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

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 reducing latency, but new challenges arise: edge n...

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Bibliographic Details
Published in:2022 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) pp. 144 - 155
Main Authors: Baresi, Luciano, Hu, Davide Yi Xian, Quattrocchi, Giovanni, Terracciano, Luca
Format: Conference Proceeding
Language:English
Published: ACM 01.05.2022
Subjects:
Computer architecture
control theory
dynamic resource allocation
edge computing
gpu
Machine learning
Multi-access edge computing
Organizations
placement
Prototypes
Scheduling algorithms
serverless
Time factors
ISSN:2157-2321
Online Access:Get full text
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Summary: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 reducing 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 well-known approaches. CCS CONCEPTS * Theory of computation → Scheduling algorithms; * Computing methodologies →Distributed computing methodologies; * Computer systems organization →Distributed architectures.
ISSN:2157-2321
DOI:10.1145/3524844.3528051