FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems

TL;DR

This paper introduces FELARE, a lightweight, fair scheduling heuristic for heterogeneous edge systems that optimizes energy and latency for machine learning tasks, improving task completion and energy efficiency.

Contribution

The paper proposes a novel fair, multi-objective heuristic for resource allocation in energy-limited, heterogeneous edge systems, outperforming existing methods.

Findings

8.9% increase in on-time task completion rate

12.6% energy savings

Effective at low to moderate request rates

Abstract

Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGAs) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.