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Efficient Data-Driven Network Functions

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Abstract

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)

Identifiers

  • HAL Id : hal-03753202 , version 1

Cite

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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