A Dynamic Distributed Scheduler for Computing on the Edge

TL;DR

This paper presents a distributed scheduler for edge computing that dynamically manages resources to meet latency and privacy constraints, improving task scheduling efficiency in resource-limited, variable environments.

Contribution

It introduces a novel distributed scheduling approach specifically designed for dynamic, resource-constrained edge environments, with real-world AI application evaluation.

Findings

Outperforms existing schedulers in meeting application constraints

Effectively manages resources in highly hybrid, dynamic environments

Demonstrates improved latency and privacy compliance

Abstract

Edge computing has become a promising computing paradigm for building IoT (Internet of Things) applications, particularly for applications with specific constraints such as latency or privacy requirements. Due to resource constraints at the edge, it is important to efficiently utilize all available computing resources to satisfy these constraints. A key challenge in utilizing these computing resources is the scheduling of different computing tasks in a dynamically varying, highly hybrid computing environment. This paper described the design, implementation, and evaluation of a distributed scheduler for the edge that constantly monitors the current state of the computing infrastructure and dynamically schedules various computing tasks to ensure that all application constraints are met. This scheduler has been extensively evaluated with real-world AI applications under different scenarios and demonstrates that it outperforms current scheduling approaches in satisfying various application constraints.