VR

Virtualized Resource

Management →
Introduced in Rel-13 Also in: Management

VR is an abstract representation of compute, storage, or networking hardware capabilities that are the fundamental building blocks for deploying network functions as software on cloud infrastructure.

Category
Management
Introduced
Rel-13
Where
Services › Codecs
Also touches
1 segments
Specifications
35 specs
VR Description Purpose Related Classification Specifications

Description

A Virtualized Resource (VR) is a key concept within the 3GPP management framework, particularly for the management of virtualized network functions (VNFs) in a cloud-native environment. It represents a logical abstraction of a physical resource, such as a CPU core, a block of memory, a storage volume, or a virtual network interface card (vNIC). These abstractions are created and managed by a virtualization layer (e.g., a hypervisor or container runtime) on top of physical hardware. The 3GPP management system, defined in specifications like 28.520 (Management and Orchestration, MANO), interacts with VRs to allocate, monitor, and orchestrate them for the purpose of instantiating and scaling network functions.

The architecture involves several key entities. The Virtualized Infrastructure Manager (VIM) is responsible for controlling and managing the NFVI (Network Functions Virtualization Infrastructure) compute, storage, and network resources. The VIM exposes these resources as VRs. The NFV Orchestrator (NFVO) and VNF Manager (VNFM) then use these VRs, described in a VNF Descriptor (VNFD), to deploy VNFs. A VNFD defines the VNF's requirements in terms of Virtualized Compute, Storage, and Network Resources (Vnfc, VnfStorage, VnfVirtualLink). During instantiation, the MANO system maps these requirements to available VRs on the infrastructure, creating the necessary virtual machines (VMs) or containers with the specified CPU, memory, and network connectivity.

VRs are dynamic and elastic. Their lifecycle (creation, modification, termination) is managed through standardized interfaces, such as the Or-Vi reference point between the NFVO and VIM. Monitoring of VR performance metrics (e.g., CPU utilization, memory usage, I/O rates) is also standardized, allowing the MANO system to perform automated scaling actions. For example, if a VNF experiences high load, the VNFM can request additional virtualized compute resources (more VRs) from the VIM via the NFVO, leading to the scaling out of the VNF instance. This abstraction is crucial for achieving the goals of NFV: hardware independence, efficient multi-tenancy, and agile service deployment.

Purpose & Motivation

The concept of the Virtualized Resource was introduced to address the limitations of traditional telecom networks built on proprietary, physical appliances. These appliances were tightly coupled to specific hardware, leading to long procurement and deployment cycles, inefficient resource utilization (often over-provisioned for peak capacity), and high operational costs. The shift towards Network Functions Virtualization (NFV), championed by industry forums like ETSI ISG NFV and adopted by 3GPP, required a standardized way to model and manage the software-based resources that would replace physical network functions.

The creation of the VR abstraction in 3GPP specifications, notably from Rel-13 onwards, provided this standardized model. It solved the problem of heterogeneity in cloud infrastructure by defining a common set of resource types (compute, storage, network) and their management interfaces, regardless of the underlying hypervisor (VMware, KVM, etc.) or hardware vendor. This allows network operators to deploy VNFs from different vendors on a common, shared pool of physical resources, enabling true multi-vendor interoperability and preventing vendor lock-in at the infrastructure layer. The VR model is the foundation for automation, elastic scaling, and the efficient "cloudification" of mobile networks, which are essential for supporting diverse 5G and beyond services with varying demands.

Classification

Part ofNFV
Specific typesVNFNFVISH
Related approachesMANO

Release Timeline

Evolution Across Releases

Rel-13 Initial

Initial introduction of Virtualized Resource concepts and management requirements as part of the early 5G and NFV study phase. Defined basic models for virtualized compute and storage resources and their integration into the network management architecture.

Enhanced VR management procedures and interfaces. Formalized the role of the VIM and defined more detailed resource models, including affinity/anti-affinity rules for placing VRs and initial support for network resource abstraction.

Full integration of VR management into the 5G system architecture. Defined the management services for 5G network functions, ensuring VR models support network slicing, where physical resources are partitioned into multiple logical, isolated VR sets for different slices.

