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مقاله استفاده از تکنیکهای یادگیری ماشینی برای بهبود زمانبندی فرایند لینوکس
ترجمه این مقاله در 17 صفحه و در فایل ورد است فایل دانلودی پس از خرید فایل زیپ است که شامل فایل ورد و پی دی اف ترجمه مقاله و فایل اصلی مقاله به زبان انگلیسی است
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| دسته بندی | کامپیوتر و IT |
| فرمت فایل | zip |
| حجم فایل | 1792 کیلو بایت |
| تعداد صفحات فایل | 17 |
Applying Machine Learning Techniques to Improve Linux Process Scheduling
AbstractIn this work we use Machine Learning (ML)
techniques
to learn the CPU time-slice utilization behavior of
known
programs in a Linux system. Learning is done by an analysis
of
certain static and dynamic attributes of the processes while they
are
being run. Our objective was to discover the most important
static and
dynamic attributes of the processes that can help best
in prediction of CPU
burst times which minimize the process
TaT (Turn-around-Time). In our
experimentation we modify the
Linux Kernel scheduler (version 2.4.20-8) to
allow scheduling
with customized time slices. The Waikato Environment
for
Knowledge Analysis (Weka), an open source machine-learning
tool is
used to nd the most suitable ML method to characterize
our programs. We
experimentally nd that the C4.5 Decision
Tree algorithm most effectively
solved the problem. We nd that
predictive scheduling could reduce TaT in the
range of 1:4%
to 5:8%. This was due to a reduction in the number of
context
switches needed to complete the process execution. We nd
our
result interesting in the context that generally operating
systems
presently never make use of a program's previous execution
history
in their scheduling behavior.
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استفاده از تکنیکهای یادگیری ماشینی برای بهبود زمانبندی فرایند لینوکس
