Dynamic Multi-objective Service Function Chain Placement Based on Deep Reinforcement Learning

Service function chain placement is crucial to support services flexibility and diversity for different users and vendors. Specifically, this problem is proved to be NP-hard. Existing deep reinforcement learning based methods either can only handle a limited number of objectives, or their training t...

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Veröffentlicht in:	IEEE eTransactions on network and service management 2024-08, p.1-1
Hauptverfasser:	Zhou, Cong, Zhao, Baokang, Tang, Fengxiao, Han, Biao, Wang, Baosheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Computational modeling Deep reinforcement learning Heuristic algorithms multi-objective deep reinforcement learning Optimization related zone decomposition service function chain placement Service function chaining Training Vectors
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