Terminology and Glossary
During our discussions with NRENs and at workshops it became clear that there are OAV terms that are being used in different ways and in some cases with slightly different meaning and understanding. So in order to have a common basis we decided to identify a list of relevant OAV terms and add a short definition with a reference link (source) for each term as well as an acronym table with definitions of abbreviations. We tried to use standard-based definitions whenever we could find them and listed internal definitions in cases where no standard definitions were found.
Internal definitions are based on the consensus of all team members; to come to an agreed definition of all team members a terminology document was created with descriptions of the terms and an internal survey was conducted for final adjustments. Additional comments are welcome!
OAV Common Terms
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Glossary
| OAV Terms | Definition and reference |
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AIOps | AIOps is (the usage of) Artificial Intelligence for IT Operations. It combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination. |
Adaptive Machine Learning
| Adaptive machine learning builds on traditional machine learning to create a more advanced solution to real-time environments with variable data. As its name suggests, adaptive machine learning can adapt to rapidly changing data sets, making it more applicable to real-world situations.
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Adversarial AI/ML | A practice concerned with the design of ML algorithms that can resist security challenges, the study of the capabilities of attackers, and the understanding of attack consequences.
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AI Accuracy | Closeness of computations or estimates to the exact or true values that the statistics were intended to measure.
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AI Agent | An artificial intelligence (AI) agent is a software program that can interact with its environment, collect data, and use the data to perform self-determined tasks to meet predetermined goals. Unlike traditional automation agents, which follow static, predefined rules, AI agents can learn from their environment, adapt their behaviour, and make autonomous decisions based on real-time data, making them more flexible and capable of handling dynamic situations.
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AI as a Service | Artificial Intelligence as a Service (AIaaS) is a cloud-based service offering artificial intelligence (AI) outsourcing. AIaaS enables individuals and businesses to experiment with AI, and even take AI to production for large-scale use cases.
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AI Deployment Flexibility | Flexibility to deploy the same system in multiple scenarios without any modifications to the AI models. It goes hand in hand with generalisability.
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AI Policy Enforcer | AI functionality to implement a recommended policy.
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| AI-powered Virtual Agent (AIVA) | An AI-powered Virtual Agent is an animated virtual character, more complex than a chatbot, that makes use of technologies like machine learning and natural language processing (NLP). This allows it to actively participate in a conversation, acting more like a human.
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| Analytics Logical Function | A logical function in NWDAF, which performs inference, derives analytics information (i.e. derived statistics and/or predictions based on Analytics Consumer Request) and exposes analytics service.
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API (Application Programming Interface) | An API is a set of commands, functions, protocols, and objects that programmers can use to create software or interact with an external system. Any data can be shared with an application program interface. |
Architecture component | An architecture component is a nontrivial, nearly independent, and replaceable part of a system that fulfills a clear function in the context of a well-defined architecture.
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Architecture principles | Architecture principles define the underlying general rules and guidelines for the use and deployment of all IT resources and assets across the organisation. They reflect a level of consensus among the various elements of the enterprise, and form the basis for making future IT decisions. |
| Artificial General Intelligence | Human-like intelligence, which can be applied widely as opposed to narrow AI, which can only be applied to one particular problem or task. Also called 'strong' AI as opposed to 'weak' AI.
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| Artificial Intelligence | Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. It is the system’s ability to correctly interpret external data, to learn from such data, and to use that learning to achieve specific goals and tasks through flexible adaptation.
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Automated root cause analysis | Automated RCA is the process of using automation to investigate incident root causes in real time using AI/ML.
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Automated service provisioning | Automated service provisioning is the ability to deploy an information technology or telecommunications service by using pre-defined procedures that are carried out electronically without requiring human intervention.
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Automation | Processing tasks in a repeatable manner to yield the same result every time without human intervention.
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Autonomy (autonomous AI system) | AI-enabled Autonomy is the capability of machines (either platforms or computer software) to operate independent of direct human intervention, but within constraints, to achieve a goal or solve a problem.
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Auto-scaling support | Autoscale allows you to automatically scale your applications or resources based on demand.
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Bias | A systematic error that occurs in the machine learning model itself due to incorrect assumptions in the ML process. Technically, bias is the error between average model prediction and the ground truth. Unwanted bias may place privileged groups at systematic advantage and unprivileged groups at systematic disadvantage.
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Bidirectional Encoder Representations | Bidirectional Encoder Representations from Transformers (BERT) is a deep learning strategy for natural language processing (NLP) that helps artificial intelligence (AI) programs understand the context of ambiguous words in text. |
Big data | Big data reflects extremely large or complex datasets that may be analysed computationally, rather than by traditional data-processing application software, to reveal patterns, trends and associations, especially relating to human behaviours and interactions. |
| Big data-driven networking | A type of future network framework that collects big data from networks and applications, and generates big data intelligence based on that data; it then provides big data intelligence to facilitate smarter and autonomous network management, operation, control, optimisation and security, etc.
