Network Working Group Z. Li Internet-Draft Huawei Technologies Intended status: Informational Y. Zheng Expires: September 14, 2017 China Unicom J. Zhang S. Xu Huawei Technologies March 13, 2017 An Architecture of Network Artificial Intelligence(NAI) draft-li-opsawg-network-ai-arch-00 Abstract Artificial intelligence is an important technical trend in the industry. With the development of network, it is necessary to introduce artificial intelligence technology to achieve self- adjustment, self- optimization, self-recovery of the network through collection of huge data of network state and machine learning. This draft defines the architecture of Network Artificial Intelligence (NAI), including the key components and the key protocol extension requirements. Requirements Language The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described inRFC 2119 [RFC2119] Status of This Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at http://datatracker.ietf.org/drafts/current/. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." This Internet-Draft will expire on September 14, 2017. Li, et al. Expires September 14, 2017 [Page 1] Internet-Draft An Architecture of NAI March 2017 Copyright Notice Copyright (c) 2017 IETF Trust and the persons identified as the document authors. All rights reserved. This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (http://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. Table of Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3 3. Architecture . . . . . . . . . . . . . . . . . . . . . . . . 3 4. Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 5. Classification . . . . . . . . . . . . . . . . . . . . . . . 5 6. Requirement of Protocol Extensions . . . . . . . . . . . . . 5 6.1. Requirement of Southbound Protocols . . . . . . . . . . . 5 6.2. Requirement of Data Collection . . . . . . . . . . . . . 6 6.3. Requirement of Devices . . . . . . . . . . . . . . . . . 6 6.4. Requirement of Northbound Interface . . . . . . . . . . . 6 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 6 8. Security Considerations . . . . . . . . . . . . . . . . . . . 7 9. Normative References . . . . . . . . . . . . . . . . . . . . 7 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 7 1. Introduction Artificial Intelligence is an important technical trend in the industry. The two key aspects of Artificial Intelligence are perception and cognition. Artificial Intelligence has evolved from an early non-learning expert system to a learning-capable machine learning era. In recent years, the rapid development of the deep learning branch based on the neural network and the maturity of the big data technology and software distributed architecture make the Artificial Intelligence in many fields (such as transportation, medical treatment, education, etc.) have been applied. With the development of network, it is necessary to introduce artificial intelligence technology to achieve self-adjustment, self- optimization, self-recovery of the network through collection of huge data of network state and machine learning. The areas of machine learning which are easier to be used in the network field may Li, et al. Expires September 14, 2017 [Page 2] Internet-Draft An Architecture of NAI March 2017 include: root cause analysis of network failures, network traffic prediction, traffic adjustment and optimization, security defense, security auditing, etc., to implement network perception and cognition. This draft defines the architecture of Network Artificial Intelligence (NAI), including the key components and the key protocol extension requirements. 2. Terminology AI: Artificial Intelligence NAI: Network Artificial Intelligence 3. Architecture ^ ^ (4)| |(4) +---------------|--------------+ +---------------|--------------+ | Domain 1 | | | | Domain 2 | | +------------+ | | +------------+ | | | Central | | | | Central | | | (1)| Controller |----------------------| Controller |(1) | | | with | | | | with | | | | NTA | | | | NTA | | | +------------+ | | +------------+ | | / \ | | / \ | | (3)/ \ | | / \(3) | | / \ | | / \ | | +--------+ +--------+ | | +--------+ +--------+ | | | | | | | | | | | | | | |Network | ...... |Network | | | |Network | ...... |Network | | | | Device | (2) | Device | | | | Device | (2) | Device | | | | 1 | | N | | | | 1 | | N | | | +--------+ +--------+ | | +--------+ +--------+ | | | | | +------------------------------+ +------------------------------+ Figure 1: An Architecture of Network Artificial Intelligence(NAI) The architecture of Network artificial intelligence includes following key components: (1) Central Controller: Centralized controller is the core part of Network Artificial Intelligence which can be called as 'Network Li, et al. Expires September 14, 2017 [Page 3] Internet-Draft An Architecture of NAI March 2017 Brain'. The Network Telemetry and Analytics (NTA) engines can be introduced acompanying with the central controller. The Network Telemetry and Analytics (NTA) engine inclues data collector, analytics framework, data persistence, and NAI applications. (2) Network Device: IP network operation and maintenance are always a big challenge since the network can only provide limited state information. The network states includes but are not limited to topology, traffic engineering, operation and maintenance information, network failure information and related information to locate the network failure. In order to provide these