GN5-1 Orchestration, Automation and Virtualisation Terminology

© GÉANT Association on behalf of the GN5-1 project. The research leading to these results has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101100680 (GN5-1). Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. 29-10-2024 Orchestration, Automation and Virtualisation Terminology Version 3.0 Grant Agreement No.: 101100680 Work Package: WP6 Task Item: Task 4 Nature of Document: White Paper Dissemination Level: PU (Public) Document ID: GN5-1-24-79G78F Authors: Iacovos Ioannou (CYNET), Susanne Naegele Jackson (FAU/DFN), Daniel Lete (HEAnet), Kostas Stamos (GRNET), Hamzeh Khalili (RedIRIS/i2CAT), Martin Dunmore (Jisc), Maria Isabel Gandia (RedIRIS/CSUC), Ivana Golub (PSNC), Tim Chown (Jisc) Abstract This document provides a list of terms and abbreviations in the context of Orchestration, Automation and Virtualisation. Definitions are provided based on standardisation documents wherever possible; some have also been extended to reflect the understanding of the terms as used by a large number of NRENs in the GÉANT community. As of version 1.1, the document has also been adopted by the GNA-G Network Automation working group as their reference terminology. Orchestration, Automation and Virtualisation Terminology Document ID: GN5-1-24-79G78F ii Table of Contents Executive Summary 1 1 Introduction 2 2 Term Definitions 3 3 Acronyms 32 4 Conclusions 40 References 41 Terminology Documents 41 Table of Tables Table 2.1: Term definitions 31 Table 3.1: Acronyms 39 Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 1 Executive Summary It first became evident during discussions at the GN4-3 1 Future Services Strategy Workshop which took place in Amsterdam on 09 May 2009, that different usages and understandings existed for various terms in the context of Orchestration, Automation and Virtualisation (OAV). To address this, a Focus Group (FG) on terminology was established within the Network Services Evolution and Development task (Task 2) of the Network Technologies and Services Development work package (WP6) in GN4-3 to provide definitions and a common understanding of these terms and facilitate better collaborative discussions within the GÉANT National Research and Education Network (NREN) community and globally. The Focus Group was established with a six-month time frame to conduct an initial investigation. This involved compiling a list of key OAV terms and acronyms, accompanied by brief definitions. When possible, these definitions drew on documents from recognised standardisation bodies. In cases where such references were not available, the Focus Group developed definitions based on internal discussions and feedback from the GN43 WP6 T2 consensus-building team. After the end of the Focus Group, the list remains subject to updates as needed. The latest list of terms and abbreviations, updated under GN5-1 2 WP6 Task 4, is included in this document (v3.0) and can also be found on WP6’s public OAV wiki [Wiki]. The original version of this document was shared with the Network Automation working group of the Global Network Advancement Group (GNA-G) [GNA-G]. Subsequent versions (to date, version 1.1, version 2.0, and the current version 3.0) have been amended in collaboration with that WG and adopted by them as their terminology document of reference. This latest version includes machine-learning terminology related to OAV. This joint work aims to further promote the potential for international collaboration on OAV beyond the GÉANT community. 1 https://geant.org/projects/a-european-success-story/ 2 https://geant.org/gn5-1/ Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 2 1 Introduction Orchestration, automation, and virtualisation have become key enablers for service providers to facilitate faster, agile, more efficient, and cost-effective service development, deployment, and provisioning. Adopting OAV principles allows organisations to better utilise their resources, including physical and virtual hardware and software, to facilitate their digital transformation process. The GÉANT and NREN community has been on this path for several years. Organisations are at various stages in their journeys but for most, the motivation for their efforts stems from an internal focus—prioritising improvements within their own areas of operation. Therefore, today, most of the work known so far is singledomain and domain-specific. This document aims to achieve a common language across all GÉANT Project 3 deliverables and to serve as a terminology of reference