MLFV: Network-Aware Orchestration for Placing Chains of Virtualized Machine Learning Functions
| Autor: | Magnos Martinello, Celio Trois, João Carlos D. Lima, Luis C. E. Bona, João Henrique G. M. Corrêa, Diego Rossi Mafioletti, Renan Lírio de Souza, Rogério C. Turchetti, Alencar Machado |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Information privacy Computer science business.industry 020206 networking & telecommunications Cloud computing 02 engineering and technology Virtualization computer.software_genre Machine learning Network aware 020901 industrial engineering & automation Scalability 0202 electrical engineering electronic engineering information engineering Orchestration (computing) Artificial intelligence business computer Virtual network |
| Zdroj: | GLOBECOM |
| DOI: | 10.1109/globecom38437.2019.9013295 |
| Popis: | Machine Learning as a Service (MLaaS) platforms enables access to Machine Learning (ML) processing with scalable infrastructure, from anywhere, and at any time, but requires sending large amounts of data to the cloud. ML on the edge is emerging as an option to reduce latency and bandwidth usage, maintaining data privacy. However, the existing edge approaches are not aware of the current network state for orchestrating the tasks. Network- aware orchestration services are supported by the Network Function Virtualization (NFV) architecture, making it a promising approach to manage and place ML tasks. In this paper, we propose Machine Learning Function Virtualization (MLFV), a fully network-aware framework that explores the NFV environment to virtualize ML tasks as virtual network functions. We describe a novel model for placing chains of ML tasks, considering constraints on CPU, memory, the existence of ML libraries, and the network overload, aiming to reduce the overall execution time of a chain. The results showed that MLFV outperformed existing cloud and edge approaches, particularly when network connections present instabilities. MLFV was able to identify the irregularities, allocating the ML tasks on hosts with normal connections, and thus, reducing the time for classifying single and multiple concurrent requests. |
| Databáze: | OpenAIRE |
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