number and services of servers change from a time to time Recent computation is thus performed in a distributed computational environment Consequently, programming style is changing from an algorithmic/synthetic way to \try to use them" way That is, a program is written to make maximal use of existing services at each step in the computation In such a programming style, it is impossible to know in advance of execution what kinds of services are available at a certain time In addition, in order to know the available services in the course of executing a program, an object has to use time and computational power Nevertheless, the result returned to the object might not be correct, since the state of the system could change before it takes the planned action This is the signicance of Open Systems[Hewitt 84] Although this is an unavoidable drawback from the conventional viewpoint of programming methodology, we should armatively utilize this characteristic for eciency and robustness of a system 7 Computational Field Model As the number of computers increases, and as the mesh of networks becomes ner, it would be reasonable to consider an open distributed environment as a continuous computational eld This is one level higher model than the model of computers connected with networks, since we can ignore the topology and capacity of networks, and kinds and performance of connected computers We call such a model computational eld model, or CFM for short In this model, solving a problem is envisaged as a mutual eect between the computational eld and the problem By using CFM, we can rst observe the nature and behavior of a problem macroscopically and nd the method to control the problem so as to maximally utilize the given resources One important notion in CFM is the principle of locality Eect of any event is local This is due to the distance between objects in the eld, and communication delay in the eld We propose one way of controlling the mutual eect between problems and the computational eld 5

6 in the next three sections 8 Distance In distributed computing, especially in open environments, it is very important to keep the communication delay between objects short Communication delay is a function of geographical distance, communication bandwidth, and other communication overhead Let us dene distance between objects as communication delay between the objects 1 In open systems where computation proceeds utilizing existing objects (servers), we should use closer objects if the same services are provided, and we should ask objects to move closer for higher performance However, an open system is a multiuser system Thus, an object which is used by n users (or n objects) should be placed at (or migrated to) a location where those users can eciently use it In order to decide such an ideal location, it is rational to dene gravitational force between objects by the frequency of communication between the two objects If we want to dene the locations of objects only by gravitational forces, all of the objects get together at one single point In such a case, although communication delay is minimum, the execution speed of each object is slowed, because all the loads are gathered into one computer Thus, we should introduce repulsive force, which can be dened by the load of a processor By knowing the distance and communication frequency between objects in CFM, it is possible to determine the optimum position of objects for each event in the course of execution However, it should also be noted that observing distance requires time and computing power, and the measured distance may already incorrect when we take action to the object 9 Mass Assume that at a certain time in the course of computing the objects are all placed at their optimum locations Also assume that we can know their optimum locations at the next time It is not always true that migration should take place This is because we have to pay a cost for migration The cost for migration is a function of the distance and the mass of the migrating object The mass can be dened by the size of the object We may also have to take the inertia of an object into consideration, which is interpreted as the overhead for migration According to the above consideration, the location of the objects at the next time should be determined by the cost and the eect of migration 1 This denition is reasonable since ultimate communication delay between objects is determined by geographical distance 6

7 10 MD-based Computing We now dene MD-based computing as a method of computing in CFM where computation proceeds by trying to maximally utilize existing objects and determining the optimal location of objects at the next timing taking cost and eect of migration into account Determination of the optimal locations is performed by using the notions of distance, mass, gravitation force and repulsive force, where distance is a function of communication delay between objects, mass is the size or inertia of an object, gravitation force is a function of frequency of communication between objects, and repulsive force is a function of additional load to a computer Thus, MD-based computing is a computational method in which each user (or each object) does locally a best eort to achieve satisfactory allocation of objects using the notion of distance and mass Conict between users for their satisfactory object allocations should be solved by negotiation between users to nd sub-satisfactory object allocations for them This is viewing computation as transforming a part of the huge computational eld into its adequate shape That is to say, in MD-based computing, solving a problem is considered as mutual eects between the computational eld and the problem Figure 1 illustrates MD-based computing in CFM There are two tasks (Task A and Task B) being executed in a open distributed computational eld If you want to start a new task, you will put the task to the eld through your interface computer The load of the computer becomes very high so that repulsive forces appear among the objects consisting the task At the same time, gravitational forces appear between these objects and some existing objects on the computational eld, as these objects communicate with the existing objects Thus, the task starts to diuse, that is, objects consisting the task migrate Some of the existing objects may move to their new locations, being attracted by the new task Negotiations among objects may be necessary for nding satisfactory locations Please note that all the existing computations in the computational eld form the environment for the new task, and the new task changes the environment 11 Conclusion In this paper, we proposed a new computing model called computational eld model for solving a problem in an open distributed environment In this model, we view an open distributed computing environment as a continuous computational eld, as opposed to conventional view of a computing system being a discrete computational eld We also proposed the method of MDbased computing to give a concrete idea of how to perform computation in the computational eld model We introduced the notions of distance and mass in MD-based computing, and we envisage solving a problem as mutual eects between the computational eld and the problem Based on the notion of the computational eld model and MD-based computing, we have 7

