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Thesis
Home   /   Thesis   /   IO access scheduling on magnetic tapes using machine learning

IO access scheduling on magnetic tapes using machine learning

Computer science and software Engineering sciences

Abstract

Numerical simulations are used to obtain responses to physical phenomena that
cannot be reproduced, either because they are too dangerous or too expensive.
The models used for these simulations are increasingly complex, in terms of
size and precision, and require access to increasingly large computing and
data storage capacities. To this end, and in order to optimize costs, the use
of mass storage technologies such as magnetic tapes is critical. However, to
ensure good overall system performance, the development of algorithms and
mechanisms related to data placement and tape access scheduling is essential.
The objective of the thesis is to study the technology of magnetic tapes, as
well as existing mechanisms such as RAO (Recommended Access Order) or request
retention; and to implement new strategies for the optimization of magnetic
tape performance.

Laboratory

Département centres de calcul, logiciels et codes
DCLC
DCLC
Versailles-St-Quentin-en-Yvelines
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