  Many science and industry IoT applications necessitate data processing across
the edge-to-cloud continuum to meet performance, security, cost, and privacy
requirements. However, diverse abstractions and infrastructures for managing
resources and tasks across the edge-to-cloud scenario are required. We propose
Pilot-Edge as a common abstraction for resource management across the
edge-to-cloud continuum. Pilot-Edge is based on the pilot abstraction, which
decouples resource and workload management, and provides a
Function-as-a-Service (FaaS) interface for application-level tasks. The
abstraction allows applications to encapsulate common functions in high-level
tasks that can then be configured and deployed across the continuum. We
characterize Pilot-Edge on geographically distributed infrastructures using
machine learning workloads (e.g., k-means and auto-encoders). Our experiments
demonstrate how Pilot-Edge manages distributed resources and allows
applications to evaluate task placement based on multiple factors (e.g., model
complexities, throughput, and latency).
