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DRL-based Slice Placement under Realistic Network Load Conditions

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Amina Boubendir
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  • PersonId : 1080196
Fabice Guillemin
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  • PersonId : 1080198
Pierre Sens

Abstract

We propose to demonstrate a network slice placement optimization solution based on Deep Reinforcement Learning (DRL), referred to as Heuristically-controlled DRL, which uses a heuristic to control the DRL algorithm convergence. The solution is adapted to realistic networks with large scale and under non-stationary traffic conditions (namely, the network load). We demonstrate the applicability of the proposed solution and its higher and stable performance over a non-controlled DRL-based solution. Demonstration scenarios include full online learning with multiple volatile network slice placement request arrivals.
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Dates and versions

hal-03516310 , version 1 (07-01-2022)

Identifiers

  • HAL Id : hal-03516310 , version 1

Cite

Jose Jurandir Alves Esteves, Amina Boubendir, Fabice Guillemin, Pierre Sens. DRL-based Slice Placement under Realistic Network Load Conditions. CNSM 2021 - 17th International Conference on Network and Service Management, Oct 2021, Izmir, Turkey. ⟨hal-03516310⟩
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