Multimedia data is rapidly gaining importance along with recent
deployment of publicly accessible digital television archives,
surveillance cameras in public locations, and automatic comparison
of forensic video evidence.
In a few years, analyzing the content of multimedia data will
be a problem of phenomenal proportions, as digital video may produce
high data rates, and multimedia archives steadily run into Petabytes of
Consequently, for urgent and scientifically challenging problems in
multimedia content analysis, Grid computing is rapidly becoming
Emerging multimedia applications often must meet strict time
constraints, even under variable workloads, data-dependent
computation, and dynamic resource availability.
Hence, multimedia Grid applications must be made variability-tolerant
by way of controlled adaptive resource utilization.
This raises the need for new stochastic runtime performance control
methodologies that react to the continuously changing circumstances
in large-scale Grid systems.
The JADE-MM project aims to develop stochastic control schemes that make
time-constrained multimedia applications tolerant to the dynamics of
large-scale Grid environments, and to integrate these control schemes into
a software framework for
large-scale multimedia content analysis.
The project is divided in three complementary parts.
Part one will research platform independent techniques for
parallelization and optimization of multimedia applications.
Part two will develop performance models and control schemes that
are robust to the dynamics of Grid environments.
Part three will research coordinated methods to reliable, adaptive,
and efficient use of Grid resources.
Throughout the project, results from all three parts will be integrated
via software prototypes for immediate, application-driven evaluation.
A key aspect of the project is that it requires a multi-disciplinary
approach, involving new and existing solutions from the fields of multimedia
content analysis (MMCA), mathematical modeling, and Grid computing.
If successful, the project will significantly benefit all these research areas.
For MMCA, JADE-MM will vastly enlarge the possibilities of its application areas,
like surveillance camera deployment or access to petascale multimedia archives.
Also, JADE-MM will allow the design of better MMCA algorithms, by enabling in-depth
testing with colossal data sets. Also, a versatile MMCA execution environment is
bringing deeper understanding in these algorithms and their efficiency.
For mathematical modeling, the project contains a variety of innovations.
In particular, a challenging complication occurring in performance control
models for Grid environments is that the speed of compute and network resources
may change over time, which is fundamentally different
from the classical performance control models where servers are
autonomous entities, operating at a constant speed.
This time-variability may have a dramatic impact on performance,
requiring advancement of the theory way beyond the state-of-the-art.
Moreover, the potential explosion of the model state space
raises new challenges for the development of approximate control
schemes to force control actions on-the-fly.
For Grid computing, the research will be
right on the cutting edge of existing approaches.
Grid application environments are slowly maturing towards
being able to reliably submit and run applications.
While fault-tolerance mechanisms for Grid applications are in their
infancy, no viable approaches exist for fulfilling the elementary
promise of Grids: let 'the Grid' automatically execute applications
on the most suitable resources.
We are aware that this is a very challenging problem.
Focusing on the MMCA domain will limit this hard
problem to a tractable complexity.
We expect that, finally, our results will be applicable to other
application domains or even to more generally applicable runtime