Authors: E. Billard and J. Pasquale.
Title: Localized decision making and the value of information in decentralized control.
Published in: Proc. 7th ISCA Intl. Conf. on Parallel and Distributed
  Computing Systems, pp. 417-425, 1994.

Abstract: Each job scheduling agent in large
decentralized load balancing systems generally must consider whether it is
advantageous to offload 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 affected 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 different 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.