Efficient Data-Driven Network Functions
| Autor: | Zhiyuan Yao, Yoann Desmouceaux, Juan Antonio Cordero Fuertes, Mark Townsley, Thomas Heide Clausen |
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| Přispěvatelé: | Yao, Zhiyuan |
| Rok vydání: | 2022 |
| Předmět: |
Computer Science - Networking and Internet Architecture
high performance network [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] Networking and Internet Architecture (cs.NI) FOS: Computer and information sciences Virtual Network Functions [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI] data-driven [INFO.INFO-DC] Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] cloud performance evaluation |
| Zdroj: | HAL |
| DOI: | 10.48550/arxiv.2208.11385 |
| Popis: | Cloud environments require dynamic and adaptive networking policies. It is preferred to use heuristics over advanced learning algorithms in Virtual Network Functions (VNFs) in production becuase of high-performance constraints. This paper proposes Aquarius to passively yet efficiently gather observations and enable the use of machine learning to collect, infer, and supply accurate networking state information-without incurring additional signalling and management overhead. This paper illustrates the use of Aquarius with a traffic classifier, an autoscaling system, and a load balancer-and demonstrates the use of three different machine learning paradigms-unsupervised, supervised, and reinforcement learning, within Aquarius, for inferring network state. Testbed evaluations show that Aquarius increases network state visibility and brings notable performance gains with low overhead. |
| Databáze: | OpenAIRE |
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