Intelligence Defined Network (IDN)

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1 Intelligence Defined (IDN) Proposed NMLRG IETF 97, November 17 th, 2016 Sheng Jiang (Speaker, Co-chair) Page 1/10

2 Intelligence (ML) Defined - Towards Full Autonomic Mannually Defined Basic approach Step 1: network planning Step 2: CLI configuration Step 3: network optimization, diagnostic, debug Characteristics Fully depend on human s experience and knowledge High cost throughput totally decided by human plan. It is rigid afterwards Software Defined Basic approach South bound: using Netconf/YANG, or OpenFlow to configure the network forwarding table North bound: abstract description for the application requirements, thus deriving the forwarding table Characteristics Semi-autonomic Cost reduced throughput increased Intelligence Defined/Driven Basic approach Machine learning: network traffic patterns and application patterns Finding a model to match the learnt patterns/experiences Re-define and drive the network by machine learning algorithms and technologies. Characteristics Full-autonomic Minimum cost Maximum throughput Page 2/10

3 A Reference Model for the IDN API for Net AI APPs Backend Customer Operator Learning Models AI Algorithms Policy Auto Decision Intelligence Policy & Data Interface Policy (AI Result) Distribution Data Collection (Measurement & States) SDN ler N FrontEnd SDN ler (Policy Execution) Metro Backbone Internet Page 3/10

4 IDN Re-defines ling & Management Restructure the network towards a better ML-based autonomic controlling & management It is network research rather than machine learning research The rollout could be incremental from the SDN-based network There will be many new requirements for networks towards IDN, some of them needs new standards (out of NMLRG scope) Page 4/10

5 Does s & Devices Ready to be Managed by ML The availability of network data Packet size, latency, path (measurement) Awareness of flow and their attributes, awareness and coordination of contents/applications Unified format for all data? The quality of data (sampling vs. representability) complexity, the coordination of multiple tasks and multiple perspective Data selection & minimizing the transported data vs. the integrality (unknown knowledge) Quantity of data (sufficient? Data storage?) Page 5/10

6 Does s & Devices Ready to be led by ML Applying the result of ML into network controlling Executable, policy description Scalability Trustable (black box issue) Real time requirement How difficult for network experts to create a intelligence task How to combine the network expert knowledge with machine-based intelligence Page 6/10

7 Functions of IDN Back End - Make it easy for network experts to create tasks Admin Task APP 1 Task APP 2 Task APP 3 Clients Intelligence Task Description API Task Modeling AI Algorithms 3rd-party Algorithms policy task management (Training, decision) Front End SDN ler Policy Agent Service Deployment Data preprocessing Intelligence Policy & Data Interface (IPDI) AI Platform Training data set Back End SDN Managed Routers Page 7/10

8 Examples: network experts to create Intelligence Tasks The network experts create intelligence tasks in a lightweight way with only minimum ML knowledge Traffic prediction Describe the task (to predict [10, 30, 120 seconds after] traffics or bandwidth requirements on [selected] key nodes Describe the relevant data [traffic changing in last hour, traffic changing yesterday the same time, traffic changing last week the same time] and potential relationship or any human observed result Traffic management Describe the trigger conditions for traffic re-routing. It could based on traffic predition Describe the traffic management target [low latency, balance, etc.] Description in a well-defined description language Page 8

9 Potential Standardization Works API/Language for intelligence network task description Backend Customer Operator Unified data & collection Learning Models AI Algorithms Policy Auto Decision Measurement Data Selection Intelligence Policy & Data Interface Policy (AI Result) Distribution Policy description language & distribution protocol Data Collection (Measurement & States) SDN ler N FrontEnd SDN ler (Policy Execution) Metro Backbone Internet Page 9/10

10 Let s have good discussion today and in the future! Thank You! jiangsheng@huawei.com Page 10/10

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