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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Vydáno v:2022 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) s. 144 - 155
Hlavní autoři: Baresi, Luciano, Hu, Davide Yi Xian, Quattrocchi, Giovanni, Terracciano, Luca
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 01.05.2022
Témata:
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
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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 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.
AbstractList 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.
Author Hu, Davide Yi Xian
Baresi, Luciano
Quattrocchi, Giovanni
Terracciano, Luca
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StartPage 144
SubjectTerms Computer architecture
control theory
dynamic resource allocation
edge computing
gpu
Machine learning
Multi-access edge computing
Organizations
placement
Prototypes
Scheduling algorithms
serverless
Time factors
Title NEPTUNE: Network- and GPU-aware Management of Serverless Functions at the Edge
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