Trading Risk in Mobile-Agent Computational Markets

Mobile-agent systems allow user programs to autonomously relocate from one host site to another. This autonomy provides a powerful, flexible architecture on which to build distributed applications. The asynchronous, decentralized nature of mobile-agent systems makes them flexible, but also hinders their deployment. We argue that a marketbased approach where agents buy computational resources from their hosts solves many problems faced by mobileagent 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. Here we improve on our earlier approach by implementing resource guarantees where mobileagent hosts issue call options on computational resources. Call options allow an agent to reserve and guarantee the cost and time necessary to complete its itinerary before the agent begins execution. 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 to show that option trade reduces variance in agent completion times.