Recommendation ITU-T Y.3510 provides requirements for cloud computing infrastructure; these include the essential capabilities for processing, storage and networking resources, as well as the capabilities of resource abstraction and control.
This Recommendation provides overview and functional requirements of data storage federation. Data storage federation provides a single virtual volume from multiple data sources in heterogeneous storages. In this Recommendation, configuration for logical components, and ecosystem of data storage federation as well as cloud computing based data storage federation are introduced for data storage federation. Functional requirements are derived from use cases.
Draft Recommendation ITU-T Y.3504 (formerly Y.DaaS-arch), "Functional Architecture for Desktop as a Service"
Cloud computing is a paradigm for enabling network access to a scalable and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on demand. See Rec. ITU-T Y.3500 | ISO/IEC 17788. The CCRA presented in this Recommendation | International Standard provides an architectural framework that is effective for describing the cloud computing roles, sub-roles, cloud computing activities, cross-cutting aspects, as well as the functional architecture and functional components of cloud computing.
This Recommendation provides a cloud computing framework by identifying high-level requirements for cloud computing. The Recommendation addresses the general requirements and use cases for: – Cloud computing; – Infrastructure as a service (IaaS), network as a service (NaaS), desktop as a service (DaaS), platform as a service (PaaS), communication as a service (CaaS) and big data as a service (BDaaS); – Inter-cloud computing, end-to-end cloud computing management, trusted cloud service, and cloud infrastructure. This Recommendation addresses a set of use cases and related requirements which are included in Appendix I. Appendix II provides information on the methodology and edition plan of this Recommendation.
This Recommendation specifies framework of big data driven networking. The scope of this recommendation includes the model architecture of big data driven networking (bDDN), the high-level capabilities of bDDN, the interface capabilities among different planes and layers.
Recommendation ITU-T F.743.7 specifies requirements for visual surveillance enhanced by big data (VSBD) services. It promotes the value of visual surveillance services by using big data analytics method and tools. Massive video, event and sensing data are analysed to support enhanced visual surveillance services, including video retrieval, event detection and status prediction.
The ITU-T Focus Group on Machine Learning for Future Networks including 5G was established by ITU-T Study Group 13 at its meeting in Geneva, 6-17 November 2017. The Focus Group will draft technical reports and specifications for machine learning (ML) for future networks, including interfaces, network architectures, protocols, algorithms and data formats.
The ITU/WHO Focus Group on artificial intelligence for health (FG-AI4H) works in partnership with the World Health Organization (WHO) to establish a standardized assessment framework for the evaluation of AI-based methods for health, diagnosis, triage or treatment decisions. Participation in the FG-AI4H is free of charge and open to all. The group was established by ITU-T Study Group 16 at its meeting in Ljubljana, Slovenia, 9-20 July 2018.
Recommendation ITU-T M.3071 introduces a new network management functional architecture with cloud-computing technology. In this Recommendation, the background and basic concept of cloud-based network management are provided. This Recommendation also provides details of a cloud-based network management functional architecture, including its basic components, functionalities and the relationship between its components.
Recommendation ITU‑T Y.3602 describes a model and operations for big data provenance. Also, this Recommendation provides the functional requirements for big data service provider (BDSP) to manage big data provenance. The reliability of data is an important factor in determining the reliability of the analysis result. Data provenance aims to ensure the reliability of data by providing transparency of the historical path of the data. In a big data environment, complex data processing and migration due to the big data lifecycle and data distribution cause various difficulties in managing data provenance.
This recommendation provides end-to-end fault and performance management framework of virtual network services (VNSs) in inter-cloud computing and relevant use cases. In particular, the aspects of faults detection and localization of affected area in inter-cloud environments is presented.
