ITU-T Y 3600-2015 Big data C Cloud computing based requirements and capabilities (Study Group 13)《大数据-云计算为基础的要求和能力(研究组13)》.pdf

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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.3600 TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU (11/2015) SERIES Y: GLOBAL INFORMATION INFRASTRUCTURE, INTERNET PROTOCOL ASPECTS AND NEXT-GENERATION NETWORKS Cloud Computing Big data Cloud computing based requirement

2、s and capabilities Recommendation ITU-T Y.3600 ITU-T Y-SERIES RECOMMENDATIONS GLOBAL INFORMATION INFRASTRUCTURE, INTERNET PROTOCOL ASPECTS AND NEXT-GENERATION NETWORKS GLOBAL INFORMATION INFRASTRUCTURE General Y.100Y.199 Services, applications and middleware Y.200Y.299 Network aspects Y.300Y.399 Int

3、erfaces 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.1100Y.1199 Architecture, access, network capabiliti

4、es 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 over NGN Y.1900Y.1999 NEXT GENERATION NETWORKS Fr

5、ameworks 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.2250Y.2299 Enhancements to NGN Y.2300Y.2399 Network

6、 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 COMPUTING Y.3500Y.3999 For further details, plea

7、se refer to the list of ITU-T Recommendations. Rec. ITU-T Y.3600 (11/2015) i Recommendation ITU-T Y.3600 Big data Cloud computing based requirements and capabilities Summary Recommendation ITU-T Y.3600 provides requirements, capabilities and use cases of cloud computing based big data as well as its

8、 system context. Cloud computing based big data provides the capabilities to collect, store, analyse, visualize and manage varieties of large volume datasets, which cannot be rapidly transferred and analysed using traditional technologies. History Edition Recommendation Approval Study Group Unique I

9、D* 1.0 ITU-T Y.3600 2015-11-06 13 11.1002/1000/12584 Keywords Big data, big data ecosystem, cloud computing, data analytics, data storage, real-time analysis. * To access the Recommendation, type the URL http:/handle.itu.int/ in the address field of your web browser, followed by the Recommendations

10、unique ID. For example, http:/handle.itu.int/11.1002/1000/11830-en. ii Rec. ITU-T Y.3600 (11/2015) FOREWORD The International Telecommunication Union (ITU) is the United Nations specialized agency in the field of telecommunications, information and communication technologies (ICTs). The ITU Telecomm

11、unication 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 basis. The World Telecommunication Standardization Asse

12、mbly (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 Resolution 1. In some areas of information technology whi

13、ch 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 and a recognized operating agency. Compliance with t

14、his 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 met. The words “shall“ or some other obligatory lan

15、guage 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 possibility that the practice or implementation of this

16、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 Recommendation development process. As of the date o

17、f approval of this Recommendation, ITU had 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 information and are therefore strongly urged to consult the TS

18、B patent database at http:/www.itu.int/ITU-T/ipr/. ITU 2015 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.3600 (11/2015) iii Table of Contents Page 1 Scope . 1 2 References . 1 3 Definitions 1 3.

19、1 Terms defined elsewhere 1 3.2 Terms defined in this Recommendation . 2 4 Abbreviations and acronyms 2 5 Conventions 3 6 Overview of big data 3 6.1 Introduction to big data 3 6.2 Big data ecosystem . 4 6.3 Relationship between cloud computing and big data . 6 7 Cloud computing based big data . 6 7.

20、1 Cloud computing based big data system context . 6 7.2 Benefits of cloud computing based big data . 10 8 Requirements of cloud computing based big data 11 8.1 Data collection requirements 11 8.2 Data pre-processing requirements 11 8.3 Data storage requirements 12 8.4 Data analysis requirements . 12

21、 8.5 Data visualization requirements . 13 8.6 Data management requirements . 13 8.7 Data security and protection requirements . 14 9 Cloud computing based big data capabilities . 14 9.1 Data collection capabilities . 14 9.2 Data pre-processing capabilities . 14 9.3 Data storage capabilities . 14 9.4

22、 Data analytics capabilities 15 9.5 Data visualization capabilities 15 9.6 Data management capabilities 15 9.7 Data security and protection capabilities 16 10 Security considerations . 16 Appendix I Use cases of cloud computing in support of big data 17 Appendix II Use cases of cloud computing based

23、 big data as analysis services 26 Appendix III Mapping of big data ecosystem roles into user view of ITU-T Y.3502 29 Bibliography. 30 Rec. ITU-T Y.3600 (11/2015) 1 Recommendation ITU-T Y.3600 Big data Cloud computing based requirements and capabilities 1 Scope This Recommendation provides an approac

24、h to use cloud computing to meet existing challenges in the use of big data. This Recommendation addresses the following subjects: Overview of big data; Introduction to big data; Big data ecosystem and roles; Relationship between cloud computing and big data; Cloud computing based big data system co

25、ntext and benefits; Cloud computing based big data requirements; Cloud computing based big data capabilities. Note that use cases of cloud computing based big data are provided in Appendix I and II. 2 References The following ITU-T Recommendations and other references contain provisions which, throu

26、gh reference in this text, constitute provisions of this Recommendation. At the time of publication, the editions indicated were valid. All Recommendations and other references are subject to revision; users of this Recommendation are therefore encouraged to investigate the possibility of applying t

27、he most recent edition of the Recommendations and other references listed below. A list of the currently valid ITU-T Recommendations 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.160

