ITU-T Y 3650-2018 Framework of big-data-driven networking (Study Group 13).pdf

1、 I n t e r n a t i o n a l T e l e c o m m u n i c a t i o n U n i o n ITU-T Y.3650 TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU (01/2018) SERIES Y: GLOBAL INFORMATION INFRASTRUCTURE, INTERNET PROTOCOL ASPECTS, NEXT-GENERATION NETWORKS, INTERNET OF THINGS AND SMART CITIES Cloud Computing Framewor

2、k of big-data-driven networking Recommendation ITU-T Y.3650 ITU-T Y-SERIES RECOMMENDATIONS GLOBAL INFORMATION INFRASTRUCTURE, INTERNET PROTOCOL ASPECTS, NEXT-GENERATION NETWORKS, INTERNET OF THINGS AND SMART CITIES GLOBAL INFORMATION INFRASTRUCTURE General Y.100Y.199 Services, applications and middl

3、eware Y.200Y.299 Network aspects Y.300Y.399 Interfaces and protocols Y.400Y.499 Numbering, addressing and naming Y.500Y.599 Operation, administration and maintenance Y.600Y.699 Security Y.700Y.799 Performances Y.800Y.899 INTERNET PROTOCOL ASPECTS General Y.1000Y.1099 Services and applications Y.1100

4、Y.1199 Architecture, access, network capabilities and resource management Y.1200Y.1299 Transport Y.1300Y.1399 Interworking Y.1400Y.1499 Quality of service and network performance Y.1500Y.1599 Signalling Y.1600Y.1699 Operation, administration and maintenance Y.1700Y.1799 Charging Y.1800Y.1899 IPTV ov

5、er NGN Y.1900Y.1999 NEXT GENERATION NETWORKS Frameworks and functional architecture models Y.2000Y.2099 Quality of Service and performance Y.2100Y.2199 Service aspects: Service capabilities and service architecture Y.2200Y.2249 Service aspects: Interoperability of services and networks in NGN Y.2250

6、Y.2299 Enhancements to NGN Y.2300Y.2399 Network management Y.2400Y.2499 Network control architectures and protocols Y.2500Y.2599 Packet-based Networks Y.2600Y.2699 Security Y.2700Y.2799 Generalized mobility Y.2800Y.2899 Carrier grade open environment Y.2900Y.2999 FUTURE NETWORKS Y.3000Y.3499 CLOUD C

7、OMPUTING Y.3500Y.3999 INTERNET OF THINGS AND SMART CITIES AND COMMUNITIES General Y.4000Y.4049 Definitions and terminologies Y.4050Y.4099 Requirements and use cases Y.4100Y.4249 Infrastructure, connectivity and networks Y.4250Y.4399 Frameworks, architectures and protocols Y.4400Y.4549 Services, appl

8、ications, computation and data processing Y.4550Y.4699 Management, control and performance Y.4700Y.4799 Identification and security Y.4800Y.4899 Evaluation and assessment Y.4900Y.4999 For further details, please refer to the list of ITU-T Recommendations. Rec. ITU-T Y.3650 (01/2018) i Recommendation

9、 ITU-T Y.3650 Framework of big-data-driven networking Summary Recommendation ITU-T Y.3650 specifies a framework for 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 and the interface

10、 capabilities among different planes and layers. History Edition Recommendation Approval Study Group Unique ID* 1.0 ITU-T Y.3650 2018-01-13 13 11.1002/1000/13470 Keywords Big-data-driven networking, framework. * To access the Recommendation, type the URL http:/handle.itu.int/ in the address field of

11、 your web browser, followed by the Recommendations unique ID. For example, http:/handle.itu.int/11.1002/1000/11830-en. ii Rec. ITU-T Y.3650 (01/2018) FOREWORD The International Telecommunication Union (ITU) is the United Nations specialized agency in the field of telecommunications, information and

12、communication technologies (ICTs). The ITU Telecommunication Standardization Sector (ITU-T) is a permanent organ of ITU. ITU-T is responsible for studying technical, operating and tariff questions and issuing Recommendations on them with a view to standardizing telecommunications on a worldwide basi

13、s. The World Telecommunication Standardization Assembly (WTSA), which meets every four years, establishes the topics for study by the ITU-T study groups which, in turn, produce Recommendations on these topics. The approval of ITU-T Recommendations is covered by the procedure laid down in WTSA Resolu

14、tion 1. In some areas of information technology which fall within ITU-Ts purview, the necessary standards are prepared on a collaborative basis with ISO and IEC. NOTE In this Recommendation, the expression “Administration“ is used for conciseness to indicate both a telecommunication administration a

