# Pilot-Edge: Distributed Resource Management Along the Edge-to-Cloud Continuum

arXiv: https://arxiv.org/abs/2104.03374 · HTML: https://mlsanity.com/paper/2104.03374/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2021.

## Built on by (most cited first, 2 of 2)

- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics](https://mlsanity.com/paper/2205.04622/) (2022)

## Abstract (arXiv)

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).