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Blockchain | A blockchain is an expanding list of cryptographically signed, irrevocable transactional records shared by all participants in a network.
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Cgroups (control groups) | Cgroups are linux kernel mechanisms to restrict and measure resource allocations to each process group. Using cgroups, you can allocate resources such as CPU time, network, and memory. |
| Chatbot/Bot | A computer program that simulates and processes human conversation (either written or spoken), allowing humans to interact with digital devices, systems and platforms as if they were communicating with a real person. |
| ChatGPT | A software that allows a user to ask it questions using conversational, or natural, language. It is a language model developed by OpenAI, and is based on the GPT (Generative Pre-training Transformer) architecture, which is a type of neural network designed for natural language processing tasks.
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| Classification, classifier | A classifier is the algorithm itself—the rules used by machines to classify data. A classification model, on the other hand, is the end result of your classifier's machine learning. The model is trained using the classifier, so that the model, ultimately, classifies your data.
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Closed-loop processes | An automatic control system in which an operation, process, or mechanism is regulated by feedback.
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Cloud native application | Cloud Native Application (CNA) refers to a type of computer software that natively utilises services and infrastructure provided by cloud computing providers.
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Component | A component is a functionally independent part of any system. It performs some function and may require some input or produce some output. |
Composite service | A composite service is an assembly of one or more elements into an end to end service. It may be recursive so a composite service may become a component of yet another service.
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Container | A container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another.
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Control plane | The control plane is responsible for processing a number of different control protocols that may affect the forwarding table, depending on the configuration and type of network device. These control protocols are jointly responsible for managing the active topology of the network.
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Conversational agents/conversational AI (chatbots) | A conversational agent is any dialogue system that conducts natural language processing (NLP) and responds automatically using human language. Conversational agents represent the practical implementation of computational linguistics, and are usually deployed as chatbots and virtual or AI assistants.
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Conversational AI | Conversational AI (or conversational artificial intelligence), refers to technologies that enable machines to understand, process, and respond to human language naturally. These include chatbots and virtual assistants which can perform tasks or provide information based on voice or text inputs.
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Convolutional neural network (CNN) | A convolutional neural network (CNN) is a type of artificial neural network used primarily for image recognition and processing. Due to its ability to recognize patterns in images, a CNN is a powerful tool but requires millions of labelled data points for training.
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Cortex XSOAR | A platform for security orchestration, automation, and response (SOAR), enhanced with ChatGPT for user-friendly incident analysis and response.
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Cortex XSOAR Playbook | A set of automated workflows in Cortex XSOAR, designed to handle security incidents efficiently.
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Cross-domain data services | Data services that are delivered across multiple administrative, information or technological domains that allow data sharing among authorized consumers in different domains.
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Customer Facing Services (CFS) | A logical capability that is packaged as part of a product offering by service providers to their customers, which is directly purchased, leased, visible to and/or otherwise directly usable by those customers. The logical functionality can be derived from underlying network or information technology (i.e., a dedicated contact number or tailored web-based access to operational support for a specific customer) or may be delivered or supplied by staff or contractors employed by the service provider (i.e., dedicated service team or help desk for a specific customer).
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Data center interconnect (DCI) | Data center interconnect (DCI) is a segment of the networking market that focuses on the technology used to link two or more data centers so the facilities can share resources. |
Data Governance | Data governance is the process of managing the availability, usability, integrity, and security of the data in enterprise systems, based on internal data standards and policies that also control data usage. It ensures that data is consistent, trustworthy, and doesn't get misused, facilitating effective decision-making. It also means setting internal standards – data policies – that apply to how data is gathered, stored, processed, and disposed of. It governs who can access what kinds of data and what kinds of data are under governance.
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| Data ingestion | Data ingestion is the process of transporting data from one or more sources to a target site, system or platform for further processing and analysis. This data can originate from a range of sources, including data lakes, IoT devices, on-premises databases, and SaaS apps, and end up in different target environments, such as cloud data warehouses or data marts. |
| Data lake | A storage repository that holds a vast amount of raw data in its native format, primarily in files or objects storage without hierarchical dimensions, until it is needed for analytics applications. |
| Data model | A data model (or datamodel) is an abstract model that organises elements of data and standardises how they relate to one another. |
Data plane | The data plane (sometimes known as the user plane, forwarding plane, carrier plane or bearer plane) is the part of a network that carries user traffic from one interface to another. |
Data Poisoning | Data Poisoning is an adversarial attack that tries to manipulate the training dataset in order to control the prediction behaviour of a trained model such that the model will label malicious examples into desired classes (e.g., labelling spam e-mails as safe).