15、nd a recognized operating agency. Compliance with this Recommendation is voluntary. However, the Recommendation may contain certain mandatory provisions (to ensure, e.g., interoperability or applicability) and compliance with the Recommendation is achieved when all of these mandatory provisions are

16、met. The words “shall“ or some other obligatory language such as “must“ and the negative equivalents are used to express requirements. The use of such words does not suggest that compliance with the Recommendation is required of any party. INTELLECTUAL PROPERTY RIGHTSITU draws attention to the possi

17、bility that the practice or implementation of this Recommendation may involve the use of a claimed Intellectual Property Right. ITU takes no position concerning the evidence, validity or applicability of claimed Intellectual Property Rights, whether asserted by ITU members or others outside of the R

18、ecommendation development process. As of the date of approval of this Recommendation, ITU had not received notice of intellectual property, protected by patents, which may be required to implement this Recommendation. However, implementers are cautioned that this may not represent the latest informa

19、tion and are therefore strongly urged to consult the TSB patent database at http:/www.itu.int/ITU-T/ipr/. ITU 2018 All rights reserved. No part of this publication may be reproduced, by any means whatsoever, without the prior written permission of ITU. Rec. ITU-T Y.3650 (01/2018) iii Table of Conten

20、ts Page 1 Scope . 1 2 References . 1 3 Definitions 1 3.1 Terms defined elsewhere 1 3.2 Terms defined in this Recommendation . 1 4 Abbreviations and acronyms 2 5 Conventions 2 6 Introduction . 2 7 Overview of big-data-driven networking . 3 8 The reference model of bDDN . 4 9 The model architecture of

21、 big-data-driven networking 5 10 The high-level capabilities of big-data-driven networking 7 10.1 High-level capabilities of big data plane 7 10.2 High-level capabilities of network plane 9 10.3 High-level capabilities of management plane 10 11 The interface capabilities of big-data-driven networkin

22、g . 10 11.1 The interfaces among planes of big-data-driven networking . 10 11.2 The interfaces among layers of big-data-driven networking 11 11.3 The interfaces between the bDDN control domains . 12 12 Security considerations . 12 Appendix I The concept of close-loop bDDN-ADN-NI . 13 Rec. ITU-T Y.36

23、50 (01/2018) 1 Recommendation ITU-T Y.3650 Framework of big-data-driven networking 1 Scope This Recommendation specifies a framework for big-data-driven networking. The scope of this Recommendation includes the model architecture of big-data-driven networking (bDDN), the high-level capabilities of b

24、DDN and the interface capabilities among different planes and layers. 2 References The following ITU-T Recommendations and other references contain provisions which, through reference in this text, constitute provisions of this Recommendation. At the time of publication, the editions indicated were

25、valid. All Recommendations and other references are subject to revision; users of this Recommendation are therefore encouraged to investigate the possibility of applying the most recent edition of the Recommendations and other references listed below. A list of the currently valid ITU-T Recommendati

26、ons is regularly published. The reference to a document within this Recommendation does not give it, as a stand-alone document, the status of a Recommendation. ITU-T X.200 Recommendation ITU-T X.200 (1994) | ISO/IEC 7498-1:1994, Information technology Open Systems Interconnection Basic Reference Mod

27、el: The basic model. ITU-T Y.2770 Recommendation ITU-T Y.2770 (2012), Requirements for deep packet inspection in next generation networks. ITU-T Y.3600 Recommendation ITU-T Y.3600 (2015), Big data Cloud computing based requirements and capabilities. 3 Definitions 3.1 Terms defined elsewhere This Rec

28、ommendation uses the following terms defined elsewhere: 3.1.1 big data ITU-T Y.3600: A paradigm for enabling the collection, storage, management, analysis and visualization, potentially under real-time constraints, of extensive datasets with heterogeneous characteristics. NOTE Examples of datasets c

29、haracteristics include high-volume, high-velocity, high-variety, etc. 3.1.2 deep packet inspection (DPI) ITU-T Y.2770: Analysis, according to the layered protocol architecture OSI-BRM ITU-T X.200, of: payload and/or packet properties (see list of potential properties in clause 3.2.11 of ITU-T Y.2770

30、); deeper than protocol layer 2, 3 or 4 (L2/L3/L4) header information, and other packet properties in order to identify the application unambiguously. NOTE The output of the DPI function, along with some extra information such as the flow information, is typically used in subsequent functions such a

