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Conference Papers Year : 2021

DRL-based Slice Placement under Realistic Network Load Conditions

Amina Boubendir
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Fabice Guillemin
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Pierre Sens


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)


  • HAL Id : hal-03516310 , version 1


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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