  As the next generation of diverse workloads like autonomous driving and
augmented/virtual reality evolves, computation is shifting from cloud-based
services to the edge, leading to the emergence of a cloud-edge compute
continuum. This continuum promises a wide spectrum of deployment opportunities
for workloads that can leverage the strengths of cloud (scalable
infrastructure, high reliability) and edge (energy efficient, low latencies).
Despite its promises, the continuum has only been studied in silos of various
computing models, thus lacking strong end-to-end theoretical and engineering
foundations for computing and resource management across the continuum.
Consequently, developers resort to ad hoc approaches to reason about
performance and resource utilization of workloads in the continuum. In this
work, we conduct a first-of-its-kind systematic study of various computing
models, identify salient properties, and make a case to unify them under a
compute continuum reference architecture. This architecture provides an
end-to-end analysis framework for developers to reason about resource
management, workload distribution, and performance analysis. We demonstrate the
utility of the reference architecture by analyzing two popular continuum
workloads, deep learning and industrial IoT. We have developed an accompanying
deployment and benchmarking framework and first-order analytical model for
quantitative reasoning of continuum workloads. The framework is open-sourced
and available at https://github.com/atlarge-research/continuum.
