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Population-based learning of load balancing policies for a distributed computer systemEffective load-balancing policies use dynamic resource information to schedule tasks in a distributed computer system. We present a novel method for automatically learning such policies. At each site in our system, we use a comparator neural network to predict the relative speedup of an incoming task using only the resource-utilization patterns obtained prior to the task's arrival. Outputs of these comparator networks are broadcast periodically over the distributed system, and the resource schedulers at each site use these values to determine the best site for executing an incoming task. The delays incurred in propagating workload information and tasks from one site to another, as well as the dynamic and unpredictable nature of workloads in multiprogrammed multiprocessors, may cause the workload pattern at the time of execution to differ from patterns prevailing at the times of load-index computation and decision making. Our load-balancing policy accommodates this uncertainty by using certain tunable parameters. We present a population-based machine-learning algorithm that adjusts these parameters in order to achieve high average speedups with respect to local execution. Our results show that our load-balancing policy, when combined with the comparator neural network for workload characterization, is effective in exploiting idle resources in a distributed computer system.
Document ID
19940034879
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Mehra, Pankaj
(NASA Ames Research Center Moffett Field, CA, United States)
Wah, Benjamin W.
(Illinois Univ. Urbana, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1993
Publication Information
Publication: In: AIAA Computing in Aerospace Conference, 9th, San Diego, CA, Oct. 19-21, 1993, Technical Papers. Pt. 2 (A94-11401 01-62)
Publisher: American Institute of Aeronautics and Astronautics
Subject Category
Computer Systems
Report/Patent Number
AIAA PAPER 93-4664
Accession Number
94A11534
Funding Number(s)
CONTRACT_GRANT: NAG1-613
CONTRACT_GRANT: NCC2-481
Distribution Limits
Public
Copyright
Other

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