NEPTUNE: Network- and GPU-aware Management of Serverless Functions at the Edge

TL;DR

NEPTUNE is a serverless framework designed for edge computing that optimizes function placement, resource allocation, and GPU utilization to reduce latency and resource use in resource-constrained MEC environments.

Contribution

The paper introduces NEPTUNE, a novel serverless management framework for MEC that dynamically allocates resources and exploits GPUs to improve performance.

Findings

Significant reduction in response time

Lower network overhead

Decreased resource consumption

Abstract

Nowadays a wide range of applications is constrained by low-latency requirements that cloud infrastructures cannot meet. Multi-access Edge Computing (MEC) has been proposed as the reference architecture for executing applications closer to users and reduce latency, but new challenges arise: edge nodes are resource-constrained, the workload can vary significantly since users are nomadic, and task complexity is increasing (e.g., machine learning inference). To overcome these problems, the paper presents NEPTUNE, a serverless-based framework for managing complex MEC solutions. NEPTUNE i) places functions on edge nodes according to user locations, ii) avoids the saturation of single nodes, iii) exploits GPUs when available, and iv) allocates resources (CPU cores) dynamically to meet foreseen execution times. A prototype, built on top of K3S, was used to evaluate NEPTUNE on a set of experiments that demonstrate a significant reduction in terms of response time, network overhead, and resource consumption compared to three state-of-the-art approaches.