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Data Quality | Data quality measures how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose, and it is critical to all data governance initiatives within an organization.
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| Decision management engine | A decision management engine is a customisable solution that represents the logic, often in the form of a rules flow or decision tree, that can be operationalised to automate a decision. […] A decision management engine articulates how smaller decisions branch off to bigger and more complex decisions and ultimately end with a final outcome. This logic can be codified, documented, and often executed in an automated fashion. |
Decoupling | Building approach (in electronics, software, etc.) where the constituent components of a system can be produced, sourced and interchanged independently of the other.
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| Deep learning | Deep learning is an iterative approach to artificial intelligence (AI) that stacks machine learning algorithms in a hierarchy of increasing complexity and abstraction. Each deep learning level is created with knowledge gained from the preceding layer of the hierarchy. |
Domain | A collection of network infrastructure under the administrative control of the same organisation.
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Dynamic Function Placement (DPS) | The act of dynamically placing network functions. This is done by deploying intelligent algorithms to optimally orchestrate differentiated services across multiple sites and clouds based on diverse intents and dynamic environments' policy constraints.
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Edge Computing | Edge computing refers to a distributed computing paradigm that brings computation and data storage closer to the location where it is needed to improve response times and save bandwidth. Instead of relying on a centralised cloud data centre, edge computing performs these processes at or near the physical location of the user or data source.
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Evasion attacks | Evasion attacks (a.k.a. adversarial examples) consist of carefully perturbing the input samples at test time to have them misclassified.
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Expert system | An expert system uses artificial intelligence (AI) technologies to simulate the judgement and behaviour of a human expert based on “knowledge” programmed into it by humans, and only following predetermined rules.
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| Extract, Transform, Load (ETL) | The data processing technique that engineers use to extract data from different sources, transform the data into a usable and trusted resource, and load that data into the systems end users can access and use downstream to solve business problems. |
Federated Learning | A learning model that addresses the problem of data governance and privacy by training algorithms collaboratively without transferring the data to another location.
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Federated orchestration | Service orchestration performed by multiple autonomous management domains, to effectively allow services to span across several providers.
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Foundation model | An AI model that is trained on broad data at scale, is designed for generality of output, and can be adapted to a wide range of distinctive tasks.
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Functional block | Self contained unit in an overall system that performs a specific function or task.
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Generative Adversarial Network (GAN) | An approach to training AI models useful for applications like data synthesis, augmentation, and compression where two neural networks are trained in tandem: one is designed to be a generative network (the forger) and the other a discriminative network (the forgery detector). The objective is for each network to train and better itself off the other, reducing the need for big, labeled training data.
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Generative AI | Foundation models used in AI systems specifically intended to generate, with varying levels of autonomy, content such as complex text, images, audio, or video.
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Generative Pre-trained Transformer | GPT, or Generative Pre-trained Transformer, is a state-of-the-art language model developed by OpenAI. It uses deep learning techniques to generate natural language text, such as articles, stories, or even conversations, that closely resemble human-written text.
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Hierarchical orchestration | Orchestration decomposed into one or more hierarchical interactions where parts of the service are delegated to a subordinate orchestrator.
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Holistic Anomaly Detection (e.g., via multi-vector AI/ML-based behavioural analytics) | Anomaly detection, or outlier detection, is the identification of observations, events or data points that deviate from what is usual, standard or expected, making them inconsistent with the rest of a data set. Holistic anomaly detection takes an overall approach to anomaly detection using a variety of methods. Holistic anomaly detection is a comprehensive approach to identifying unusual patterns or behaviors within data. Rather than relying on a single method, it combines multiple techniques—such as statistical analysis, machine learning models, and rule-based algorithms—to capture a wider range of anomalies. This approach is valuable because it considers the data from multiple perspectives, enhancing the ability to detect different types of anomalies, including subtle or complex ones that might be missed by a single-method approach.
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Horizontal Scaling | Horizontal scaling (or scaling out) means that you scale by adding more machines into your pool of resources.
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Human-centric AI | Human-Centered AI (HCAI) is an emerging discipline intent on creating AI systems that amplify and augment rather than displace human abilities. HCAI seeks to preserve human control in a way that ensures artificial intelligence meets our needs while also operating transparently, delivering equitable outcomes, and respecting privacy.
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Intent-based policy / network | Technology incorporating artificial intelligence (AI) and machine learning to automate administrative tasks across a network.
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| Intelligent network | An architectural concept for the support, maintenance, operation and provision of new services which is characterised by: information processing, efficient management, control and use of network resources and standardised communication between physical resources, network functions and services.
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Intent-based Networking | A software-enabled automation process that uses high levels of intelligence, analytics, and orchestration to improve network operations and uptime.
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Intent-based policy / network | Technology incorporating artificial intelligence (AI) and machine learning to automate administrative tasks across a network.
