LCM

Life Cycle Management

Management
Introduced in Rel-14
The end-to-end process of managing the life cycle of network services, network functions, and network slices in a 3GPP system. It encompasses instantiation, configuration, scaling, updating, monitoring, and termination, enabling automated and efficient operation in virtualized and software-defined networks.

Description

Life Cycle Management (LCM) in 3GPP refers to the comprehensive set of management procedures and capabilities required to manage the complete life cycle of managed entities within a 3GPP network, particularly in the context of network virtualization and softwarization introduced with 5G. The primary managed entities are Network Functions (NFs), which can be virtualized (VNFs) or cloud-native (CNFs), Network Services (NS) composed of multiple interconnected NFs, and Network Slices which are logical end-to-end networks serving specific business cases. The LCM process is a core function of the Management and Orchestration (MANO) framework, as aligned with ETSI NFV, and is performed by entities like the Network Function Virtualization Orchestrator (NFVO) and the 3GPP Network Slice Management Function (NSMF).

The LCM operation follows a phased model. For a Network Service or Network Slice, it begins with the **instantiation and configuration phase**. This involves validating the deployment template (e.g., a Network Service Descriptor), allocating necessary resources (compute, storage, network), deploying the constituent VNF/CNF packages on the chosen infrastructure, and configuring the initial parameters and connectivity between functions. Following instantiation, the **runtime operation phase** includes continuous performance monitoring, fault supervision, and dynamic actions like **scaling** (in/out, up/down) to adapt to load changes, and **healing** to recover from failures by re-instantiating components. The **update and upgrade phase** manages changes, such as applying software patches or modifying service descriptors, often requiring complex rollback procedures. Finally, the **termination phase** involves gracefully shutting down the service, releasing all allocated resources, and updating the inventory.

Key architectural components involved in LCM include the **Service Management Function (SMF)** for business-level management, the **Network Slice Management Function (NSMF)** and **Network Slice Subnet Management Function (NSSMF)** for slice-specific LCM, the **NFVO** for resource orchestration, and the **Virtualized Infrastructure Manager (VIM)** for infrastructure-level control. LCM works through standardized interfaces (e.g., Os-Ma-nfvo, Or-Vnfm) and data models (e.g., based on YANG) that carry LCM operations. Its role is fundamental to achieving the agility, automation, and efficiency promised by 5G, allowing operators to rapidly deploy and elastically operate services with minimal manual intervention.

Purpose & Motivation

LCM was introduced to address the operational complexity and rigidity of traditional telecom networks built on dedicated, proprietary hardware appliances. Managing the life cycle of physical network elements was a manual, slow, and error-prone process. The shift towards Network Functions Virtualization (NFV), Software-Defined Networking (SDN), and cloud-native principles in 3GPP, starting notably in Release 14 for 5G, created a need for automated, standardized management procedures. Without LCM, the benefits of virtualization—such as rapid service deployment, elastic scaling, and efficient resource usage—could not be realized.

The primary problems LCM solves are the slow time-to-market for new services, the inefficient use of hardware resources due to static provisioning, and the high operational expenditure (OPEX) associated with manual configuration and troubleshooting. It provides a framework for treating network functions and services as software that can be managed programmatically. This was motivated by the desire to compete with web-scale cloud providers in agility and to support the diverse and dynamic requirements of 5G use cases, such as massive IoT, enhanced mobile broadband, and ultra-reliable low-latency communication, each potentially requiring its own uniquely managed network slice.

Furthermore, LCM enables closed-loop automation, where monitoring data automatically triggers LCM operations (e.g., scale-out under load), moving towards self-optimizing networks. It addresses the limitations of traditional OSS/BSS systems by defining a standardized, intent-based management interface that separates the 'what' (service intent) from the 'how' (orchestration details), which is crucial for multi-vendor, cloud-native environments.

Key Features

  • Automated instantiation, configuration, and termination of NFs, NS, and Slices
  • Elastic scaling (horizontal and vertical) based on performance metrics
  • Software update and upgrade management with rollback capabilities
  • Integrated fault recovery and healing procedures
  • End-to-end management across multiple administrative and technological domains
  • Standardized interfaces and data models for interoperability

Evolution Across Releases

Rel-14 Initial

Initial framework for the management and orchestration of network slices and virtualized network functions. Defined key concepts, requirements, and high-level architecture for LCM, aligning with ETSI NFV MANO principles. Introduced the NSMF and NSSMF functions.

First phase of 5G system specifications. Enhanced LCM procedures for 5G Network Slices and Network Functions, detailing service provisioning, activation, and deactivation. Defined more concrete interfaces and data models for slice LCM.

Enhanced support for network automation and closed-loop operations. Introduced more advanced LCM scenarios, including concurrent slice LCM operations, enhanced scalability, and improved integration with policy-driven management.

Further refinements for edge computing and non-public network deployments. Specified LCM aspects for network slices extending to the edge and for simpler enterprise-managed networks.

Continued evolution towards AI-driven automation and enhanced slice performance management. Worked on standardizing AI/ML models for predictive scaling and optimization as part of the LCM loop.

Defining Specifications

SpecificationTitle
TS 23.700 3GPP TS 23.700
TS 23.758 3GPP TS 23.758
TS 28.500 3GPP TS 28.500
TS 28.533 3GPP TS 28.533
TS 28.834 3GPP TS 28.834
TS 28.869 3GPP TS 28.869
TS 28.890 3GPP TS 28.890
TS 33.876 3GPP TR 33.876
TS 38.843 3GPP TR 38.843