Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems - Optimisation Dynamique de Requêtes Réparties à grande échelle
Communication Dans Un Congrès Année : 2024

Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems

Résumé

Given its intuitive nature, many Cloud providers opt for threshold-based data replication to enable automatic resource scaling. However, setting thresholds effectively needs human intervention to calibrate thresholds for each metric and requires a deep knowledge of current workload trends, which can be challenging to achieve. Reinforcement learning is used in many areas related to the Cloud Computing, and it is a promising field to get automatic data replication strategies. In this work, we survey data replication strategies and data scaling based on reinforcement learning (RL).
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Dates et versions

hal-04723684 , version 1 (07-10-2024)
hal-04723684 , version 2 (11-10-2024)

Identifiants

  • HAL Id : hal-04723684 , version 2

Citer

Fahem Arar, Riad Mokadem, Djamel Eddine Zegour. Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems. Doctoral Conference on computer Science ADCCS’2024, Ecole Supérieure d'Informatique ESI, May 2024, Algiers, Algeria. ⟨hal-04723684v2⟩
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