Glossary

Terminology

To help guide the reader, this glossary provides an introduction to the terminology used within this document. These definitions are, with a few exceptions, based on the ETSI GR NFV 003 V1.5.1 [1] definitions. In a few cases, they have been modified to avoid deployment technology dependencies only when it seems necessary to avoid confusion.

Software Layer Terminology

• Cloud Infrastructure: A generic term covering NFVI, IaaS and CaaS capabilities - essentially the infrastructure on which a Workload can be executed.

Note: NFVI, IaaS and CaaS layers can be built on top of each other. In case of CaaS some cloud infrastructure features (e.g.: HW management or multitenancy) are implemented by using an underlying IaaS layer.

• Cloud Infrastructure Profile: The combination of the Cloud Infrastructure Software Profile and the Cloud Infrastructure Hardware Profile that defines the capabilities and configuration of the Cloud Infrastructure resources available for the workloads.

• Cloud Infrastructure Software Configuration: a set of settings (Key:Value) that are applied/mapped to cloud infrastructure SW deployment.

• Cloud Infrastructure Software Profile: defines the behaviour, capabilities and metrics provided by a Cloud Infrastructure Software Layer on resources available for the workloads.

• Cloud Native Network Function (CNF): A cloud native network function (CNF) is a cloud native application that implements network functionality. A CNF consists of one or more microservices. All layers of a CNF is developed using Cloud Native Principles including immutable infrastructure, declarative APIs, and a “repeatable deployment process”.

  Note: This definition is derived from the Cloud Native Thinking for Telecommunications Whitepaper (https://github.com/cncf/telecom-user-group/blob/master/whitepaper/cloud_native_thinking_for_telecommunications.md#1.4), which also includes further detail and examples.

• Compute flavour: defines the sizing of the virtualised resources (compute, memory, and storage) required to run a workload.

  Note: used to define the configuration/capacity limit of a virtualised container.

• Hypervisor: a software that abstracts and isolates workloads with their own operating systems from the underlying physical resources. Also known as a virtual machine monitor (VMM).

• Instance: is a virtual compute resource, in a known state such as running or suspended, that can be used like a physical server. Used interchangeably with Compute Node and Server.

  Note: Can be used to specify VM Instance or Container Instance.

• Network Function (NF): functional block or application that has well-defined external interfaces and well-defined functional behaviour.

• Within NFV, a Network Function is implemented in a form of Virtualised NF (VNF) or a Cloud Native NF (CNF).

• Network Function Virtualisation (NFV): The concept of separating network functions from the hardware they run on by using a virtual hardware abstraction layer.

• Network Function Virtualisation Infrastructure (NFVI): The totality of all hardware and software components used to build the environment in which a set of virtual applications (VAs) are deployed; also referred to as cloud infrastructure.

  Note: The NFVI can span across many locations, e.g. places where data centres or edge nodes are operated. The network providing connectivity between these locations is regarded to be part of the cloud infrastructure. NFVI and VNF are the top-level conceptual entities in the scope of Network Function Virtualisation. All other components are sub-entities of these two main entities.

• Network Service (NS): composition of Network Function(s) and/or Network Service(s), defined by its functional and behavioural specification, including the service lifecycle.

• Software Defined Storage (SDS): An architecture which consists of the storage software that is independent from the underlying storage hardware. The storage access software provides data request interfaces (APIs) and the SDS controller software provides storage access services and networking.

• Virtual Application (VA): A general term for software which can be loaded into a Virtual Machine.

  Note: a VNF is one type of VA.

• Virtual CPU (vCPU): Represents a portion of the host’s computing resources allocated to a virtualised resource, for example, to a virtual machine or a container. One or more vCPUs can be assigned to a virtualised resource.

• Virtual Machine (VM): virtualised computation environment that behaves like a physical computer/server.

  Note: A VM consists of all of the components (processor (CPU), memory, storage, interfaces/ports, etc.) of a physical computer/server. It is created using sizing information or Compute Flavour.