8 New Task Task A Task B Open Ended Gravitational Force Repulsive Force Figure 1: MD-based computing in a computational eld been developing a new operating system called MUSE[Yokote 89,?] which supports object migration We are also developing a programming language called MUSIC[Watari 90] to describe wide variety of application programs on the MUSE operating system Acknowledgment The author is indebted to Professor Carl E Hewitt, who is currently an IBM Chair Visiting Adjunct Professor at Keio University, for giving him an insight of Open Systems The author wishes to thank Shinji Kono, Chisato Numaoka, Eiichi Osawa, Rik Smoody, Shigeru Watari, and Yasuhiko Yokote for their critical comments and discussion References [Birman 85] Kenneth P Birman Replication and Fault-Tolerance in the ISIS System In Proceedings of the 10th ACM Symposium on Operating System Principles, ACM, December 1985 [Carriero 89] Nicholas Carriero and David Gelernter Linda in context Communications of the ACM, Vol32, No4, 1989 [Hewitt 73] C Hewitt, P Bishop, and R Steiger A Universal Modular ACTOR Formalism for Articial Intelligence In Proceedings of the 3rd International Joint Conference on Articial Intelligence, August

9 [Hewitt 84] Carl Hewitt and Peter de Jong Open Systems In J Mylopoulos and J W Schmidt M L Brodie, editors, On Conceptual Modeling, Springer-Verlag, 1984 [Li 86] Kai Li Shared Virtual Memory on Loosely Coupled Multiprocessors Technical Report, Yale University, December 1986 [Tokoro 84] Mario Tokoro and Yutaka Ishikawa Object-Oriented Approach to Knowledge Systems In Proceedings of the International Conference on Fifth Generation Computer Systems 1984, November 1984 [Tokoro 88] Mario Tokoro Issues in Object-Oriented Concurrent Computing In Proceedings of 4th Conference of Japan Society for Software Science and Technology, September 1988 (in Japanese) [Watari 90] Shigeru Watari, Ei-ichi Osawa, Yasuaki Honda, and Mike Reeve Towards Music: A Description Language for the Muse Object Model Technical Report SCSL-TM , Sony Computer Science Laboratory Inc, February 1990 [Yokote 86] Yasuhiko Yokote and Mario Tokoro Design and Implementation of Concurrent- Smalltalk In Proceedings of Object-Oriented Programming Systems, Languages and Applications in 1986, ACM, September{October 1986 [Yokote 87] Yasuhiko Yokote and Mario Tokoro Concurrent Programming in Concurrent- Smalltalk In Akinori Yonezawa and Mario Tokoro, editors, Object-Oriented Concurrent Programming, pp129{158, The MIT Press, 1987 [Yokote 89] Yasuhiko Yokote, Fumio Teraoka, and Mario Tokoro A Reective Architecture for an Object-Oriented Distributed Operating System In Proceedings of European Conference on Object-Oriented Programming, July 1989 also appeared in SCSL-TR of Sony Computer Science Laboratory Inc 9