AI-Empowered VNF Migration as a Cost-Loss-Effective Solution for Network Resilience
원문 초록
With a wide deployment of Multi-Access Edge Computing (MEC) in the Fifth Generation (5G) mobile networks, virtual network functions (VNF) can be flexibly migrated between difference locations, and therewith significantly enhances the network resilience to counter the degradation in quality of service (QoS) due to network function outages. A balance has to be taken carefully, between the loss reduced by VNF migration and the operations cost generated thereby. To achieve this in practical scenarios with realistic user behavior, it calls for models of both cost and user mobility. This paper proposes a novel cost model and a AI-empowered approach for a rational migration of stateful VNFs, which minimizes the sum of operations cost and potential loss caused by outages, and is capable to deal with the complex realistic user mobility patterns.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing
Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
The More is not the Merrier: Investigating the Effect of Client Size on Federated Learning
Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks
PolygoNet: Leveraging Simplified Polygonal Representation for Effective Image Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.