• Virtual Network Function (VNF): a software implementation of a Network Function, capable of running on the Cloud Infrastructure.

  • VNFs are built from one or more VNF Components (VNFC) and, in most cases, the VNFC is hosted on a single VM or Container.

• Virtual resources:

  • Virtual Compute resource (a.k.a. virtualisation container): partition of a compute node that provides an isolated virtualised computation environment.

  • Virtual Storage resource: virtualised non-volatile storage allocated to a virtualised computation environment hosting a VNFC.

  • Virtual Networking resource: routes information among the network interfaces of a virtual compute resource and physical network interfaces, providing the necessary connectivity.

• Workload: an application (for example VNF, or CNF) that performs certain task(s) for the users. In the Cloud Infrastructure, these applications run on top of compute resources such as VMs or Containers. Most relevant workload categories in the context of the Cloud Infrastructure are:

  • Data Plane Workloads: that perform tasks related to packet handling of the end-to-end communication between applications. These tasks are expected to be very I/O and memory read/write operations intensive.

  • Control Plane Workloads: that perform tasks related to any other communication between NFs that is not directly related to the end-to-end data communication between applications. For example, this category includes session management, routing or authentication.

  • Storage Workloads: that perform tasks related to disk storage (either SSD or HDD or other). Examples range from non-intensive router logging to more intensive database read/write operations.

Hardware Layer Terminology

• Cloud Infrastructure Hardware Configuration: a set of settings (Key:Value) that are applied/mapped to Cloud Infrastructure HW deployment.

• Cloud Infrastructure Hardware Profile: defines the behaviour, capabilities, configuration, and metrics provided by a cloud infrastructure hardware layer resources available for the workloads.

  • Host Profile: is another term for a Cloud Infrastructure Hardware Profile.

• CPU Type: A classification of CPUs by features needed for the execution of computer programs; for example, instruction sets, cache size, number of cores.

• Hardware resources: Compute/Storage/Network hardware resources on which the cloud infrastructure platform software, virtual machines and containers run on.

• Physical Network Function (PNF): Implementation of a network function via tightly coupled dedicated hardware and software system.

  Note: This is a physical cloud infrastructure resource with the NF software.

• Simultaneous Multithreading: Simultaneous multithreading (SMT) is a technique for improving the overall efficiency of superscalar CPUs with hardware multithreading. SMT permits multiple independent threads of execution on a single core to better utilise the resources provided by modern processor architectures.

Operational and Administrative Terminology

• Cloud service user: Natural person, or entity acting on their behalf, associated with a cloud service customer that uses cloud services.

  Note Examples of such entities include devices and applications.

• Compute Node: An abstract definition of a server. Used interchangeably with Instance and Server.

  Note: A compute node can refer to a set of hardware and software that support the VMs or Containers running on it.

• External Network: External networks provide network connectivity for a cloud infrastructure tenant to resources outside of the tenant space.

• Fluentd (https://www.fluentd.org/): An open source data collector for unified logging layer, which allows data collection and consumption for better use and understanding of data. Fluentd is a CNCF graduated project.

• Kibana: An open source data visualisation system.

• Multi-tenancy: feature where physical, virtual or service resources are allocated in such a way that multiple tenants and their computations and data are isolated from and inaccessible by each other.

• Prometheus: An open-source monitoring and alerting system.

• Quota: An imposed upper limit on specific types of resources, usually used to prevent excessive resource consumption by a given consumer (tenant, VM, container).

• Resource pool: A logical grouping of cloud infrastructure hardware and software resources. A resource pool can be based on a certain resource type (for example, compute, storage and network) or a combination of resource types. A Cloud Infrastructure resource can be part of none, one or more resource pools.

• Service Assurance (SA): collects alarm and monitoring data. Applications within SA or interfacing with SA can then use this data for fault correlation, root cause analysis, service impact analysis, SLA management, security, monitoring and analytic, etc.

