Multi-Objective Deep Reinforcement Learning Assisted Service Function Chains Placement

Research output: Contribution to journalArticle (Academic Journal)peer-review

23 Citations (Scopus)


The study of Service Function Chains (SFCs) placement problem is crucial to support services flexibly and use resources efficiently. Solutions should satisfy various Quality of Service requirements, avoid edge resource congestion, and improve service acceptance ratio (SAR). This work presents a novel approach to address these challenges by solving a multi-objective SFCs placement problem based on the Pointer Network in multi-layer edge and cloud networks. We design a Deep Reinforcement Learning algorithm, called Chebyshev-assisted Actor-Critic SFCs Placement Algorithm, to overcome the limitations of traditional heuristic and evolutionary algorithms. Then, we run this algorithm iteratively with a set of weights to obtain non-dominated fronts, which have much higher hypervolume values than those obtained from other state-of-the-art algorithms. Moreover, running our algorithm individually with selected weights from non-dominated fronts can avoid edge resource congestion and achieve 98% SARs of low-latency services during high-workload periods. Finally, based on both simulation and real testbed experimental results, it is validated that the proposed algorithm fits for pragmatic service deployment while achieving 100% of SARs in the use cases deployed on the testbed.

Original languageEnglish
Pages (from-to)4134-4150
Number of pages17
JournalIEEE Transactions on Network and Service Management
Issue number4
Early online date15 Nov 2021
Publication statusPublished - Mar 2022

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.


  • Approximation algorithms
  • Cloud computing
  • Computational modeling
  • Costs
  • Heuristic algorithms
  • Multi-Access Edge Computing
  • Multi-Objective Deep Reinforcement Learning
  • Network Function Virtualisation
  • Optical Network.
  • Optimization
  • Quality of service
  • Service Function Chaining


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