Deep Reinforcement Learning Approaches to Network Slice Scaling and Placement: A Survey

Network slicing in 5G and beyond networks allows the network to be customized for each application or service by chaining together different virtualized network functions (VNFs) according to service requirements. The increased flexibility offered by network slicing comes at the cost of complexity in management and orchestration, which cannot be solved by traditional reactive human-in-the-loop solutions. This necessitates minimizing human intervention through the use of artificial intelligence techniques (zero touch network management). In particular, the scaling and placement of the chain of VNFs that constitute a network slice is a complex combinatorial optimization problem that is difficult to solve effectively with traditional approaches. Driven by the benefits of deep reinforcement learning (DRL) in solving various combinatorial optimization problems, in this article, we survey various DRL-based approaches to slice scaling and placement, including different ways to model the problem and benefits of various DRL techniques in addressing specific aspects of the problem. Further, we highlight key challenges and open issues in the effective use of DRL for network slice scaling and placement.

Automated summary

Network slicing in 5G and beyond networks customizes the network for each application or service by chaining virtualized network functions (VNFs) according to service requirements. The increased flexibility of network slicing comes with management complexity that cannot be solved by traditional solutions, and therefore requires minimizing human intervention through the use of artificial intelligence techniques. This article surveys various deep reinforcement learning (DRL)-based approaches to slice scaling and placement, highlighting their benefits and addressing key challenges and open issues.