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| -rw-r--r-- | docs/docs_intent_based_network.rst | 166 |
1 files changed, 88 insertions, 78 deletions
diff --git a/docs/docs_intent_based_network.rst b/docs/docs_intent_based_network.rst index 760cdb1d..b29a9f04 100644 --- a/docs/docs_intent_based_network.rst +++ b/docs/docs_intent_based_network.rst @@ -1,79 +1,89 @@ -.. contents:: - :depth: 3 -.. -.. _docs_intent_based_network: - - -Intent Based Network -============================= - -Overall Blueprint ------------------ -Intent-based network (IBN) is a self-driving network that uses decoupling -network control logic and closed-loop orchestration techniques to automate -application intents. An IBN is an intelligent network, which can automatically -convert, verify, deploy, configure, and optimize itself to achieve target -network state according to the intent of the operators, and can automatically -solve abnormal events to ensure the network reliability. - -REQ-453 Smart Operator Intent Translation in UUI based on IBN - R8 5G Slicing Support -In R8, the smart operator intent translation function is proposed to support -the 5G slicing selection of current E2E usecase in UUI. -The target architecture of the Intent-Based Network is divided into a Intent -orchestration layer (hereinafter referred to as the Intent layer), a control -layer and a network layer. - - -Abbreviations -------------- - -+---------------+--------------------------------------------+ -| Abbreviation | Meaning | -+===============+============================================+ -| IBN | Intent Based Network | -+---------------+--------------------------------------------+ - - - -Scope of Honolulu release +.. contents::
+ :depth: 3
+..
+.. _docs_intent_based_network:
+
+
+Intent Based Network
+=============================
+
+Overall Blueprint
+-----------------
+Intent-based network (IBN) is a self-driving network that uses decoupling
+network control logic and closed-loop orchestration techniques to automate
+application intents. An IBN is an intelligent network, which can automatically
+convert, verify, deploy, configure, and optimize itself to achieve target
+network state according to the intent of the operators, and can automatically
+solve abnormal events to ensure the network reliability.
+
+REQ-453 Smart Operator Intent Translation in UUI based on IBN - R8 5G Slicing Support
+In R8, the smart operator intent translation function is proposed to support
+the 5G slicing selection of current E2E usecase in UUI.
+The target architecture of the Intent-Based Network is divided into a Intent
+orchestration layer (hereinafter referred to as the Intent layer), a control
+layer and a network layer.
+
+
+Abbreviations
+-------------
+
++---------------+--------------------------------------------+
+| Abbreviation | Meaning |
++===============+============================================+
+| IBN | Intent Based Network |
++---------------+--------------------------------------------+
+
+
+
+Scope of Honolulu release
+-----------------------
+The scope for Honolulu developed in UUI includes GUI, UUI-server, and NLP.
+
+GUI
+- Services
+- 5G Slicing Management
+- Package Management
+- NLP Model resource
+
+Server
+- Intent Management Module
+
+NLP Server
+(new Micro-service)
+Three NLP algorithms are considered to be applied in current solutions:
+- BERT (Bidirectional Encoder Representations from Transformers)：developed by researchers at Google AI Language. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks. BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling.
+
+Scope of Istanbul release
----------------------- -The scope for Honolulu developed in UUI includes GUI, UUI-server, and NLP. - -GUI -- Services -- 5G Slicing Management -- Package Management -- NLP Model resource - -Server -- Intent Management Module - -NLP Server -(new Micro-service) -Three NLP algorithms are considered to be applied in current solutions: -- BERT (Bidirectional Encoder Representations from Transformers)：developed by researchers at Google AI Language. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks. BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling. - - - -Impacted Modules for Honolulu ---------------------------- - -U-UI -~~~~ -Target of R8: translate from the human inputs to the slice parameters based on NLP -in UUI, and then run the slices based on the current ONAP. - -A new page is required in the UUI that users can enter network requirements through -the natural language, which then sends the user input to the IBN component and displays -the response information to the user. This process can be repeated several times -until the dialog completes and a new Intent is formed in the IBN component. - - - -Functional Test Cases ---------------------- - - - -Operation Guidance ------------------- +The scope for Istanbul developed in AAI focuses on Intent Instance Management + +The storage and management of AAI-based Intent Instance are realized in the R9 version. +Intent Instance is created to save the users' real-time intent (network parameters) and connected service ID (CCNVPN service ID / E2E Slicing customer service intent ID) in AAI. + +The user's intent is stored from the UUI or SO to the intent instance in the AAI, and then DCAE calls the intent instance in the AAI through the interface.
+
+Impacted Modules for Honolulu
+---------------------------
+
+U-UI
+~~~~
+Target of R8: translate from the human inputs to the slice parameters based on NLP
+in UUI, and then run the slices based on the current ONAP.
+
+A new page is required in the UUI that users can enter network requirements through
+the natural language, which then sends the user input to the IBN component and displays
+the response information to the user. This process can be repeated several times
+until the dialog completes and a new Intent is formed in the IBN component.
+
+Target of R9: support multiple usecase services, so it is not a sub-node of any usecase in AAI. + +The IBN will be expect to provide unawares service to users. Multiple usecases services could be changed by IBN instead of the users, so it should be an independent node in AAI. +
+
+Functional Test Cases
+---------------------
+
+
+
+Operation Guidance
+------------------
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