for use by the GÉANT and NREN community. Where necessary, detailed descriptions providing background for concise formal definitions are given. The original version of this document was shared with the Network Automation working group of the Global Network Advancement Group (GNA-G). Subsequent versions (to date, version 1.1, version 2.0, and the current version 3.0) have been amended in collaboration with that WG and adopted by them as their terminology document of reference. This latest version includes machine-learning terminology related to OAV. The updated list of OAV terms along with their definitions can be found in section 2 of this document, while the list of abbreviations is provided in section 3. A list of key terminology documents from which the definitions for the listed terms were drawn is included in the Terminology Documents section under References. 3 https://geant.org/projects/ Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 3 2 Term Definitions The following list of terms aims to serve as a reference for use by the GÉANT and NREN community in the area of OAV. The term definitions have where possible been drawn from terminology documents issued by standardisation bodies. The remaining terms and definitions have been identified and agreed upon internally by the WP6, in collaboration with the Network Automation WG of the GNA-G (key documents, and/or those cited more than once, are listed individually under Terminology Documents): OAV Term Definition and Reference(s) or Source 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, causality determination, and, for networking, predictive analytics, root cause analysis, and real-time automation of repetitive tasks. Reference(s) or Source: https://www.gartner.com/en/informationtechnology/glossary/aiops-artificial-intelligence-operations 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. Reference(s) or Source: https://www.encora.com/insights/machinelearning-what-is-adaptive-ml 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. Reference(s) or Source: “The Language of Trustworthy AI: An In-Depth Glossary of Terms (updated August 4, 2024)” (https://docs.google.com/spreadsheets/d/e/2PACX1vTRBYglcOtgaMrdF11aFxfEY3EmB31zslYI4q2_7ZZ8z_1lKm7OHtF0t4xIscku ogNZ3hRZAaDQuv_K/pubhtml) NIST(Reznik,_Leon) AI Accuracy Closeness of computations or estimates to the exact or true values that the statistics were intended to measure. Reference(s) or Source: “The Language of Trustworthy AI: An In-Depth Glossary of Terms (Updated August4, 2024)” (https://docs.google.com/spreadsheets/d/e/2PACX1vTRBYglcOtgaMrdF11aFxfEY3EmB31zslYI4q2_7ZZ8z_1lKm7OHtF0t4xIscku ogNZ3hRZAaDQuv_K/pubhtml) Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 4 OAV Term Definition and Reference(s) or Source (https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-3.pdf) 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 selfdetermined 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. Reference(s) or Source: https://aws.amazon.com/what-is/ai-agents/ 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 largescale use cases. Reference(s) or Source: https://www.run.ai/guides/machine-learning-inthe-cloud/ai-as-a-service 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. Reference(s) or Source: https://hexa-x.eu/wpcontent/uploads/2023/07/Hexa-X-D1.4-Final.pdf AI Policy Enforcer AI functionality to implement a recommended policy. Reference(s) or Source: https://hexa-x.eu/wp-content/uploads/ 2023/07/Hexa-X-D1.4-Final.pdf 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. Reference(s) or Source: https://www.ringcentral.com/virtualagent.html;“TM Forum AI Fundamentals” course [TMF_AIF]; TM Forum “AI and its pivotal role in transforming operations” report and webinar [TMF_AI] 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. Reference(s) or Source: https://www.tech-invite.com/3m23/toc/tinv-3gpp23-288_c.html Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 5 OAV Term Definition and Reference(s) or Source Application Programming Interface (API) An API is a set of commands, functions, protocols, and objects that programmers can use to create software or interact with an external system. Data can be shared through an application programming interface. Reference(s) or Source: Based on https://techterms.com/definition/api and https://searchapparchitecture.techtarget.com/definition/applicationprogram-interface-API Architecture component An architecture component is a nontrivial, nearly independent, and replaceable part of a system that fulfils a clear function