Enhancements for automation and closed-loop operations. Introduced more granular VR monitoring and analytics to support AI/ML-driven orchestration. Improved models for managing VRs in edge computing deployments.

Expansion to support non-public networks (NPN) and industrial IoT. Defined VR management for private 5G networks and enhanced support for specialized hardware accelerators (e.g., for UPF) modeled as specific types of VRs.

Further evolution towards cloud-native principles, with enhanced VR models for containerized network functions (CNFs). Improved lifecycle management for microservices-based architectures and support for function-as-a-service (FaaS) concepts.

Ongoing work to refine VR management for AI-native networks and further integration with open-source cloud platforms. Focus on sustainability, optimizing VR allocation for energy efficiency, and managing VRs across heterogeneous compute domains (cloud, edge, far-edge).

Explore further

Broader topics and technologies where VR plays a role.

Defining Specifications

3GPP specifications that define or reference VR, with the latest known release. Sourced from the 3GPP document catalog — see methodology.

SpecificationTitleRelease
TR 21.905 vj00 3GPP Technical Terms and Definitions Rel-19
TS 22.261 vk30 5G System Service Requirements Rel-20
TR 22.804 vg30 5G Automation in Vertical Domains Study Rel-16
TR 22.873 vi00 Technical Report on IMS Multimedia Telephony Service Enhancements Rel-18
TS 26.114 vj10 IMS Multimedia Telephony Media Handling Rel-19
TS 26.118 vj00 Virtual Reality Media Formats Rel-19
TS 26.119 vj00 XR Media Capabilities for AR Devices Rel-19
TS 26.234 vj00 3GPP PSS Protocols and Codecs Specification Rel-19
TS 26.346 vj20 MBMS User Services Media Codecs & Protocols Rel-19
TS 26.511 vj00 5G Media Streaming Profiles, Codecs & Formats Rel-19
TR 26.812 vi10 Technical Report Rel-18
TS 26.818 vf00 Audio Media Profiles Test Results for VR Streaming Rel-15
TS 26.841 vj00 Study on Media Messaging Enhancements Rel-19
TR 26.857 vi00 Technical Report on Media Service Enablers Rel-18
TR 26.862 vh00 Immersive Teleconferencing & Telepresence for Remote Terminals Rel-17
TS 26.891 vg00 Media Distribution Services in 5G System Rel-16
TR 26.918 vj00 Virtual Reality Relevance Study for 3GPP Rel-19
TR 26.925 vj00 Media Traffic Characteristics for 3GPP Networks Rel-19
TR 26.928 vj00 Study on eXtended Reality (XR) in 5G Rel-19
TR 26.929 vj00 QoE Metrics for VR Services Study Rel-19
TR 26.933 vj00 Study on Diverse Audio Capturing System Rel-19
TR 26.956 vj01 Beyond 2D Video Formats & Codecs Study Rel-19
TR 26.999 vj00 VR Streaming Interoperability Test Material Rel-19
TS 28.404 vj00 QoE Measurement Collection: Concepts & Requirements Rel-19
TS 28.405 vj40 QoE Measurement Control & Configuration Rel-19
TS 28.406 vj00 QoE measurement collection: info definition & transport Rel-19
TS 28.520 vj00 PM for Virtualized Mobile Networks Rel-19
TS 32.401 vj00 Performance Management Concept & Requirements Rel-19
TS 32.426 vj00 EPC Performance Measurements Specification Rel-19
TS 32.842 vd10 Management of Virtualized 3GPP Core Networks Rel-13
TS 38.300 vj00 NG-RAN Overall Description Rel-19
TS 38.331 vj00 NR Radio Resource Control (RRC) Protocol Specification Rel-19
TR 38.835 vi01 Technical Report on XR Enhancements for NR Rel-18
TR 38.838 vh00 Study on XR Evaluations for NR Rel-17
TR 38.890 vh00 NR QoE Management and Optimization Rel-17
Patrick Zandl

About the author: Patrick Zandl (b. 1974)

Telecommunications specialist, technology journalist (founder of the Mobil server), and developer who has been running since 2025 — the largest Czech-language resource on AI-assisted programming. Formerly Chief Wizard Architect at Prusa3D and head of development for Turris at CZ.NIC; currently a consultant and instructor on AI implementation in companies.