Recommendation ITU-T Y.3606 (12/2021) Big data – Deep packet inspection mechanism for big data in network
Summary
History
FOREWORD
Table of Contents
1 Scope
2 References
3 Definitions
     3.1 Terms defined elsewhere
     3.2 Terms defined in this Recommendation
4 Abbreviations and acronyms
5 Conventions
6 Introduction of differences between generic DPI and big data DPI
     6.1 Difference between generic DPI and big data DPI for the velocity feature
     6.2 Difference between generic DPI and big data DPI for the volume feature
     6.3 Difference between general DPI and big data DPI for the variety feature
7 Overview of big data processing procedure
     7.1 An example model for big data processing
     7.2 Big data processing model in ITU context
8 Connection between deep packet inspection and big data process
     8.1 Summary of relationship between DPI and big data processing model
     8.2 DPI carries out the data collecting functions for big data
     8.3 Big data process carries out partial functions of R-PDF
9 Data classification mechanism for big data DPI
     9.1 Basic mechanism of information representation
          9.1.1 General information representation method
          9.1.2 Deterministic finite automation and non-deterministic finite automation
          9.1.3 Hybrid and distributed storage method
     9.2 Basic data classification mechanism used deep packet inspection
          9.2.1 Distributed processing mode
          9.2.2 Cascaded processing mode
     9.3 Data classification mechanism for structured data used DPI
          9.3.1 Data classification mechanism for protocol-known structured data
          9.3.2 Data classification mechanism for protocol-unknown structured data
     9.4 Data classification mechanism for non-structured data used DPI
          9.4.1 Multi-stage mechanism
          9.4.2 Packet-aggregate mechanism
          9.4.3 Decompression mechanism
          9.4.4 Classification mechanism based on a Bloom filter
          9.4.5 Classification mechanism based on regular expression
     9.5 Data classification mechanism for semi-structured data used DPI
     9.6 Energy efficient mechanisms for classification function
10 Data pre-processing mechanism used big data DPI
     10.1 Extension of DPI-engine
          10.1.1 DPI pre-extraction function and DPI pre-transformation function
          10.1.2 Extension of the inner path of a DPI engine
          10.1.3 DPI meta-engine
          10.1.4 DPI multiple meta-engine
          10.1.5 General model for an extended DPI engine
          10.1.6 DPI entity based on a DPI meta-engine
          10.1.7 Mechanism to implement DPI functions based on DPI meta-engine
     10.2 DPI application mode based on DPI meta-engine
          10.2.1  Single DPI functional entity and multiple DPI meta-engines
          10.2.2  Multiple DPI functional entity and multiple DPI meta-engine
     10.3 Data pre-processing mechanism for structured data using DPI
     10.4 Data pre-processing mechanism for non-structured data by DPI
11 Coordination processing mechanism of big -data DPI
     11.1 General coordination processing mechanism
     11.2 Coordination processing mechanism for multiple DPI engines
          11.2.1  General structure of multiple DPI engines within a DPI entity
          11.2.2 Coordination process mechanism for multiple DPI engines within a DPI entity
     11.3 Coordination processing mechanism for multiple DPI nodes
12 Interfaces between deep packet inspection and the upper-layer big data-related method
     12.1 Downstream interface
     12.2 Upstream interface
13 Other aspects of the DPI mechanism for big data in networks
     13.1 Manageability
     13.2 Applicability
     13.3 Availability
14 Performance consideration
15 Security considerations
Bibliography
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