Intelligent and Collaborative Orchestration of Network Slices

5G and beyond network will support vertical industry applications, and the resource requirements of each service vary widely. The introduction of network slices provides great flexibility to the network, which can realize the differentiated customization requirements of service. However, while determining how to intelligently orchestrate the network slices is an important challenge, current solutions rarely treat multiple customized requirements of delay, bandwidth, load balancing, and slice isolation. In this article, network slice orchestration is considered from the perspective of slice isolation and cloud-edge collaboration. First, differentiated isolation level requirements are restricted to constraints, the customized isolation is realized. Second, bandwidth is saved and network latency is reduced via the collaboration of cloud and edge data centers. In addition, exclusive orchestration optimization objectives that match various service needs are proposed to distinguish the specific requirements of different slices. Finally, two deep reinforcement learning-based algorithms are proposed. The experimental results demonstrate that the proposed algorithms can optimize the objectives while ensuring differentiated isolation levels. For typical slices, the proposed algorithms respectively reduce bandwidth consumption by about 29% and 64%, reduce slice delay by about 14% and 70%, and optimize load balancing by about 17% and 23%.

Automated summary

5G and beyond network will support vertical industry applications with varying resource requirements for each service. The introduction of network slices provides flexibility to meet customization requirements. However, existing solutions rarely consider multiple customized requirements such as delay, bandwidth, load balancing, and slice isolation. This article proposes algorithms based on deep reinforcement learning to optimize network slice orchestration and demonstrates significant improvements in bandwidth consumption, slice delay, and load balancing for typical slices.