NDLI: Resource-usage prediction for demand-based network-computing

Content Provider	IEEE Xplore Digital Library
Author	Kapadia, N.H. Brodley, C.E. Fortes, J.A.B. Lundstrom, M.S.
Copyright Year	1998
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA (Kapadia, N.H.)
Abstract	This paper reports on an application of artificial intelligence to achieve demand-based scheduling within the context of a network-computing infrastructure. The described AI system uses tool-specific, run-time input to predict the resource-usage characteristics of runs. Instance-based learning with locally weighted polynomial regression is employed because of the need to simultaneously learn multiple polynomial concepts and the fact that knowledge is acquired incrementally in this domain. An innovative use of a two-level knowledge base allows the system to account for short-term variations in compute-server and network performance and exploit temporal and spatial locality of runs. Instance editing allows the approach to be tolerant to noise and computationally feasible for extended use. The learning system was tested on three tools during normal use of the Purdue University Network Computing Hubs. Results indicate that the described instance-based learning technique using locally weighted regression with a locally linear model works well for this domain.
Starting Page	372
Ending Page	377
File Size	245894
Page Count	6
File Format	PDF
ISBN	0818692189
ISSN	10609857
DOI	10.1109/RELDIS.1998.740526
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-10-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer networks Runtime Intelligent networks Application software Processor scheduling Artificial intelligence Polynomials Learning systems System testing Software tools
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Networks and Communications Software Hardware and Architecture

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