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Conference Papers Year : 2022

Efficient Data-Driven Network Functions


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.
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Dates and versions

hal-03753202 , version 1 (18-08-2022)


  • HAL Id : hal-03753202 , version 1


Zhiyuan Yao, Yoann Desmouceaux, Juan Antonio Cordero Fuertes, Mark Townsley, Thomas Heide Clausen. Efficient Data-Driven Network Functions. 30th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2022), Oct 2022, Nice, France. ⟨hal-03753202⟩
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