• Tenant: cloud service users sharing access to a set of physical and virtual resources ITU (Y.3500: Information technology - Cloud computing - Overview and vocabulary).

  Note Tenants represent an independently manageable logical pool of compute, storage and network resources abstracted from physical hardware.

• Tenant Instance: refers to a single Tenant.

• Tenant (Internal) Networks: Virtual networks that are internal to Tenant Instances.

Container Related Terminology

Note: Relevant terms are added here from RA2. Most of these term definitions are taken from Kubernetes glossary (https://kubernetes.io/docs/reference/glossary) but in some cases should be made independent from Kubernetes as a specific container orchestration engine.

• CaaS Manager: A management plane function that manages the lifecycle (instantiation, scaling, healing, etc.) of one or more CaaS instances, including communication with VIM for master/node lifecycle management.

• Container: A lightweight and portable executable image that contains software and all of its dependencies.

  Note: OCI defines Container as “An environment for executing processes with configurable isolation and resource limitations. For example, namespaces, resource limits, and mounts are all part of the container environment.” A Container provides operating-system-level virtualisation by abstracting the “user space”. One big difference between Containers and VMs is that unlike VMs, where each VM is self-contained with all the operating systems components are within the VM package, containers “share” the host system’s kernel with other containers.

• Container Engine: Software components used to create, destroy, and manage containers on top of an operating system.

• Container Image: Stored instance of a container that holds a set of software needed to run an application.

• Container Runtime: The software that is responsible for running containers.

  Note: as explained in OCI Glossary (https://github.com/opencontainers/runtime-spec/blob/master/glossary.md) it reads the configuration files for a Container from a directory structure, uses that information to create a container, launches a process inside the container, and performs other lifecycle actions.

• Container-as-a-Service (CaaS): A complete set of technologies to enable the management of containerised software, including a Kubernetes cluster, container networking, storage, routing, service mesh, etc.

• Kubernetes Cluster: A set of machines, called nodes and master, that run containerised applications managed by Kubernetes. A cluster has at least one worker node and at least one master.

  Note: adapted from Kubernetes Glossary(https://kubernetes.io/docs/reference/glossary/?all=true#term-cluster).

• Kubernetes Control Plane: The container orchestration layer that exposes the API and interfaces to define, deploy, and manage the lifecycle of containers.

• Kubernetes Master: Master(s) manage the worker nodes and the Pods in the cluster. The master may run on a VM or a physical machine. Multiple masters can be used to provide a cluster with failover and high availability.

• Kubernetes Node: A node is a worker machine in Kubernetes. A worker node may be a VM or physical machine, depending on the cluster. It has local daemons or services necessary to run Pods and is managed by the control plane.

• Kubernetes Service: An abstract way to expose an application running on a set of Pods as a network service.

  Note: This definition from Kubernetes Glossary(https://kubernetes.io/docs/reference/glossary/?all=true#term-service) uses the term “network service” differently than in ETSI NFV.

• Pod: The smallest and simplest Kubernetes object. A Pod represents a set of running containers on your cluster. A Pod is typically set up to run a single primary container. It can also run optional sidecar containers that add supplementary features like logging.

OpenStack Related Terminology

Note: The official OpenStack Glossary( https://docs.openstack.org/image-guide/common/glossary.html) is an extensive list of OpenStack-related concepts. Some additional terms used in the Reference Architecture RA-1 or used to relate RA-1 terms with terms defined elsewhere.

• Core (physical): An independent computer processing unit that can independently execute CPU instructions and is integrated with other cores on a multiprocessor (chip, integrated circuit die). Please note that the multiprocessor chip is also referred to as a CPU that is placed in a socket of a computer motherboard.

• Flavor Capability: The capability of the Cloud Infrastructure Profile, such as CPU Pinning, NUMA or huge pages.

• Flavor Geometry: Flavor sizing such as number of vCPUs, RAM, disk, etc.