31、s reporting or actions on the packet. 3.2 Terms defined in this Recommendation This Recommendation defines the following terms: 2 Rec. ITU-T Y.3650 (01/2018) 3.2.1 application-driven networking (ADN): ADN is a type of future network framework that provides the network programmability for the applica

32、tions. ADN is application quality of experience (QOE)-centric while a traditional network is network efficiency-centric. 3.2.2 big-data-driven networking (bDDN): Big-data-driven networking (bDDN) is a type of future network framework that collects big data from networks and applications, and generat

33、es big data intelligence based on the big data; it then provides big data intelligence to facilitate smarter and autonomous network management, operation, control, optimization and security, etc. 3.2.3 big-data plane: The big-data plane is the main part of bDDN. It is responsible for network big dat

34、a collection, storage and computation, and has powerful big data computing and analytical ability. It extracts the useful information and intelligence from the networks big data. Then it provides network intelligence for network management, operation, control, optimization and security, etc. 4 Abbre

35、viations and acronyms This Recommendation uses the following abbreviations and acronyms: ADN Application-Driven Networking bDDN big-Data-Driven Networking CAPEX Capital Expenditure DPI Deep Packet Inspection LB Load Balance NI Network Infrastructure OAM Operation, Administration and Maintenance OPEX

36、 Operating Expense PDU Protocol Data Unit QoE Quality of Experience QoS Quality of Service SDN Software Defined Network 5 Conventions This Recommendation uses the following conventions: The term “is required to“ indicates a requirement which must be strictly followed and from which no deviation is p

37、ermitted, if conformance to this Recommendation is to be claimed. In the body of this Recommendation and its appendices, the words shall, shall not, should and may sometimes appear, in which case they are to be interpreted, respectively as, is required to, is prohibited from, is recommended, and can

38、 optionally. The appearance of such phrases or keywords in an appendix or in material explicitly marked as informative are to be interpreted as having no normative intent. 6 Introduction As the Internet becomes ubiquitous in its role as a social infrastructure, various Internet applications have eme

39、rged, and the complexity of traffic carried over the telecommunication networks continues to increase. We can obtain large amounts of traffic data based on deep packet inspection and operation data from the telecommunication network management or operation entity, and how to use this data to obtain

40、useful information, thus improving the traffic and network management process is the problem that the operators need to consider. Big data technology and machine learning are important Rec. ITU-T Y.3650 (01/2018) 3 technical trends in the industry. The big data technology will be effective in handli

41、ng traffic and network complexities, and big-data-driven networking will propose to study the applying of big data analytics technologies and machine learning for future networks. 7 Overview of big-data-driven networking The big data generated by networks themselves implies a great deal of useful in

42、formation for network management, operation, control, optimization and security, etc. Such valuable and such a tremendous amount of information, unfortunately, cannot be efficiently utilized by traditional network architecture. Big-data-driven networking (bDDN) solves this problem by make use of big

43、 data generated by the network itself. The bDDN separates complex data computing and processing functionalities from the network control plane and management plane, and converges it into a big data plane. This big data plane has powerful big data computing and analytical ability, can perform pervasi

44、ve and inclusive network data collection and computation, and extract useful information and intelligence from the big data. By applying big data technologies to massive data in the future network, the bDDN provides computational data intelligence support for network management, operation, control,

45、optimization and security, etc. Furthermore, the bDDN would decrease the complexity of the network plane and management plane, which in return, would facilitate smart management, improve user QoE, elastic expansion and easy adaptation to emerging business requirements in the future network. Figure 7

46、-1 The framework of big-data-driven networking (bDDN) As shown in Figure 7-1, the bDDN framework is made up of three planes big data plane, management plane and network plane. The bDDN model differs from existing network models in two major ways: 1) The bDDN framework is tridimensional, unlike the t

47、raditional vertical layered model which focuses on a common process of network traffic. As time goes by, challenges are brought forward mostly by network measurement and management issues. However, they are not clearly and independently illustrated in the traditional vertical layered model. As a res

48、ult, solving network problems in the traditional framework becomes increasingly obscure and cumbersome. The bDDN model clarifies the three major facets of future networks, as well as their relationships. 2) The new model introduces the big data plane to support the management plane and network plane

49、. 4 Rec. ITU-T Y.3650 (01/2018) The big data plane is introduced because we found neither the SDN architecture nor the traditional network framework could handle big data challenges properly. The SDN centralizes the problems described earlier and transfers problems to the network management and control process. The burden is centralized and must be predigested there. Facing “network inflation“ in future networks, the complexity of network management would increase and problems during the control proces