in the context of a well-defined architecture. Reference(s) or Source: TM Forum Reference Document, TMF071 ODA Terminology, Release 19.0.1, October 2019 [TMF071] Architecture principles Architecture principles define the underlying general rules and guidelines for the use and deployment of all IT resources and assets across an organisation. They reflect a level of consensus among the various elements of the enterprise or organisations and form the basis for making future IT decisions. Reference(s) or Source: https://pubs.opengroup.org/architecture/togaf8doc/arch/chap29.html 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. Reference(s) or Source: “The Language of Trustworthy AI: An In-Depth Glossary of Terms (updated August 4, 2024)” (https://docs.google.com/spreadsheets/d/e/2PACX1vTRBYglcOtgaMrdF11aFxfEY3EmB31zslYI4q2_7ZZ8z_1lKm7OHtF0t4xIscku ogNZ3hRZAaDQuv_K/pubhtml) NIST (AI_Ethics_Mark_Coeckelbergh) Artificial intelligence Artificial intelligence (AI) is the ability of a digital computer or computercontrolled 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. Reference(s) or Source: based on https://www.britannica.com/technology/artificial-intelligence; and Kaplan, A., & Haenlein, M. “Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence”. Business Horizons. 2019; 62:15–25 (https://www.sciencedirect.com/science/article/abs/pii/S00076813183013 93) Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 6 OAV Term Definition and Reference(s) or Source Automated root cause analysis Automated RCA is the process of using automation to investigate incident root causes in real time using AI/ML. Reference(s) or Source: https://www.bigpanda.io/blog/why-automatedroot-cause-analysis-matters/ Automated service provisioning Automated service provisioning is the ability to deploy an information technology or telecommunications service by using predefined procedures that are carried out electronically without requiring human intervention. Reference(s) or Source: multiple sources including US government documents, e.g. “Financial Services and General Government Appropriations for 2016”, p. 201 (https://books.google.de/books?id=h4SVIm3XaUsC&printsec=frontcover& hl=de&source=gbs_ge_summary_r&cad=0#v=onepage&q=201&f=false) Automation Processing tasks in a repeatable manner to yield the same result every time without human intervention. Reference(s) or Source: internal definition 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. Reference(s) or Source: https://www.baesystems.com/enus/definition/what-is-ai-enabled-autonomy Auto-scaling support Autoscale allows you to automatically scale your applications or resources based on demand. Reference(s) or Source: https://learn.microsoft.com/en-us/azure/azuremonitor/autoscale/autoscale-get-started 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. Reference(s) or Source: https://www.bmc.com/blogs/bias-variancemachine-learning/ 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. Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 7 OAV Term Definition and Reference(s) or Source Reference(s) or Source: https://www.techopedia.com/definition/34116/bidirectional-encoderrepresentations-from-transformers-bert 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. Reference(s) or Source: https://link.springer.com/article/10.1057/s41272019-00191-9; https://en.wikipedia.org/wiki/Big_data 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. Reference(s) or Source: ITU Recommendation Y.3652 “Big data driven networking – requirements” (06/20) (https://www.itu.int/rec/T-RECY.3652-202006-I/en) Blockchain A blockchain is an expanding list of cryptographically signed, irrevocable transactional records shared by all participants in a network. Reference(s) or Source: TM Forum Reference Document, “TMF071 ODA Terminology”, Release 19.0.1, October 2019 [TMF071] Cgroups (control groups) Cgroups are Linux kernel mechanisms to restrict and measure resource allocations to each process group. You can use cgroups to allocate resources such as CPU time, network, and memory. Reference(s) or Source: Bharadwaj, R. “Comprehending Processes, Address Space, and Threads: Namespaces and cgroups”, in Mastering Linux Kernel Development, Packt, October 2017 [Bharadwaj] 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. Reference(s) or Source: https://www.oracle.com/chatbots/what-is-achatbot/ 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. Reference(s) or Source: https://www.britannica.com/technology/ChatGPT Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 14 OAV Term