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| Internet of Things (IoT) | The Internet of Things, or IoT, is a system of interrelated networking computing devices, mechanical and digital machines aimed at objects, animals or people and provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. |
Kubernetes | Kubernetes is an open-source platform used to automate the deployment, scaling, and management of containerized applications. It orchestrates computing, networking, and storage infrastructure on behalf of user workloads, providing a resilient environment for running distributed systems. Kubernetes allows for self-healing, scaling, and service discovery, making it a vital tool for managing containerized applications at scale.
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Language Model | A machine-learning model designed to represent the language domain.
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Large Language Model | A class of language models that use deep-learning algorithms and are trained on extremely large textual datasets that can be multiple terabytes in size. LLMs can be classed into two types: generative or discriminatory. Generative LLMs are models that output text, such as the answer to a question or even writing an essay on a specific topic. They are typically unsupervised or semi-supervised learning models that predict what the response is for a given task. Discriminatory LLMs are supervised learning models that usually focus on classifying text, such as determining whether a text was made by a human or AI.
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| Machine learning (ML) | Processes that enable computational systems to “understand” data and gain “knowledge” from it without necessarily being explicitly programmed. (Supervised machine learning and unsupervised machine learning are two examples of machine learning.)
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Management | The processes aiming at fulfilment, assurance, and billing of services, network functions, and resources in both physical and virtual infrastructure including compute, storage, and network resources.
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Management API | A Management API allows a service requestor to perform all management operations before, during and after the use of a service.
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Management domain | A collection of physical or functional elements under the control of an entity, aiming at fulfilment, assurance, and billing of services, network functions, and resources in both physical and virtual infrastructure.
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Maturity level | A maturity level is a defined evolutionary plateau for organisational process improvement. Each maturity level matures an important subset of the organisation’s processes, preparing it to move to the next maturity level. The maturity levels are measured by the achievement of the specific and generic goals associated with each predefined set of process areas. |
| Maturity model | A maturity model is an instrument that evaluates the current position of certain capabilities of an organisation and provides indications of how it can transform to improve.
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Microservices | Microservices is an approach to software architecture that builds a large, complex application from multiple small components that each perform a single function, such as authentication, notification, or payment processing. Each microservice is a distinct unit within the software development project, with its own code base, infrastructure, and database. The microservices work together, communicating through web APIs or messaging queues to respond to incoming events. |
Modelling Abstractions | Model abstraction is a way of simplifying an underlying conceptual model on which a simulation is based while maintaining the validity of the simulation results with respect to the question being addressed by the simulation. |
Natural Language Generation | Natural language generation (NLG) is the use of artificial intelligence (AI) programming to produce written or spoken narratives from a data set. |
| Natural language processing (NLP) | Natural language processing (NLP) refers to the branch of AI concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. |
Network automation | Network automation is the process of automating the configuration, management, testing, deployment, and operations of physical and virtual devices within a network. |
Network controller | Functional block that centralizes some or all of the control and management functionality of a network domain and may provide an abstract view of its domain to other functional blocks via well-defined interfaces.
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Network function (NF) | Network Function (NF) – a functional building block within a network infrastructure, which has well-defined external interfaces and a well-defined functional behaviour.
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Network function disaggregation (NFD) | Defines the evolution of switching and routing appliances from proprietary, closed hardware and software sourced from a single vendor, towards totally decoupled, open components which are combined to form a complete switching and routing device. |
| Network intelligence level | A three-level application of automation capabilities (i.e., full automated infrastructure management, data centre infrastructure management and traceable/intelligent patch cords), including those enabled by integrating artificial intelligence techniques in the network.
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Network namespaces | Network namespaces is a virtualization mechanism (a virtualised networking stack) which provides abstraction and virtualisation of network protocol services and interfaces. Each network namespace has its own network device instances that can be configured with individual network addresses.
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Network orchestration | Network orchestration is the execution of the operational and functional processes involved in designing, creating, and delivering an end-to-end service. For example, it uses network automation to provide services through the use of applications that drive the network. An orchestrator functions to arrange and organise the various components involved in delivering a network service.
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Network resource | Physical or logical network component of hardware, software or data in the data, control or management planes within an organization's infrastructure.
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Network service | A collection of network functions with a well specified behavior (i.e. content delivery networks (CDNs) and IP multimedia subsystem (IMS)).
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Network Service Meshes | A network service mesh is intended to support application-to-application and function-to-function communications in networks and scenarios through dynamic and automated virtual network services – to be allocated on-demand, based on application requirements. Additionally, a service mesh is a software layer that handles all communication between services in applications. This layer is composed of containerized microservices. |
Network slice instance | A network slice instance is a set of network function instances and the required resources (e.g., compute, storage and networking resources) which form a deployed network slice.