• Huge pages: Physical memory is partitioned and accessed using the basic page unit (in Linux default size of 4 KB). Huge pages, typically 2 MB and 1GB size, allows large amounts of memory to be utilised with reduced overhead. In an NFV environment, huge pages are critical to support large memory pool allocation for data packet buffers. This results in fewer Translation Lookaside Buffers (TLB) lookups, which reduces the virtual to physical pages address translations. Without huge pages enabled high TLB miss rates would occur thereby degrading performance.

• Server: For the OpenStack Compute API, a server is a virtual machine (VM), a physical machine (bare metal) or a container.

Cloud Platform Abstraction Related Terminology:

• Abstraction: Process of removing concrete, fine-grained or lower level details or attributes or common properties in the study of systems to focus attention on topics of greater importance or general concepts. It can be the result of decoupling. Adapted from Wikipedia:Abstraction (https://en.wikipedia.org/wiki/Abstraction_(computer_science)), Wikipedia:Generalization(https://en.wikipedia.org/wiki/Generalization)

• Appliance deployment model: Application has tight coupling with underlying Platform even if the application is virtualized or containerized.

• Application Control: Any method or system of controlling applications (VNFs). Depending on RA and technologies used, this can be a VNF Manager or NFV Orchestrator provided as a VNF or Platform capability.

• Cloud deployment model: Applications are decoupled from the platform provided by Cloud operator.

• Decomposition: Decomposition (also known as factoring) is breaking a complex system into parts that are easier to program and maintain. Adapted from Wikipedia:Decomposition (https://en.wikipedia.org/wiki/Decomposition_(computer_science)

• Decoupling, Loose Coupling: Loosely coupled system is one in which each of its components has, or makes use of, little or no knowledge of the implementation details of other separate components. Loose coupling is the opposite of tight coupling. Adapted from Wikipedia:Loose Coupling (https://en.wikipedia.org/wiki/Loose_coupling.

• Encapsulation: Restricting of direct access to some of an object’s components. Adapted from Wikipedia:Encapsulation (https://en.wikipedia.org/wiki/Encapsulation_(computer_programming)

• Observability: Observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. Adapted from Wikipedia:Observability (https://en.wikipedia.org/wiki/Observability

• Resilience: Resilience is the ability to provide and maintain an acceptable level of service in the face of various faults and challenges to normal operation. Adapted from Wikipedia:Resilience (https://en.wikipedia.org/wiki/Resilience_(network)

Test Related Terminology

• Calibration: The process of checking and/or adjusting a stimulus generation or measurement device with a known reference value, to improve the overall quality of the measured results. Calibration may be very simple, such as a comparison of the configured traffic generator sending rate and measured rate using a simple SUT (System Under Test) such as loop-back cable between interfaces, such that the known reference value is the published nominal interface rate.

• Reference Value: A measured or established result or outcome for comparison with new measurements. For example, the reference value or expected outcome of a Functional Test is “PASS”. The reference value or expected outcome of a Performance Measurement or Benchmarking test may be the value measured for the previous SUT release, or the published value or theoretical limit of a simple SUT.

• API Testing: Testing against a protocol specification for conformance.

• Functional Testing: The main objective of functional testing is the verification of compliance against specific functional requirements using a specific stimulus / response within the SUT, including the expected behaviour. These tests generally result in a binary outcome, i.e. pass / fail. For example, verification of an “API call” and its associated response, such as the instantiation of a VM (or container) and verification of the VM’s (or container’s) existence (expected behaviour), or the ability to activate a specific feature of the SUT (e.g. SR-IOV).

