This work is sponsored
by
Office of Naval Research (ONR)
as part of the
DoD Multidisciplinary University Research Initiative (MURI)
Project
CONTESSA
High speed network and increasingly powerful desktop machines boosted the
emergence of grid computing that was intended to facilitate computational-intensive and
data-intensive applications. After more than ten years of research, people
start trying to apply grid services into operational usage over the
Internet. One perspective is to leverage grid resources to support
advanced mobile applications, which are driven by rapid evolution in
versatile handheld devices and wireless networks but still challenged by
innate constraints of mobile environments. Major issues, such as: user
mobility, device energy deficiency and dynamic network connectivity remain
to be tackled to improve system performance and reliability. Our MAPGrid
project is one of the research efforts that address how to provide effective QoS for mobile
applications by leveraging grid resources.
We focus on devising efficient resource discovery algorithms and data placement strategies for providing mobile users with multimedia services by leveraging heterogeneous and intermittently available grid resources. The objective is to support diverse QoS requirements of mobile applications while improving overall system performance in terms of client admission ratio, grid throughput and grid utilization. For instance, by exploiting the knowledge of client mobility patterns, device energy profiles and grid resource availability, we determine localized computational and storage resources within the grid for enhancing overall user experiences for mobile applications. We have applied techniques from graph theory, neural nets, etc. to deal with quality-aware discovery of grid resources for QoS-based mobile applications (e.g. streaming multimedia). We have devised predictive data placement techniques to cache multimedia segments on grid machines for better system performance. We also proposed an integrated solution that adapts to dynamic changes in device energy consumption and unpredictable grid resource availability without compromising application QoS.
In future work, we will develop techniques to enhance the system predictability using statistic models and to improve adaptive interoperability through policy studies. We will continue working on system performance analysis and optimization in the prototype implementation.