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Network slicing | Network slicing is a specific form of virtualisation that allows multiple logical networks to run on top of a shared physical network infrastructure. (..) The intent of network slicing is to be able to partition the physical network at an end-to-end level to allow optimum grouping of traffic, isolation from other tenants, and configuring of resources at a macro level.
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Neural Network | Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another. Artificial neural networks (ANNs) consist of multiple layers: an input layer, one or more hidden layers, and an output layer, all organized within a node structure. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.
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NFV | Network Function Virtualisation (NFV) is a network architecture concept that uses virtualization to classify entire classes of network node functions into building blocks that may connect, or chain together, to create communication services. More specifically, it is the deployment of software implementations of traditional network functions (e.g. load balancers, firewalls, office switches/routers) on virtualized infrastructure rather than on function-specific specialized hardware devices.
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NFV-MANO(Network Functions Virtualisation Management and Orchestration) | Management and orchestration (MANO) is a key element of the ETSI network functions virtualization (NFV) architecture. MANO is an architectural framework that coordinates network resources for cloud-based applications and the lifecycle management of virtual network functions (VNFs) and network services. As such, it is crucial for ensuring rapid, reliable NFV deployments at scale. MANO includes the following components: the NFV orchestrator (NFVO), the VNF manager (VNFM), and the virtual infrastructure manager (VIM). |
NFV-MANO Architectural Framework(Network Functions Virtualisation Management and Orchestration Architectural Framework) | Collection of all functional blocks (including those in NFV-MANO category as well as others that interwork with NFV-MANO), data repositories used by these functional blocks, and reference points and interfaces through which these functional blocks exchange information for the purpose of managing and orchestrating NFV.
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NFVO(Network Functions Virtualisation Orchestrator) | Functional block that manages the Network Service (NS) lifecycle and coordinates the management of NS lifecycle, VNF lifecycle (supported by the VNFM) and NFVI resources (supported by the VIM) to ensure an optimized allocation of the necessary resources and connectivity.
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Omni-channel Capabilities | Omnichannel capabilities is a term used in e-commerce and retail to describe if a business has the capabilities to implement a strategy that aims to provide a seamless shopping experience across all channels, including in-store, mobile, and online.
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OpenFlow protocol | OpenFlow protocol is a protocol defined by the OpenFlow Switch Specification that allows separation of the network control plane by providing access to the forwarding plane.
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OpenFlow (standard) | OpenFlow is an open standard that enables you to control traffic and run experimental protocols in an existing network by using a remote controller. The OpenFlow components consist of a controller, an OpenFlow or OpenFlow-enabled switch, and the OpenFlow protocol. |
OpenStack | Open source software for creating private and public clouds. OpenStack software controls large pools of compute, storage, and networking resources throughout a data center, managed through a dashboard or via the OpenStack API. |
Open virtual network (OVN) | Open Virtual Network (OVN) is an Open vSwitch-based software-defined networking (SDN) solution for supplying network services to instances. |
Open vSwitch (OVS) | Open source multilayer virtual switch that supports standard interfaces and protocols.
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Operational domain | Scope of management delineated by an administrative and technological boundary.
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Orchestration (ONAP) | The arrangement, sequencing and automated implementation of tasks, rules and policies to coordinate logical and physical resources in order to meet a customer or on-demand request to create, modify or remove network or service resources.
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| Process automation | Process automation refers to the usage of technology to automate complex processes. It typically has three functions: automating processes, centralising information, and reducing the requirement for input from people. It is designed to remove bottlenecks and reduce errors and data loss, all while increasing transparency, communication across departments, and processing speed. |
Raw Model | In the context of machine learning, a 'raw model' typically refers to a model that has been trained on data without much preprocessing or feature engineering. It is a basic model without any fine-tuning or optimisation.
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Rectification Activation Function | Rectification is the process of using a rectifier activation function (also referred to as a Rectified Linear Unit or ReLU): Rectified linear units, allow for faster and effective training of deep neural architectures on large and complex datasets compared to sigmoid function or similar activation functions.
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Recurrent Neural Network | RNN stands for Recurrent Neural Network. This is a type of artificial neural network that can process sequential data, recognise patterns, and predict the final output. This type of neural network is called recurrent because it can repeatedly perform the same task or operation on a sequence of inputs.
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| Reinforcement learning | Reinforcement learning, in the context of machine learning and artificial intelligence (AI), is a type of dynamic programming that trains algorithms using a system of reward and punishment. |
Resource Facing Services (RFS) | A logical capability that is packaged as part of a product offering by service providers to their customers, but which is not directly visible to and/or usable by those customers. The logical functionality can be derived from underlying network or information technology (i.e., MPLS capabilities provided as part of a router), or may be delivered or supplied by staff or contractors employed by the service provider.