• Performance Measurement: The procedure or set of operations having the objective of determining a Measured Value or Measurement Result of an infrastructure in operation according to a defined metric. In the context of telemetry, Performance Measurements reflect data generated and collected within the cloud infrastructure that reflects a performance aspect of the cloud infrastructure. For example, a count of frames or packets traversing an interface per unit of time, memory usage information, other resource usage and availability, etc. This data may be instantaneous or accumulated, and made available (i.e. exposed) based on permissions and contexts (e.g. workload vs. infra). Other Performance Measurements are designed to assess the efficiency of SUT Functions, such as the time to successfully instantiate one or more VMs or containers, or the percentage of failures in a set of many instantiation attempts. Still other Performance Measurements are conducted under controlled conditions using Calibrated test systems, such that the measured results are more likely to comparable with other such measurements.

• Performance Testing: The main objective of performance testing is to understand if the SUT is able to achieve the expected performance, through conducting a series of performance measurements and comparing those results against a reference value. Performance testing is needed to help dimension a solution or to assess that a platform (particular hardware + software combination) is configured correctly and performing as expected i.e. as compared with capacity / performance claims made by the infrastructure and VNF/CNF vendors. Performance Testing may be useful to compare infrastructure capabilities between a particular SUT and a reference implementation with well understood known good configurations and previously established performance ranges. Performance testing for the purpose of comparing between different commercial implementations is not a goal here and hence out of scope for the purposes of this definition. Performance testing relies on well established benchmark specifications to help establish appropriate methodologies and accuracy tolerances.

• Benchmarking: Benchmarking is a type of performance test that assesses a key aspect of the computing environment in its role as the infrastructure for network functions, using calibrated test systems and controlled conditions. In general the benchmark testing attempts to isolate the feature or parameter under test, to reduce the impact of other system components or operations on the test result. The benchmark (and related metrics) have been agreed by the Industry and documented in publications of an accredited standards body. As a result, benchmarks are a sub-set of all possible performance tests and metrics i.e. they are selected measurements which are more important than others. Example benchmarks include Zero-loss Throughput, Latency, and Loss ratio (ref to ETSI NFV TST009, RFC 2544) of various components of the environment, expressed in quantitative units to allow direct comparison between different systems treated as a black box (vendor-independence). Because the demands on a particular system may vary from deployment to deployment, benchmarking assessments do not define acceptance criteria or numerical performance requirements. Benchmark testing and conformance testing intersect when a specific requirement in the reference architecture specification is very important to the performance of the system. Correct execution of the performance test with the valid result constitutes conformance. The completion time for a single conforming execution, or the number of conforming executions per second are potential benchmark metrics, and sources of known reference values.

Other Referenced Terminology

• Anuket Assured Program (AAP): An open source, community-led program to verify compliance of the telecom applications and the cloud infrastructures with the Anuket specifications.

• Carrier Grade: Carrier grade refers to network functions and infrastructure that are characterised by all or some of the following attributes: High reliability allowing near 100% uptime, typically measured as better than “five nines”; Quality of Service (QoS) allowing prioritization of traffic; High Performance optimized for low latency/packet loss, and high bandwidth; Scalability to handle demand growth by adding virtual and/or physical resources; Security to be able to withstand natural and man-made attacks.

• Monitoring (Capability): Monitoring capabilities are used for the passive observation of workload-specific traffic traversing the Cloud Infrastructure. Note, as with all capabilities, Monitoring may be unavailable or intentionally disabled for security reasons in a given cloud infrastructure instance.

• NFV Orchestrator (NFVO): Manages the VNF lifecycle and Cloud Infrastructure resources (supported by the VIM) to ensure an optimised allocation of the necessary resources and connectivity.

• Platform: A cloud capabilities type in which the cloud service user can deploy, manage and run customer-created or customer-acquired applications using one or more programming languages and one or more execution environments supported by the cloud service provider. Adapted from ITU (Y.3500: Information technology - Cloud computing - Overview and vocabulary).

  Note: This includes the physical infrastructure, Operating Systems, virtualisation/containerisation software and other orchestration, security, monitoring/logging and life-cycle management software.

• Vendor Implementation: A commercial implementation of a cloud platform.

• Virtualised Infrastructure Manager (VIM): Responsible for controlling and managing the Network Function Virtualisation Infrastructure compute, storage and network resources.