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Performance modeling and evaluation
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At ZRL there is extensive knowledge of and simulation experience
with graphical models, including factor graphs and Bayesan belief
networks.
In particular this means probabilistic inference in graphical models
using the probability propagation approach known as the "belief
propagation algorithm" or the "sum product algorithm".
The applications of such graphical models with sometimes millions
of nodes are
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pattern classification, |
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unsupervised learning, |
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data compression and |
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channel coding. |
The team at ZRL has particularly vast experience with the latter
application.
Deep Computing technology is used for performance evaluation in
terms of probability of error or failure of communication and storage
systems. The ZRL team has particular expertise with Monte Carlo
simulations of such complex systems. These extensive simulations
involve adaptive structures and complex algorithms that must be
validated over extremely long sequences of excitation signals.
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The Storage Technologies group at ZRL is investigating the application of advanced
error-control
coding schemes such as low-density parity-check (LDPC) codes to magnetic tape
storage systems
as well as to large-scale RAID systems. Both of these storage systems are key
offerings of IBM's
Systems and Technology group.
Future tape systems aiming to achieve very high storage densities will need
to operate in the presence
of increased distortion, noise, and other impairments. LDPC codes offer a practical
and high-performance
alternative to the currently used error-control coding scheme.
Large, Internet-scale data storage systems pose significant challenges in meeting
the reliability and
availability needs. The work at ZRL explores the application of graph-based,
LDPC-like codes to
the design of such storage systems.
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