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Resource slice | A grouping of physical or virtual (network, compute, storage) resources. A resource slice could be one of the components of Network Slice, however on its own does not represent fully a Network Slice. |
| Robotic Process Automation (RPA) | Robotic Process Automation (RPA) is a type of AI; it is a software technology that allows people to configure robots to perform rules-based tasks. It can be particularly useful for processes with predictable and frequent interactions with multiple applications.
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SecOps | Security operations, also known as SecOps, refers to a business combining internal information security and IT operations practices to improve collaboration and reduce risks.
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Self-learning AI | Self-learning models are AI models that, once deployed, can be optimised by training them on data that becomes more available over time. This process prevents engineers from having to begin building new AI models from scratch every single time they collect more data.
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Software-defined networking (SDN) | A programmable network approach that supports the separation of control and forwarding planes via standardized interfaces.
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Self-configuration | A process by which computer systems or networks automatically adapt their own configuration of components without human direct intervention.
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| Self-organising network (SON) | The term self-organising network comes from the mobile radio network industry and refers to automated planning, configuration, management, optimisation and healing of a network. |
Service access point | A Service Access Point is a kind of Resource Function (RF) that handles access into and out of another RF, such as an application RF or virtualized appliance RF.
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Serverless Architecture | Serverless architecture is a cloud-computing execution model where the cloud provider dynamically manages the allocation of machine resources. Pricing is based on the actual amount of resources consumed by an application, rather than pre-purchased units of capacity. This architecture allows developers to build and run applications without managing the underlying infrastructure.
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Service chaining (NFV) | Network service chaining, also known as service function chaining (SFC) is a capability that uses software-defined networking (SDN) capabilities to create a service chain of connected network services (such as L4-7 like firewalls, network address translation [NAT], intrusion protection) and connects them in a virtual chain. This capability can be used by network operators to set up suites or catalogs of connected services that enable the use of a single network connection for many services, with different characteristics. |
Single Source of Truth | A single source of truth can be defined as a centralized and authoritative data repository that serves as the definitive reference for all relevant information within an organization.
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| Software (Engineering) Governance | Software Engineering Governance or Software Governance is the set of structures, processes and policies by which the software development and deployment function within an organisation is directed and controlled to yield business values and to mitigate risk. |
Software defined exchanges (SDX) | Software Defined IXP (SDX) is an internet exchange that utilizes SDN to do interdomain routing. In addition, SDX design incorporates high levels of programmability, open APIs, shared resources across multiple domains, dynamic provisioning, resource discovery, quick resource integration and configuration, and granulated control of resources.
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| Supervised learning / Supervised machine learning | Supervised learning, also known as supervised machine learning, is an approach to creating artificial intelligence (AI), where a computer algorithm is trained on input data that has been labelled for a particular output. The model is trained until it can detect the underlying patterns and relationships between the input and output labels, enabling it to yield accurate labelling results when presented with never-before-seen data. Also: “Note 2 – Supervised machine learning and unsupervised machine learning are two examples of machine learning types.” From ITU Recommendation Y.3172 (06/19). |
Switch abstraction interface (SAI) | Definition of the API to provide a vendor-independent way of controlling forwarding elements, such as a switching ASIC, an NPU or a software switch in a uniform manner.
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| Technical Reference Model (TRM) | Architecture of generic services and functions that provides a foundation on which more specific architectures and architectural components can be built.
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The Network Data Analytics Function (NWDAF) | A network function that collects data from various network functions, application functions, as well as operations, administration, and management (OAM) systems, and operational support systems. Note: This term is frequently used in 5G architecture.
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Training Data | The data that are used to try to fit the best combination of weights and biases to a machine learning algorithm to minimize a loss function over the prediction range.
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Training model | A machine learning training model is a process in which a machine learning (ML) algorithm is fed with sufficient training data to learn from. ML models can be trained to benefit manufacturing processes in several ways. The result of the process is a trained model.
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Transfer Learning | A technique in machine learning in which an algorithm learns to perform one task, such as recognising cars, and then is used as the starting point for a second, different task such as recognising cats. By using the knowledge from the first task the model can learn more quickly and effectively on the second task.
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Transformers | A procedure that modifies a dataset.
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| Unsupervised learning / Unsupervised machine learning | Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyse and cluster unlabelled datasets. These algorithms discover hidden patterns or data groupings without human intervention. Its ability to discover similarities and differences in information makes it the ideal solution for exploratory data analysis, cross-selling strategies for offering different products to customers, customer segmentation, and image recognition. |
User interface orchestration | User Interface Orchestration defines, formats and structures the sequence of user interfaces (UIs) needed for a process. For example, the orchestration of UI during a service request from customers.
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Validation Data | ‘Validation data’ means data used for providing an evaluation of the trained AI system and for tuning its non-learnable parameters and its learning process, among other things, in order to prevent underfitting or overfitting; whereas the validation dataset is a separate dataset or part of the training dataset, either as a fixed or variable split.
