Automated Learning of Load-Balancing Strategies For A Distributed Computer System (1992)
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BibTeX
@MISC{Mehra92automatedlearning,
author = {P. Mehra and Load Balancing As and A Strategy-learning Task},
title = {Automated Learning of Load-Balancing Strategies For A Distributed Computer System},
year = {1992}
}
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Abstract
(or derived) decision metrics are exemplified by MinLoad, which denotes the least among all the Load values. ###################################################################################### SENDER-SIDE RULES (s) Possible-destinations = { site: Load(site) - Reference(s) < d(s) } Destination = Random(Possible-destinations) IF Load(s) - Reference(s) > q 1 (s) THEN Send RECEIVER-SIDE RULES (r) IF Load(r) < q 2 (r) THEN Receive Figure 3. The load-balancing policy considered in this thesis The sender-side rules are applied by the load-balancing software at the site of arrival (s) of a task. Reference can be either 0 or MinLoad; the other parameters --- d, q 1 , and q 2 --- take non-negative floating-point values. A remote destination (r) is chosen randomly from Destinations, a set of sites whose load index falls within a small neighborhood of Reference. If Destinations is the empty set, or if the rule for sending fails, then the task is executed locally at s, its site of arrival; ot...