Definition and Reference(s) or Source 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. Reference(s) or Source: “The Language of Trustworthy AI: An In-Depth Glossary of Terms (updated August 4, 2024)” (https://docs.google.com/spreadsheets/d/e/2PACX1vTRBYglcOtgaMrdF11aFxfEY3EmB31zslYI4q2_7ZZ8z_1lKm7OHtF0t4xIscku ogNZ3hRZAaDQuv_K/pubhtml), NIST(NSCAI) 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. Reference(s) or Source: “Proposal for a regulation of the European Parliament and of the Council on harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union Legislative Acts” (https://www.europarl.europa.eu/meetdocs/2014_2019/plmrep/COMMIT TEES/CJ40/DV/2023/05-11/ConsolidatedCA_IMCOLIBE_AI_ACT_EN.pdf), P. 42 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. Reference(s) or Source: https://encord.com/glossary/gpt-definition/ Hierarchical orchestration Orchestration decomposed into one or more hierarchical interactions where parts of the service are delegated to a subordinate orchestrator. Reference(s) or Source: ETSI GS ZSM 007 V1.1.1 (2019-08), “Zero-touch network and Service Management (ZSM); Terminology for concepts in ZSM” [ETSI_ZSM_007] Holistic Anomaly Detection (e.g., via multi-vector AI/MLbased 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 a comprehensive approach to anomaly detection using a variety of methods. Reference(s) or Source: https://www.ibm.com/topics/anomaly-detection Horizontal Scaling Horizontal scaling (or scaling out) means that you scale by adding more machines into your pool of resources. Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 15 OAV Term Definition and Reference(s) or Source Reference(s) or Source: https://ibm.github.io/data-science-bestpractices/scaling.html 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. Reference(s) or Source: https://research.ibm.com/blog/what-is-humancentered-ai 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. Reference(s) or Source: based on International Telegraph and Telephone Consultative Committee (CCITT) Recommendation I.312 / Q.1201 (10/92) “Principles of Intelligent Network Architecture” (https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-REC-I.312-199210I!!PDF-E&type=items) Intent-based Networking A software-enabled automation process that uses high levels of intelligence, analytics, and orchestration to improve network operations and uptime. Reference(s) or Source: https://www.juniper.net/us/en/researchtopics/what-is-intent-based-networking.html Intent-based policy / network Technology incorporating artificial intelligence (AI) and machine learning to automate administrative tasks across a network. Reference(s) or Source: based on TM Forum Reference Document, “TMF071 ODA Terminology”, Release 19.0.1, October 2019 [TMF071] 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. Reference(s) or Source: based on https://en.wikipedia.org/wiki/Internet_of_things and https://www.techtarget.com/iotagenda/definition/Internet-of-Things-IoT Kubernetes Kubernetes is an open-source platform used to automate the deployment, scaling, and management of containerized applications. It orchestrates Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 16 OAV Term Definition and Reference(s) or Source 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. Reference(s) or Source: https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/ Language Model A machine-learning model designed to represent the language domain. Reference(s) or Source: https://www.deepset.ai/blog/what-is-a-languagemodel 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. Reference(s) or Source: “The Language of Trustworthy AI: An In-Depth Glossary of Terms (updated August 4, 2024)” (https://docs.google.com/spreadsheets/d/e/2PACX1vTRBYglcOtgaMrdF11aFxfEY3EmB31zslYI4q2_7ZZ8z_1lKm7OHtF0t4xIscku ogNZ3hRZAaDQuv_K/pubhtml), NIST (AI_Assurance_2022) 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.) Reference(s) or Source: based on ETSI GR ENI 004 V2.1.1 (2019-10), “Experiential Networked Intelligence (ENI); Terminology for Main Concepts in ENI” (https://www.etsi.org/deliver/etsi_gr/ENI/001_099/004/02.01.01_60/gr_e ni004v020101p.pdf) and Telecommunication Standardisation Sector of ITU (ITU-T) Recommendation Y.3177 (02/2021) “Architectural framework for artificial intelligence-based network automation for resource and fault management in future networks including IMT2020”(https://www.itu.int/rec/dologin_pub.asp?lang=s&id=T-REC-Y.3177202102-I!!PDF-E&type=items) Management The processes for fulfilment, assurance, and billing of services, network functions, and resources in both physical and virtual infrastructure including compute, storage, and network resources. Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 17 OAV Term Definition and Reference(s) or Source Reference(s) or Source: based on Telecommunication Standardisation Sector of ITU (ITU-T) Recommendation Y.3100 (09/2017); “Series Y: Global Information Infrastructure, Internet Protocol Aspects, Next-Generation Networks, Internet of Things and Smart Cities – Future networks: Terms and definitions for IMT-2020 network” [ITU-T_Y.3100] Management API A software interface that allows the performing of all management operations before, during and after the use of a service. Reference(s) or Source: based on TM Forum Reference Document, “TMF071 ODA Terminology”, Release 19.0.1, October 2019 [TMF071] Management domain A collection of physical or functional elements under the control of an entity (e.g. organisation, NREN) that provides the fulfilment, assurance, and billing of services, network functions, and resources in both physical and virtual infrastructures. Reference(s) or Source: internal definition based on Telecommunication Standardisation Sector of ITU (ITU-T) Recommendation Y.3100 (09/2017), “Series Y: Global Information Infrastructure, Internet Protocol Aspects, Next-Generation Networks, Internet of Things and Smart Cities – Future networks: Terms and definitions for IMT-2020 network” [ITU-T_Y.3100] and Telecommunication Standardisation Sector of ITU (ITU-T) Recommendation Y.110 (06/98); “Series Y: Global Information Infrastructure – General: Global Information Infrastructure principles and framework architecture” [ITU-T_Y.110] Maturity level A maturity level is a defined evolutionary plateau for organisational process improvement. Each maturity level matures an important subset of an 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. Reference(s) or Source: https://www.megatronicstech.com/maturity-levelof-technology/ 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. Reference(s) or Source: based on https://www.bmc.com/blogs/digitalmaturity-models/, https://link.springer.com/article/10.1007/s12599-0090044-5 and the TM Forum “AI Fundamentals” course [TMF_AIF] Microservices 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 architecture, with its own code base, infrastructure, and database. The microservices work together, Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 18 OAV Term Definition and Reference(s) or Source communicating through web APIs or messaging queues to respond to incoming events. Reference(s) or Source: https://www.nginx.com/learn/microservices/ 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. Reference(s) or Source: https://www.sciencedirect.com/book/9780123850850/model-basedengineering-for-complex-electronic-systems 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. Reference(s) or Source: https://www.qualtrics.com/uk/experiencemanagement/customer/natural-languagegeneration/?rid=ip&prevsite=en&newsite=uk&geo=RO&geomatch=uk Natural language processing (NLP) The ability of a machine to process, analyse, and mimic human language, either spoken or written. Reference(s) or Source: The Language of Trustworthy AI: An In-Depth Glossary of Terms (updated August 4, 2024)” (https://docs.google.com/spreadsheets/d/e/2PACX1vTRBYglcOtgaMrdF11aFxfEY3EmB31zslYI4q2_7ZZ8z_1lKm7OHtF0t4xIscku ogNZ3hRZAaDQuv_K/pubhtml), NIST (NSCAI) Network automation The process of automating the configuration, management, testing, deployment, and operations of physical and virtual devices within a network. Reference(s) or Source: https://www.juniper.net/uk/en/products-services/what-is/networkautomation/ https://www.cisco.com/c/en/us/solutions/automation/networkautomation.html https://www.netsync.com/practices/service-provider/networkautomation/ Network controller A functional block that centralises 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. Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 19 OAV Term Definition and Reference(s) or Source Reference(s) or Source: ETSI GS NFV 003 V1.4.1 (2018-08), “Network Functions Virtualisation (NFV); Terminology for Main Concepts in NFV” [ETSI_NFV_003] Network function (NF) A functional building block within a network infrastructure, which has welldefined external interfaces and functional behaviour. Reference(s) or Source: ETSI GS ZSM 007 V1.1.1 (2019-08), “Zero-touch network and Service Management (ZSM); Terminology for concepts in ZSM” [ETSI_ZSM_007] 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. Reference(s) or Source: https://drivenets.com/blog/networkdisaggregation-101/ 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. Reference(s) or Source: Telecommunication Standardisation Sector of ITU (ITU-T) Recommendation Y.3173 (02/2020) “Series Y: Global Information Infrastructure, Internet Protocol Aspects, Next-Generation Networks, Internet of Things and Smart Cities – Future networks: Framework for evaluating intelligence levels of future networks including IMT-2020 network” (https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-RECY.3173-202002-I!!PDF-E&type=items) Network namespaces A virtualisation 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. Reference(s) or Source: internal definition based on Bharadwaj, R. “Comprehending Processes, Address Space, and Threads: Namespaces and cgroups”, in Mastering Linux Kernel Development, Packt, October 2017 [Bharadwaj] 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. Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 20 OAV Term Definition and Reference(s) or Source Reference(s) or Source: internal definition based on https://www.ciena.com/insights/what-is/what-is-serviceorchestration.html Network resource Physical or logical network component of hardware, software or data in the data, control or management planes within an organisation’s infrastructure. Reference(s) or Source: internal definition Network service A collection of network functions with a well-specified behaviour (e.g. content delivery networks (CDNs) and IP multimedia subsystem (IMS)). Reference(s) or Source: internal definition based on Telecommunication Standardisation Sector of ITU (ITU-T) Recommendation Y.3515 (07/2017), “Series Y: Global Information Infrastructure, Internet Protocol Aspects, Next-Generation Networks, Internet of Things and Smart Cities – Cloud Computing: Functional architecture of Network as a Service” (https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-REC-Y.3515201707-I!!PDF-E&type=items) 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. Reference(s) or Source: https://aws.amazon.com/what-is/servicemesh/#:~:text=service%20mesh%20requirements%3F-,What%20is%20a%2 0service%20mesh%3F,the%20performance%20of%20the%20services 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. Reference(s) or Source: based on TM Forum Reference Document, “TMF071 ODA Terminology”, Release 19.0.1, October 2019 [TMF071] and the 3rd Generation Partnership Project (3GPP) Technical Specification (TS) 23.501, System architecture for the 5G System (5GS) (https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetai ls.aspx?specificationId=3144) Network slicing 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 micro level. Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 21 OAV Term Definition and Reference(s) or Source Reference(s) or Source: https://www.idginsiderpro.com/article/3231244/what-is-the-differencebetween-network-slicing-and-quality-of-service.html and https://www.samenacouncil.org/thought-leadership-read?id=151 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 organised 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. Reference(s) or Source: https://www.ibm.com/topics/neural-networks NFV Network function virtualisation (NFV) is a network architecture concept that uses virtualisation 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 virtualised infrastructure rather than on function-specific specialised hardware devices. Reference(s) or Source: based on Huang, D., & Wu, H., “Virtualization” in Mobile Cloud Computing: Foundations and Service Models, Morgan Kaufmann, 2018 (https://www.sciencedirect.com/topics/computerscience/network-function-virtualization) NFV-MANO Network function virtualisation management and orchestration (NFVMANO) is a key element of the ETSI network function virtualisation (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). Reference(s) or Source: https://www.adva.com/en/products/technology/what-is-nfv-mano NFV-MANO architectural framework Network functions virtualisation management and orchestration (NFVMANO) architectural framework is a collection of all functional blocks (including those in the NFV-MANO category and others that interwork with NFV-MANO), data repositories used by these functional blocks, and Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 22 OAV Term Definition and Reference(s) or Source reference points and interfaces through which these functional blocks exchange information to manage and orchestrate NFV. Reference(s) or Source: ETSI GS NFV 003 V1.4.1 (2018-08), “Network