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Vertical scaling | Vertical scaling (or scaling up) means that you scale by adding more power (CPU, RAM) to an existing machine.
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Virtual content delivery network | A content delivery network using virtualisation technology that enables the allocation of virtual storage, virtual machines, and network resources according to providers' requirements in a dynamic and scalable manner.
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Virtual eXtensible Local Area Network (VXLAN) | Virtual eXtensible Local Area Network (VXLAN) enables the encapsulation of Ethernet frames inside UDP packets with a designated UDP destination port (4789). VXLAN allows users to overlay L2 networks on top of existing L3 networks. In the data center, it is commonly used to stretch an L2 network across multiple racks. |
Virtual routing and forwarding (VRF) | Virtual Routing and Forwarding is a layer 3 abstraction, which provides a separate routing table for each instance, usually this is done by adding some sort of VRFID to the routing table lookup.
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Virtualisation | Abstraction of network or service objects to make them appear generic, i.e. disassociated from the underlying hardware implementation specifics.
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Virtualised network function (VNF) - virtual network function | Virtual Network Function (VNF) is a network task written as software that can be provided in a virtualised manner (i.e. firewall, router, switch). |
Workflow | The sequence of steps through which a piece of work passes from initiation to completion. |
Workflow management | Workflow management (WFM) is a technology supporting the re-engineering of business and information processes. It involves: Defining workflows, (...) and providing for fast (re)design and (re)implementation of the processes as business needs and information systems change.
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Zero-touch provisioning (ZTP) or Zero-touch enrolment | Zero-touch provisioning (ZTP), or zero-touch enrolment, is the process of remotely provisioning large numbers of network devices such as switches, routers and mobile devices without having to manually program each one individually. |
GLOSSARY
Abbreviation/ Acronym | Description/Definition |
|---|---|
| ABE | Aggregate Business Entity |
| ACMM | Analysis Capability Maturity Model |
| AI | Artificial Intelligence |
| AIOps | Artificial Intelligence for IT Operations |
| AMC | Autonomic Management and Control |
| APT | Advanced Persistent Threat |
| AMM | Automation Maturity Model |
| AnLF | Analytics Function |
| ARCMM | Architecture Capability Maturity Model |
| AWS | Amazon Web Services |
| BPMM | Business Process Maturity Model |
| BPMN | Business Process Model and Notation |
BSS | Business Support System |
CBP | Ciena Blue Planet |
CCITT | International Telegraph and Telephone Consultative Committee |
| CDE | Component DEscription |
| CDN | Content Delivery Network |
| CFS | Customer Facing Services |
| CLI | Command Line Interface |
| CMM | (Service) Capability Maturity Model |
| CMMI | Capability Maturity Model Integrated |
| CNA | Cloud Native Application |
CNI | Container Network Interface |
CNF | Containerised Network Function |
CSP | Communications Service Provider |
| D&I | Decoupling & Integration |
DC | Data Centre |
DCN | Data Communication Network |
| DE | Decision Element |
| DevOps | Development and Operations |
| DPMM | Document Process Maturity Model |
| DPRA | Digital Platform Reference Architecture |
| DTN | Data Transfer Node |
| EACM | Enterprise Architecture Content Metamodel |
| EGM | Engagement Management |
| eLMM | e-Learning Maturity Model |
ETSI | European Telecommunications Standards Institute |
EVPN | Ethernet VPN |
| FOSS | Free and Open Source Software |
FRR | Free Range Routing |
| GANA | Generic Autonomic Networking Architecture |
Geneve | Generic Network Virtualisation Encapsulation |
GRE | Generic Routing Encapsulation |
| GS | Group Specification |
| GNA-G | Global Network Advancement Group |
GVM | Generalised Virtualisation Model |
| IaaS | Infrastructure as a Service |
IaC | Infrastructure as Code |
| IDE | Integrated Development Environment |
| IDS | Intrusion Detection System |
| IDSP | Integrated Digital Service Provider |
IG | Information Governance |
IEEE | Institute of Electrical and Electronics Engineers |
IETF | Internet Engineering Task Force |
| IM | Intelligence Management |
| IMS | IP Multimedia Subsystem |
IOA | Indicators of Attack |
IOC | Indicators of Compromise |
IPS | Intrusion Prevention System |
IRTF | Internet Research Task Force |
| IS/ICT CMF | Information Systems and Information Communication Technology Management Capability Maturity Framework |
