  Computing systems have been evolving to be more pervasive, heterogeneous, and
dynamic. An increasing number of emerging domains now rely on diverse edge to
cloud continuum where the execution of applications often spans various tiers
of systems with significantly heterogeneous computational capabilities.
Resources in each tier are often handled in isolation due to scalability and
privacy concerns. However, better overall resource utilization could be
achieved if different tiers of systems had the means to communicate their
computational capabilities.
  In this paper, we propose H-EYE, a universal approach to holistically capture
diverse computational characteristics of edge-cloud systems with arbitrary
topologies and to manage the assignment of tasks to the computational resources
with the whole continuum in the scope. Our proposed work introduces two
significant innovations: (1) We present a multi-layer, graph-based hardware
(HW) representation and a modular performance modeling interface that could
capture interactions and inference between different computing and
communication resources in the system at desired level of detail. (2) We
introduce a novel orchestrator mechanism that leverages the graph-based HW
representation to hierarchically locate target devices that a given set of
tasks could be mapped to. Orchestrator provides isolation for various device
groups and allows hierarchical abstraction to scalably find mappings that
satisfy system deadlines. The orchestrator internally relies on a novel
traverser that takes shared resource slowdown into account. We demonstrate the
utility and flexibility of H-EYE on edge-server systems that are deployed on
the field in two different disciplines, improving up to 47% latency over
baselines with less than 2% scheduling overhead