Functions Virtualisation (NFV); Terminology for Main Concepts in NFV” [ETSI_NFV_003] NFVO Network Functions Virtualisation Orchestrator (NFVO) is a 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 optimised allocation of the necessary resources and connectivity. Reference(s) or Source: ETSI GS NFV 003 V1.4.1 (2018-08), “Network Functions Virtualisation (NFV); Terminology for Main Concepts in NFV” [ETSI_NFV_003] 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 instore, mobile, and online. Reference(s) or Source: https://www.techtarget.com/searchcustomerexperience/definition/omnic hannel Open virtual network (OVN) An Open vSwitch-based software-defined networking (SDN) solution for supplying network services to instances. Reference(s) or Source: https://access.redhat.com/documentation/enus/red_hat_openstack_platform/13/html/networking_with_open_virtual_ network/open_virtual_network_ovn Open vSwitch (OVS) Open-source multilayer virtual switch that supports standard interfaces and protocols. Reference(s) or Source: based on https://www.openvswitch.org/ OpenFlow protocol A protocol defined by the OpenFlow Switch Specification that allows separation of the network control plane by providing programmable access to the forwarding plane. Reference(s) or Source: internal definition based on the Open Networking Foundation’s OpenFlow Switch Specification (https://www.opennetworking.org/wpcontent/uploads/2014/10/openflow-switch-v1.5.1.pdf) and https://www.opennetworking.org/sdn-definition/?nab=1 Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 23 OAV Term Definition and Reference(s) or Source OpenFlow (standard) 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 OpenFlowenabled switch, and the OpenFlow protocol. Reference(s) or Source: https://www.juniper.net/documentation/en_US/junos/topics/concept/jun os-sdn-openflow-support-overview.html OpenStack Open-source software for creating private and public clouds. OpenStack software can control large pools of compute, storage, and networking resources throughout a data centre, managed through a dashboard or via the OpenStack API. Reference(s) or Source: https://www.openstack.org/ Operational domain Scope of management delineated by an administrative and technological boundary. Reference(s) or Source: based on TM Forum Reference Document, “TMF071 ODA Terminology”, Release 19.0.1, October 2019 [TMF071] 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. Reference(s) or Source: TM Forum Technical Specification, “TMF071 Terminology for Zero-touch Orchestration, Operations and Management, Release 17.0.1, November 2017, version 0.4.1” (https://www.tmforum.org/resources/specification/tmf071-terminologyfor-zero-touch-orchestration-operations-and-management-r17-0-1/) 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. Reference(s) or Source: https://www.tibco.com/reference-center/what-isprocess-automation 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. Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 30 OAV Term Definition and Reference(s) or Source Reference(s) or Source: https://github.com/Mellanox/mlxsw/wiki/VirtualeXtensible-Local-Area-Network-(VXLAN) Internet Engineering Task Force (IETF), Request for Comments (RFC) 7348, “Virtual eXtensible Local Area Network (VXLAN): A Framework for Overlaying Virtualized Layer 2 Networks over Layer 3 Networks, August 2014” (https://datatracker.ietf.org/doc/html/rfc7348) Virtual routing and forwarding (VRF) A layer 3 abstraction, which provides a separate routing table for each instance. Usually this is done by adding some sort of VRF ID to the routing table lookup. Reference(s) or Source: internal definition based on https://en.wikipedia.org/wiki/Virtual_routing_and_forwarding Virtualisation Abstraction of network or service objects to make them appear generic, i.e. disassociated from the underlying hardware implementation specifics. Reference(s) or Source: internal definition Virtualised network function (VNF) A network task written as software that can be provided in a virtualised manner (e.g., firewall, router, switch). Reference(s) or Source: internal definition based on https://www.sdxcentral.com/networking/nfv/definitions/virtual-networkfunction/ and https://www.webopedia.com/TERM/V/virtualized-networkfunction.html Workflow The sequence of steps through which a piece of work passes from initiation to completion. Reference(s): https://www.merriam-webster.com/dictionary/workflow Workflow management (WFM) A technology supporting the re-engineering of business and information processes. It