Suchergebnisse

Network Function Virtualization (NFV) is one of the main enablers behind the promised improvements in the Fifth Generation (5G) networking era. Thanks to this concept, Network Functions (NFs) are evolving into software components (e.g., Virtual Network Functions (VNFs)) that can be deployed in general-purpose servers following a cloud-based approach. In this way, NFs can be deployed at scale, fulfilling a great variety of service requirements. Unfortunately, the complexity in the management and orchestration of NFV-based networks has increased due to the diverse demands from a growing number of network services. Such complexity calls for an automated and autonomous solution that self adapts to the needs of those network services. In this paper, we propose and compare a Deep Reinforcement Learning (DRL) agent, a classical Proportional–Integral–Derivative (PID) controller, and a Threshold (THD)-based algorithm for autonomously determining the amount of VNF instances to fulfill a service latency requirement without knowing or predicting the expected demand. Finally, we present a comparison of the three approaches in terms of created VNFs and peak latency performed in a discrete event simulator. ; This research was partially funded by Ctrl App, a research project of the Fund for Scientific Research Flanders (FWO) under grant agreement No. G055619N, and by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 101017109 (DAEMON).

Next-generation mobile networks are expected to flaunt highly (if not fully) automated management. Network Intelligence (NI) will be the key enabler for such a vision, empowering myriad of orchestrators and controllers across network domains. In this paper, we elaborate on the DAEMON architectural model, which proposes introducing a NI Orchestration layer for the effective end-to-end coordination of NI instances deployed across the whole mobile network infrastructure. Specifically, we first outline requirements and specifications for NI design that stem from data management, control timescales, and network technology characteristics. Then, we build on such analysis to derive initial principles for the design of the NI Orchestration layer, focusing on (i) proposals for the interaction loop between NI instances and the NI Orchestrator, and (ii) a unified representation of NI algorithms based on an extended MAPE-K model. Our work contributes to the definition of the interfaces and operation of a NI Orchestration layer that foster a native integration of NI in mobile network architectures. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no.101017109 "DAEMON".

Next-generation mobile networks are expected to flaunt highly (if not fully) automated management. Network Intelligence (NI) will be the key enabler for such a vision, empowering myriad of orchestrators and controllers across network domains. In this paper, we elaborate on the DAEMON architectural model, which proposes introducing a NI Orchestration layer for the effective end-to-end coordination of NI instances deployed across the whole mobile network infrastructure. Specifically, we first outline requirements and specifications for NI design that stem from data management, control timescales, and network technology characteristics. Then, we build on such analysis to derive initial principles for the design of the NI Orchestration layer, focusing on (i) proposals for the interaction loop between NI instances and the NI Orchestrator, and (ii) a unified representation of NI algorithms based on an extended MAPE-K model. Our work contributes to the definition of the interfaces and operation of a NI Orchestration layer that foster a native integration of NI in mobile network architectures. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no.101017109 "DAEMON".