Controle de mobilidade inteligente orientado por CloudNetwork Slicing em infraestruturas 5G

Abstract

In the context of the fifth generation of mobile networks (5G), network slicing (NS) has been widely adopted to enable infrastructure for deploying services in a personalized and elastic way. NS is promoted through network resource components that can be extended through virtualization and softwarization strategies. The cloud-network slicing (CNS) approach has recently been introduced as an alternative to meet the demands of industry verticals that offer services across multiple administrative and technological domains distributed across cloud and network infrastructures. In this scenario, characterized by the certainty of handover between the different cells in the radio access network (RAN), the infrastructure management system must be extended with improved capabilities to maintain user experience during mobility events. In CNS-defined systems, decision mechanisms require complete knowledge of active CNS instances, their computational and network requirements, and service consumer nodes, among other aspects. A recent literature survey revealed works promoting mobility management in NS-defined systems but the lack of mechanisms aware and controlled by CNS. Furthermore, existing mechanisms manage the mobility of entities associated with NSs, considering classical models based on signal strength. The research developed in this PhD thesis fills this gap by paving the way for 5G CNS-defined systems based on an approach with automated and proactive mobility control and management capabilities. The main contributions of this work include: (1) extensive review and discussion of quality-oriented handover decision mechanisms compatible with the critical requirements imposed by 5G verticals in systems defined by CNS; (2) CNS-driven mobility management and control approach in an automated and proactive way, capable of keeping mobile users of CNS instances always well-connected and served, respecting end-to-end definitions and the high level of isolation; (3) mobility control driven by the compliance of CNS resources and users’ quality requirements; (4) intelligent mobility prediction and decision to enable mobile users with seamless and transparent connectivity when selecting the best access point for CNS services; (5) evaluation of the proposed solution in an emulated environment composed of technologies that enable 5G systems. The performance of the proposed solution was compared with the main state-of-the-art works. It demonstrated a better ability to make mobility decisions driven by the critical requirements of the CNS in terms of compliance with service-level agreements (SLA) and users’ quality of experience (QoE).