28、1 Recommendation ITU-T X.1601 (2015), Security framework for cloud computing. ITU-T Y.3500 Recommendation ITU-T Y.3500 (2014) | ISO/IEC 17788:2014, Information technology Cloud computing Overview and vocabulary. ITU-T Y.3502 Recommendation ITU-T Y.3502 (2014) | ISO/IEC 17789:2014, Information techno

29、logy Cloud computing Reference architecture. 3 Definitions 3.1 Terms defined elsewhere This Recommendation uses the following terms defined elsewhere: 3.1.1 activity ITU-T Y.3502: A specified pursuit or set of tasks. 3.1.2 cloud computing ITU-T Y.3500: Paradigm for enabling network access to a scala

30、ble and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on-demand. NOTE Examples of resources include servers, operating systems, networks, software, applications and storage equipment. 3.1.3 cloud service customer ITU-T Y.3500: Party which i

31、s in a business relationship for the purpose of using cloud services. NOTE A business relationship does not necessarily imply financial agreements. 2 Rec. ITU-T Y.3600 (11/2015) 3.1.4 cloud service partner ITU-T Y.3500: Party which is engaged in support of, or auxiliary to, activities of either the

32、cloud service provider or the cloud service customer, or both. 3.1.5 cloud service provider ITU-T Y.3500: Party which makes cloud services available. 3.1.6 metadata b-ITU-T M.3030: Data that describes other data. 3.1.7 role ITU-T Y.3502: A set of activities that serves a common purpose. 3.1.8 sub-ro

33、le ITU-T Y.3502: A subset of the activities of a given role. 3.2 Terms defined in this Recommendation This Recommendation defines the following terms: 3.2.1 big data: A paradigm for enabling the collection, storage, management, analysis and visualization, potentially under real-time constraints, of

34、extensive datasets with heterogeneous characteristics. NOTE Examples of datasets characteristics include high-volume, high-velocity, high-variety, etc. 3.2.2 big data as a service (BDaaS): A cloud service category in which the capabilities provided to the cloud service customer are the ability to co

35、llect, store, analyse, visualize and manage data using big data. 3.2.3 party: Natural person or legal person, whether or not incorporated, or a group of either. 4 Abbreviations and acronyms This Recommendation uses the following abbreviations and acronyms: API Application Programming Interface BDaaS

36、 Big Data as a Service BDAP Big Data Application Provider BDC Big Data service Customer BDIP Big Data Infrastructure Provider BDSP Big Data Service Provider BDSU Big Data Service User CCRA Cloud Computing Reference Architecture CDR Charging Detailed Record CGF Charging Gateway Function CSC Cloud Ser

37、vice Customer CSN Cloud Service partner CSP Cloud Service Provider DP Data Provider DPI Deep Packet Inspection HTML Hyper Text Mark-up Language IaaS Infrastructure as a Service IoT Internet of Things PDA Personal Digital Assistant Rec. ITU-T Y.3600 (11/2015) 3 PDSN Packet Data Serving Node SNS Socia

38、l Network Service SQL Structured Query Language XML Extensible Markup Language 5 Conventions The keywords “is required to“ indicate a requirement which must be strictly followed and from which no deviation is permitted if conformance to this document is to be claimed. The keywords “is recommended“ i

39、ndicate a requirement which is recommended but which is not absolutely required. Thus this requirement need not be present to claim conformance. The keywords “can optionally“ indicate an optional requirement which is permissible, without implying any sense of being recommended. This term is not inte

40、nded to imply that the vendors implementation must provide the option and the feature can be optionally enabled by the network operator/service provider. Rather, it means the vendor may optionally provide the feature and still claim conformance with the specification. In the body of this document an

41、d its annexes, 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 optionally. The appearance of such phrases or keywords in an appendix or in material explicitly marked a

42、s informative are to be interpreted as having no normative intent. 6 Overview of big data 6.1 Introduction to big data With the rapid development of information and communications technology (ICT), Internet technologies and services, huge amounts of data are generated, transmitted and stored at an e

43、xplosive rate of growth. Data are generated by many sources and not only by sensors, cameras or network devices, but also by web pages, email systems and social networks as well as by many other sources. Datasets are becoming so large and so complex or are arriving so fast that traditional data proc

44、essing methods and tools are inadequate. Efficient analytics of data within tolerable elapsed times becomes very challenging. The paradigm being developed to resolve the above issues is called big data. For the purpose of this Recommendation it is understood, that within the big data ecosystem, data

45、 types include structured, semi-structured and unstructured data. Structured data are often stored in databases which may be organized in different models, such as relational models, document models, key-value models, graph models, etc. Semi-structured data does not conform to the formal structure o

46、f data models, but contain tags or markers to identify data. Unstructured data do not have a pre-defined data model and are not organized in any defined manner. Within all data types data can exist in formats, such as text, spreadsheet, video, audio, image, map, etc. Big data are successfully used i

47、n many fields, if traditional methods and tools have become inefficient, where data processing is characterized by scale (volume), diversity (variety), high speed (velocity) and possibly other criteria like credibility (veracity) or business value. These characteristics, usually called the Vs, can b

48、e explained as follows: Volume: refers to the amount of data collected, stored, analysed and visualized, which big data technologies need to resolve; Variety: refers to different data types and data formats that are processed by big data technologies; 4 Rec. ITU-T Y.3600 (11/2015) Velocity: refers t

49、o both how fast the data is being collected and how fast the data is processed by big data technologies to deliver expected results. NOTE Additionally, veracity refers to the uncertainty of the data and value refers to the business results from the gains in new information using big data technologies. Other Vs can be considered as well. Taking into account the above Vs described characteristics, big data technologies and services allow many new challenges to be reso