Spawn: A Distributed Computational Economy

The authors have designed and implemented an open, market-based computational system called Spawn. The Spawn system utilizes idle computational resources in a distributed network of heterogeneous computer workstations. It supports both coarse-grain concurrent applications and the remote execution of many independent tasks. Using concurrent Monte Carlo simulations as prototypical applications, the authors explore issues of fairness in resource distribution, currency as a form of priority, price equilibria, the dynamics of transients, and scaling to large systems. In addition to serving the practical goal of harnessing idle processor time in a computer network, Spawn has proven to be a valuable experimental workbench for studying computational markets and their dynamics. >

[1]  Ivan E. Sutherland,et al.  A futures market in computer time , 1968, Commun. ACM.

[2]  Peter J. Denning,et al.  Operating Systems Theory , 1973 .

[3]  I. M. Sobolʹ The Monte Carlo method , 1974 .

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

[5]  John F. Shoch,et al.  The “worm” programs—early experience with a distributed computation , 1982, CACM.

[6]  Jacob A. Abraham,et al.  Load Balancing in Distributed Systems , 1982, IEEE Transactions on Software Engineering.

[7]  Randall Davis,et al.  Negotiation as a Metaphor for Distributed Problem Solving , 1988, Artif. Intell..

[8]  Douglas B. Lenat,et al.  The Role of Heuristics in Learning by Discovery: Three Case Studies , 1983 .

[9]  Barbara Liskov,et al.  Guardians and Actions: Linguistic Support for Robust, Distributed Programs , 1983, TOPL.

[10]  Daniel G. Theriault Issues in the Design and Implementation of Act 2 , 1983 .

[11]  R. M. Stark,et al.  Auctions, Bidding, and Contracting: Uses and Theory , 1983 .

[12]  D. Friedman On the Efficiency of Experimental Double Auction Markets , 1984 .

[13]  Andrew S. Tanenbaum,et al.  Distributed operating systems , 2009, CSUR.

[14]  Amnon Barak,et al.  A distributed load‐balancing policy for a multicomputer , 1985, Softw. Pract. Exp..

[15]  Carl Hewitt,et al.  The challenge of open systems: current logic programming methods may be insufficient for developing the intelligent systems of the future , 1985 .

[16]  John R. Ellis,et al.  Bulldog: A Compiler for VLIW Architectures , 1986 .

[17]  Daniel G. Bobrow,et al.  Logical Secrets , 1988, ICLP.

[18]  Miron Livny,et al.  Scheduling Remote Processing Capacity in a Workstation-Processor Bank Network , 1987, ICDCS.

[19]  Virginia Mary Lo,et al.  Heuristic Algorithms for Task Assignment in Distributed Systems , 1988, IEEE Trans. Computers.

[20]  Donald F. Ferguson,et al.  Microeconomic algorithms for load balancing in distributed computer systems , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[21]  Miron Livny,et al.  Condor-a hunter of idle workstations , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[22]  Joseph Carlo Pasquale,et al.  Intelligent decentralized control in large distributed computer systems , 1988 .

[23]  M. Hailperin Load balancing for massively-parallel soft real-time systems , 1988, Proceedings., 2nd Symposium on the Frontiers of Massively Parallel Computation.

[24]  Kenneth M. Kahn,et al.  Money as a Concurrent Logic Program , 1989, NACLP.

[25]  Carl A. Waldspurger,et al.  A distributed computational economy for utilizing idle resources , 1989 .

[26]  Hogg,et al.  Dynamics of computational ecosystems. , 1989, Physical review. A, General physics.

[27]  Gul A. Agha,et al.  ACTORS - a model of concurrent computation in distributed systems , 1985, MIT Press series in artificial intelligence.

[28]  C. Hewitt The challenge of open systems , 1990 .