Abstract: Modern distributed systems scatter sensors, storage, and computation throughout the environment. Ideally these devices communicate and share resources, but there is seldom motivation for a device's owner to yield control to another user. We establish markets for computational resources to motivate principals to share resources with arbitrary users, to enforce priority in distributed systems, to provide flexible and rational limitations on the potential of an application, and to provide a lightweight structure to balance the workload over time and between devices. As proof of concept, we implement a structure software agents can use to discover and negotiate access to networked resources. The structure separates discovery, authentication, and consumption enforcement as separate orthogonal issues to give system designers flexibility.
Mobile agents represent informational and computational flow. We develop mechanisms that distributively allocate computation among mobile agents in two settings. The first models a situation where users collectively own networked computing resources and require priority enforcement. We extend the allocation mechanism to allow resource reservation to mitigate utility volatility. The second, more general model relaxes the ownership assumption. We apply our computational market to an open setting where a principal's chief concern is revenue maximization.
Our simulations compare the performance of market-based allocation policies to traditional policies and relate the cost of ownership and consumption separation. We observe that our markets effectively prioritize applications' performance, can operate under uncertainty and network delay, provide metrics to balance network load, and allow measurement of market-participation risk versus reservation-based computation.
In addition to allocation problems, we investigate resource selection to optimize execution time. The problem is NP-complete if the costs and latencies are constant. Both metrics' dependence on the chosen set complicates matters. We study how a greedy approach, a novel heuristic, and a shortest-constrained-path strategy perform in mobile-agent applications.
Market-based computational-resource allocation fertilizes applications where previously there was a dearth of motive for or means of cooperation. The rationale behind mobile-agent performance optimization is also useful for resource allocation in general distributed systems where an application has a sequence of dependent tasks or when data collection is expensive.
Copyright © 2001 by Jonathan L. Bredin.