| Autor: |
Yao, Zhiyuan, Desmouceaux, Yoann, Cordero-Fuertes, Juan-Antonio, Townsley, Mark, Clausen, Thomas Heide |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
30th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2022) |
| Druh dokumentu: |
Working Paper |
| 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: |
arXiv |
| Externí odkaz: |
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