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Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle
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  • Joberto Martins ,
  • Adnei Donatti ,
  • Sand L. Correa ,
  • Cristiano B. Both ,
  • Flávio de Oliveira Silva ,
  • José A. Suruagy ,
  • Rafael Pasquini ,
  • Rodrigo Moreira ,
  • Kleber V. Cardoso ,
  • Tereza C. Carvalho ,
  • Antonio Abelem
Joberto Martins
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Adnei Donatti
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Sand L. Correa
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Cristiano B. Both
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Flávio de Oliveira Silva
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José A. Suruagy
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Rafael Pasquini
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Rodrigo Moreira
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Kleber V. Cardoso
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Tereza C. Carvalho
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Antonio Abelem
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Abstract

Network slicing (NS) is becoming an essential element of service management and orchestration in communication networks, starting from mobile cellular networks and extending to a global initiative. NS can reshape the deployment and operation of traditional services, support the introduction of new ones, vastly advance how resource allocation performs in networks, and notably change the user experience. Most of these promises still need to reach the real world, but they have already demonstrated their capabilities in many experimental infrastructures. However, complexity, scale, and dynamism are pressuring for a Machine Learning (ML)-enabled NS approach in which autonomy and efficiency are critical features. This trend is relatively new but growing fast and attracting much attention. This article surveys Artificial Intelligence-enabled NS and its potential use in current and future infrastructures. We have covered state-of-the-art ML-enabled NS for all network segments and organized the literature according to the phases of the NS life cycle. We also discuss challenges and opportunities in research on this topic.