Utilizing local and global queueing resources with uncertainty in state and service

In autonomous decentralized systems (ADS), there is a high degree of uncertainty, especially in global state information and the performance and reliability of various resources. We model a distributed computer system of local and shared global servers, each of which can process jobs. The local servers insure a degree of autonomy and the global servers provide added computational power and redundancy. When a job arrives, an agent decides whether to schedule the job locally, or whether to ship it to a global server. The agents have access to state information concerning current loads on the global servers and the response times of completed jobs, but because of decentralization this information is delayed, and perhaps lost, along communication channels. The agents must make good decisions under the constraint that service rates are unknown and dynamic. We evaluate a deterministic algorithm and two randomizing algorithms and show that the degree of uncertainty determines which one is the most successful