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Trading Risk In Mobile-Agent Computational Market

Author

Listed:
  • Jonathan Bredin

    (Dartmouth College)

  • David Kotz

    (Dartmouth College)

  • Daniela Rus

    (Dartmouth College)

Abstract

Mobile-agent systems allow user programs to autonomously relocate from one host site to another. This autonomicity provides a powerful flexible architecture on which to build distributed applications. A quality that makes mobile-agent systems so flexible is also one that hinders their deployment: asynchronous decentralized control. We argue that a market-based approach where agents buy computational resources from their hosts solves many problems faced by mobile-agent systems.In our earlier work, we propose a policy for allocating general computational priority among agents posed as a competitive game for which we derive a unique computable Nash equilibrium. We improve on our earlier approach by implementing resource guarantees where mobile-agent hosts issue call options on computational resources. Call options allows an agent to assess the cost and time necessary to complete its itineraries before the agent embarks.We present an algorithm based upon the binomial options-pricing model that estimates future congestion to allow hosts to evaluate call options; methods for agents to measure the risk associated with their performance and compare their expected utility of competing in the computational spot market with utilizing resource options; and test our theory with simulations.

Suggested Citation

  • Jonathan Bredin & David Kotz & Daniela Rus, 2000. "Trading Risk In Mobile-Agent Computational Market," Computing in Economics and Finance 2000 109, Society for Computational Economics.
    • Handle: RePEc:sce:scecf0:109
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