Year: 2025
Type: conference-paper
Abstract: Computing systems are evolving to be more ubiquitous, heterogeneous, and dynamic.Many emerging domains, such as Internet of Things (IoT), federated learning, and smart buildings, rely on a diverse edge-to-cloud continuum where the execution of applications spans various tiers of systems with significantly different computational capabilities.Computing resources in each tier, such as processing units inside of in-the-field edge devices and high-performance servers in datacenters, are handled in isolation due to scalability and resource segregation.This practice results in task mappings limited to only a subset of all available processing units, preventing an efficient overall utilization of the system.In this paper, we propose a holistic approach to capture diverse computational characteristics of edge-cloud systems with arbitrary topologies and to efficiently manage computational resources with the whole continuum in the scope.Our approach is built upon a multi-layer graph-based hardware (HW) representation and a modular performance modeling interface that can capture interactions and interference between computational resources in the system.We introduce an orchestrator mechanism that leverages the graph-based HW representation to hierarchically locate processing units to which a given set of tasks can be mapped while respecting the isolation between the computational tiers of an edge-cloud system.We demonstrate the utility of our approach on two distinct edge-cloud systems deployed in the field, improving the latency up to 47% over the best baseline with less than 2% scheduling overhead and reducing the average prediction error rate from 27.4% to 3.2%.
Authors: Ismet Dagli, Justin Davis, Mehmet Esat Belviranli
Institution: Colorado School of Mines
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
OpenAlex ID: W4413428976