Paradigm-oriented distributed computing using mobile agents

We describe the implementation underlying an environment for distributed computing that uses the concept of well-known paradigms. The main advantage of paradigm oriented distributed computing is that the user only needs to specify application-specific sequential code, while the underlying infrastructure takes care of the parallelization and distribution. The main features of the proposed approach, called PODC, which differentiate it from other approaches, are the following: (1) it is intended for loosely-coupled network environments, not specialized multiprocessors; (2) it is based on an infrastructure of mobile agents; (3) it supports programming in C, rather than a functional or special-purpose language, and (4) it provides a Web based interactive graphics interface through which programs are constructed, invoked, and monitored. The three paradigms presently supported in PODC are the bag-of-tasks, the branch-and-bound and genetic programming. We describe their implementation and performance within the mobile agent based PODC environment.

[1]  Peter G. Harrison,et al.  Parallel Programming Using Skeleton Functions , 1993, PARLE.

[2]  Lobomir F. Bic,et al.  Distributed computing using autonomous objects , 1995, Proceedings of the Fifth IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems.

[3]  Marco Vanneschi,et al.  A methodology for the development and the support of massively parallel programs , 1992, Future Gener. Comput. Syst..

[4]  Ajit Singh,et al.  Design Patterns for Parallel Programming , 1996, PDPTA.

[5]  George Horatiu Botorog,et al.  Skil: an imperative language with algorithmic skeletons for efficient distributed programming , 1996, Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing.

[6]  Murray Cole,et al.  Algorithmic Skeletons: Structured Management of Parallel Computation , 1989 .

[7]  Richard M. Karp,et al.  The Traveling-Salesman Problem and Minimum Spanning Trees , 1970, Oper. Res..

[8]  Michael B. Dillencourt,et al.  An application-transparent, platform-independent approach to rollback-recovery for mobile agent systems , 2000, Proceedings 20th IEEE International Conference on Distributed Computing Systems.

[9]  Fethi A. Rabhi,et al.  A Parallel Programming Methodology Based on Paradigms , 1995 .

[10]  Jonathan Schaeffer,et al.  A Template-Based Approach to the Generation of Distributed Applications Using a Network of Workstations , 1991, IEEE Trans. Parallel Distributed Syst..

[11]  Reid G. Smith,et al.  The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver , 1980, IEEE Transactions on Computers.

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  Munehiro Fukuda,et al.  Distributed Computing Using Autonomous Objects , 1996, Computer.

[14]  S. A. Prahl,et al.  A Monte Carlo model of light propagation in tissue , 1989, Other Conferences.

[15]  Sartaj Sahni,et al.  Anomalies in Parallel Branch-and-Bound Algorithms , 1984 .

[16]  Hairong Kuang,et al.  PODC: Paradigm-oriented distributed computing , 1999, Proceedings 7th IEEE Workshop on Future Trends of Distributed Computing Systems.

[17]  N. Nisan,et al.  Globally distributed computation over the Internet-the POPCORN project , 1998, Proceedings. 18th International Conference on Distributed Computing Systems (Cat. No.98CB36183).