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Communication Dans Un Congrès Année : 2020

AZTEC: Anticipatory Capacity Allocation for Zero-Touch Network Slicing

Résumé

The combination of network softwarization with network slicing enables the provisioning of very diverse services over the same network infrastructure. However, it also creates a complex environment where the orchestration of network resources cannot be guided by traditional, human-in-the-loop network management approaches. New solutions that perform these tasks automatically and in advance are needed, paving the way to zero-touch network slicing. In this paper, we propose AZTEC, a data-driven framework that effectively allocates capacity to individual slices by adopting an original multi-timescale forecasting model. Hinging on a combination of Deep Learning architectures and a traditional optimization algorithm, AZTEC anticipates resource assignments that minimize the comprehensive management costs induced by resource overprovisioning, instantiation and recon-figuration, as well as by denied traffic demands. Experiments with real-world mobile data traffic show that AZTEC dynamically adapts to traffic fluctuations, and largely outperforms state-of-the-art solutions for network resource orchestration.
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Dates et versions

hal-02904054 , version 1 (21-07-2020)

Identifiants

  • HAL Id : hal-02904054 , version 1

Citer

Dario Bega, Marco Gramaglia, Marco Fiore, Albert Banchs, Xavier Costa-Perez. AZTEC: Anticipatory Capacity Allocation for Zero-Touch Network Slicing. IEEE INFOCOM, Jul 2020, Toronto, Canada. ⟨hal-02904054⟩

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