Localized Decision-Making and the Value of Information in Decentralized Control

Each job scheduling agent in large decentralized load balancing systems generally must consider whether it is advantageous to o oad jobs to remote hosts when the local load is too high. Although processing power may appear to be available at a very distant host, two problems arise due to the transmission delay between the two systems. Predictably, the response time of the job is adversely a ected as the job spends valuable time in transit, but a more subtle problem involves the value, or reliability, of the state information regarding job queues. The longer the delay between systems, the less an agent should value the state information of the remote system. We examine the performance of agents in topologies with di erent proximity (in terms of the average number of hops between hosts) and show that adaptive agents prefer to work with agents in close proximity. That is, the agents prefer localized decision-making due to more valuable information and these agents perform better than non-adaptive agents which consider only the delays in job transmission.