  The next generation of many-core enabled large-scale computing systems relies
on thousands of billions of heterogeneous processing cores connected to form a
single computing unit. In such large-scale computing environments, resource
management is one of the most challenging, and complex issues for efficient
resource sharing and utilization, particularly as we move toward Future
ManyCore Systems (FMCS). This work proposes a novel resource management scheme
for future peta-scale many-core-enabled computing systems, based on hybrid
adaptive resource discovery, called ElCore. The proposed architecture contains
a set of modules which will dynamically be instantiated on the nodes in the
distributed system on demand. Our approach provides flexibility to allocate the
required set of resources for various types of processes/applications. It can
also be considered as a generic solution (with respect to the general
requirements of large scale computing environments) which brings a set of
interesting features (such as auto-scaling, multitenancy, multi-dimensional
mapping, etc,.) to facilitate its easy adaptation to any distributed technology
(such as SOA, Grid and HPC many-core). The achieved evaluation results assured
the significant scalability and the high quality resource mapping of the
proposed resource discovery and management over highly heterogeneous,
hierarchical and dynamic computing environments with respect to several
scalability and efficiency aspects while supporting flexible and complex
queries with guaranteed discovery results accuracy. The simulation results
prove that, using our approach, the mapping between processes and resources can
be done with high level of accuracy which potentially leads to a significant
enhancement in the overall system performance.