| ISO | International Organisation for Standardisation |
| ISO 15504 – SPICE | Software Process Improvement and Capability Determination |
| IT-BSC Maturity Model | IT governance tool Balanced Scorecard Maturity Model |
| ITPM3 | IT Performance Measurement Maturity Model |
ITU | International Telecommunication Union |
ITU-T | Telecommunication Standardisation Sector of ITU |
IXP | Internet Exchange Point |
K8s | Kubernetes |
KPI | Key Performance Indicator |
LAN | Local Area Network |
| LSO | Lifecycle Service Orchestration |
| M2M | Machine-to-Machine |
MANO | Management and Orchestration |
| MCC | Management-Control Continuum |
MDSO | Multi-Domain Service Orchestration |
| MDVPN | Multi-Domain Virtual Private Networks |
| ME | Managed Entity |
MEF | Metro Ethernet Forum |
NaaS/naas | Network as a Service |
NaC | Network as Code |
NAT | Network Address Translation |
NAO | Network Automation and Orchestration |
NCO | Network Controls and Orchestration |
| NE | Network Element |
NEP | Network Equipment Providers |
NETCONF | Network Configuration Protocol |
| NF | Network Function |
| NFD | Network Function Disaggregation |
NFV | Network Function Virtualisation |
NFVI | Network Function Virtualisation Infrastructure |
NFV-O | Network Function Virtualisation Orchestrator |
NGN | Next Generation Network |
| NMM | Network Maturity Model |
| NOC | Network Operations Centre |
NREN | National Research and Education Network |
NWDAF | Network Data Analytics Function |
NRO | Network Resource Optimisation |
| NS | Network Service |
NSA | Network Service Agent |
NSI | Network Service Interface |
| NSSAI | Network Slice Selection Assistance Information |
| NVGRE | Network Virtualisation over GRE (Generic Routing Encapsulation) |
| OAMP | Operations, Administration, Maintenance and Provisioning |
| OASIS | Organisation for the Advancement of Structured Information Standards |
OAV | Orchestration, Automation and Virtualisation |
| OCP | Open Compute Project |
| ODA | Open Digital Architecture |
ODL | OpenDaylight |
| ODM | Operational Domain Management/Manager |
| OESS | Open Exchange Software Suite |
OGF | Open Grid Forum |
ONAP | Open Networking Automation Platform |
ONOS | Open Network Operating System |
OPNFV | Open Platform for NFV Project |
OSM | Open Source MANO |
OSS | Operations Support System |
OVN | Open Virtual Network |
| OVS | Open vSwitch |
PaaS | Platform as a Service |
R&D | Research and Development |
R&E | Research & Education |
| REST | Representational State Transfer |
| RF | Resource Function |
| RFS | Resource Facing Services |
| SaaS | Software as a Service |
SAI | Switch Abstraction Interface |
| SDDC | Software-Defined Data Center |
SDN | Software Defined Network |
| SDO | Standards Developing Organization |
SD-WAN | Software-Defined networking in a Wide Area Network (WAN) |
SDX | Software-Defined Exchange |
SFC | Service Function Chaining (also known as Network Service Chaining) |
SIEM | Security Information and Event Management |
| S-NSSAI | Single Network Slice Selection Assistance Information |
| SOA | Service Oriented Architecture |
| SOAP | Simple Object Access Protocol |
| SOAR | Security Orchestration, Automation, and Response |
| SOC | Security Operations Centre |
SPA | Service Provider Architecture |
STF | Service and Technology Forum |
| STP | Service Termination Point |
STT | Stateless Transport Tunneling |
TMF | TM Forum |
| TOGAF | The Open Group Architecture Framework |
| TOSCA | Topology and Orchestration Specification for Cloud Applications |
| TEVV | Test and Evaluation, Verification and Validation |
| TTPs | Tactics, Techniques, and Procedures |
VCDN | Virtual Content Delivery Network |
VIM | Virtual Infrastructure Management |
VM | Virtual Machine |
VNF | Virtual Network Function |
| VNFM | Virtualised Network Function Manager |
| VNO | Virtual Network Operator |
VPN | Virtual Private Network |
VPP | Vector Packet Processing |
VRF | Virtual Routing Function |
VSI | Virtual Switch Instance |
VTEP | Virtual Tunnel End Point |
VXLAN | Virtual Extensible LAN |
WAN | Wide Area Network |
| WFM | Workflow Management |
XaaS | Anything as a Service |
XDP | eXpress Data Path |
XML | eXtensible Markup Language |
XSOAR | Extended Security Orchestration, Automation, and Response |
YANG | Yet Another Next Generation |
| ZOOM | Zero-touch Orchestration, Operations and Management |
| ZSM | Zero-touch network and Service Management |
ZTP | Zero Touch Provisioning |
Whitepapers
- Orchestration, Automation and Virtualisation Terminology Version 3.0 (Oct 29,2024)
- Orchestration, Automation and Virtualisation Terminology Version 2.0 (Jan 10,2023)
- Orchestration, Automation and Virtualisation Terminology Version 1.1 (Jan. 20, 2021)
- Orchestration, Automation and Virtualisation Terminology (Apr. 8, 2020)