involves defining workflows and providing fast (re)design and (re)implementation of the processes, as business needs and information systems change. Reference(s) or Source: D. Georgakopoulos, D., Hornick, M., & Sheth, A., “An Overview of Workflow Management: From Process Modeling to Workflow Automation Infrastructure”, Distributed and Parallel Databases, 3, 119–153 (1995), (http://www.workflowpatterns.com/documentation/documents/workflow 95.pdf) 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. Term Definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 31 OAV Term Definition and Reference(s) or Source Reference(s) or Source: https://en.wikipedia.org/wiki/Zerotouch_provisioning and https://www.techtarget.com/searchitoperations/definition/zero-touchprovisioning-ZTP Table 2.1: Term definitions Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 32 3 Acronyms A list of abbreviations and the corresponding terms relating to OAV commonly used in the GÉANT and NREN community is given below: Acronym Full Term ABE Aggregate Business Entity ACMM Analysis Capability Maturity Model AI Artificial Intelligence AIOps Artificial Intelligence for IT Operations AMC Autonomic Management and Control AMM Automation Maturity Model ACMM 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 CMM (Service) Capability Maturity Model CMMI Capability Maturity Model Integrated CNA Cloud Native Application CNI Container Network Interface Acronyms Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 33 Acronym Full Term CSP Communications Service Provider D&I Decoupling & Integration DC Data Centre DCN Data Communication Network DE Decision Element 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 Network Architecture Geneve Generic Network Virtualisation Encapsulation GNA-G Global Network Advancement Group GRE Generic Routing Encapsulation GS Group Specification GVM Generalised Virtualisation Model IaaS Infrastructure as a Service IaC Infrastructure as Code IDE Integrated Development Environment IDSP Integrated Digital Service Provider Acronyms Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 34 Acronym Full Term IEEE Institute of Electrical and Electronics Engineers IETF Internet Engineering Task Force IG Information Governance IM Intelligence Management IMS IP Multimedia Subsystem 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 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 Network ME Managed Entity MEF Metro Ethernet Forum Acronyms Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 35 Acronym Full Term NaaS/naas Network as a Service NaC Network as Code NAO Network Automation and Orchestration NAT Network Address Translation NCO Network Controls and Orchestration NE Network Element NEP Network Equipment Provider 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 NREN National Research and Education Network 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 Acronyms Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 36 Acronym Full Term OAV Orchestration, Automation and Virtualisation OCP Open Compute Project ODA Open Digital Architecture ODL OpenDaylight ODM Operational Domain Management ODM Operational Domain 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 AnLF Analytics Function APT Advanced Persistent Threat CFS Customer Facing Services CLI Command Line Interface CNF Containerised Network Function DevOps Development and Operations IDS Intrusion Detection System IOA Indicators of Attack IOC Indicators of Compromise IPS Intrusion Prevention System Acronyms Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 37 Acronym Full Term KPI Key Performance Indicator NOC Network Operations Centre NWDAF Network Data Analytics Function 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 Centre SDN Software-Defined Network SDO Standards Developing Organisation 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 Acronyms Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 38 Acronym Full Term STP Service Termination Point STT Stateless Transport Tunneling TEVV Test and Evaluation, Verification and Validation TMF TM Forum TOGAF The Open Group Architecture Framework TOSCA Topology and Orchestration Specification for Cloud Applications 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 Acronyms Orchestration, Automation and Virtualisation Terminology Version 3.0 Document ID: GN5-1-24-79G78F 39 Acronym Full Term YANG Yet Another Next Generation ZOOM Zero-touch Orchestration, Operations & Management ZSM Zero-touch network and Service Management ZTP Zero-Touch Provisioning Table 3.1: Acronyms