information, the network must be able to support more OAM mechanisms to acquire more state information and report to the controller. Then the controller can get the complete state information of the network which is the base of Network Artificial Intelligence(NAI). (3) Southbound Protocol and Models of Controller: As network devices provide huge network state information, it proposes a number of new requirements for protocols and models between controllers and network devices. The traditional southbound protocol such as Netconf and SNMP can not meet the performance requirements. It is necessary to introduce some new high-performance protocols to collect network state data. At the same time, the models of network data should be completed. Moreover with the introduction of new OAM mechanisms of network devices, new models of network data should be introduced. (4) Northbound Model of Controller: The goal of the Network Artificial Intelligence is to reduce the technical requirements on the network administrators and release them from the heavy network management, control, maintenance work. The abstract northbound model of the controller for different network services should be simple and easy to be understood. 4. Process NAI consists of following processes: -- Data Collection From the time aspect, data collection can be divided into real-time data collection and non-real-time collection. From the content aspect, data collection can be divided into network information collection (including topology, tunnels, routing, equipment configuration, etc.) and traffic collection (the collection network traffic, network load, device KPI, etc.). -- Data Storage Li, et al. Expires September 14, 2017 [Page 4] Internet-Draft An Architecture of NAI March 2017 Store data collected from network. Many existing big data storage technologies can be used here. -- Data Processing This is preliminary data processing too select effective data and simply analyse data relationship. -- Analyse Analyse engine will provide the data analysis results using machine learning algorithm. -- Closed Loop Control According to the results of intelligent analysis and policy set by user, the centrol controller will implement closed-loop control of the network. 5. Classification NAI can be divided into off-line process and on-line process in accordance to the time aspect of the data collection and analysis. Off-line process refers to process of the existing data, or non-real- time collection data. Although the analysis process will also focus on the relationship between data and time, but it does not require real-time analysis. Off-line process is mainly used for two purposes: (1) training or verification of real-time process design; (2) trouble shooting or reason analysis for events that have already occurred. On-line process is efficient real-time collection, processing and analysis of the data, to operate network monitoring and event forecasting. The main purpose of the on-line process are: (1) network capacity monitoring and precise optimizing; (2) network event prediction and fast trouble shooting; (3) real-time network optimization according to the policy. 6. Requirement of Protocol Extensions 6.1. Requirement of Southbound Protocols REQ 01: The southbound protocol of the controller should be introduced to meet the performance requirements of collecting huge data of network states. Li, et al. Expires September 14, 2017 [Page 5] Internet-Draft An Architecture of NAI March 2017 The soundbound protocol can be based on the extensions of the existing traditional protocols such link state colloction protocols, PCEP[RFC5440], BMP[RFC7854], etc. Or the new protocol like Telemetry[I-D.kumar-rtgwg-grpc-protocol] can be introduced as the soundbound protocols. The protocol choice will be based on the application scenarios of NAI. 6.2. Requirement of Data Collection REQ 02: The data collected from the network devices includes but not limites to following information: -- network topology information -- routing protocol status -- IP routes and MAC routes -- LSP information -- network traffic inforamtion -- network configuration -- network device KPIs -- log of network elements -- trap of network elements -- OAM information 6.3. Requirement of Devices REQ 03: New OAM mechanisms should be introduced for the network devices in order to acquire more types of network state data. 6.4. Requirement of Northbound Interface REQ 04: The abstract network-based service models should be provided by the controller as the northbound models to satisfy the requirements of different services. 7. IANA Considerations This document makes no request of IANA. Li, et al. Expires September 14, 2017 [Page 6] Internet-Draft An Architecture of NAI March 2017 8. Security Considerations TBD. 9. Normative References [I-D.kumar-rtgwg-grpc-protocol] Kumar, A., Kolhe, J., Ghemawat, S., and L. Ryan, "gRPC Protocol", draft-kumar-rtgwg-grpc-protocol-00 (work in progress), July 2016. [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997, . [RFC5440] Vasseur, JP., Ed. and JL. Le Roux, Ed., "Path Computation Element (PCE) Communication Protocol (PCEP)", RFC 5440, DOI 10.17487/RFC5440, March 2009, . [RFC7854] Scudder, J., Ed., Fernando, R., and S. Stuart, "BGP Monitoring Protocol (BMP)", RFC 7854, DOI 10.17487/RFC7854, June 2016, . Authors' Addresses Zhenbin Li Huawei Technologies Huawei Bld., No.156 Beiqing Rd. Beijing 100095 China Email: lizhenbin@huawei.com Yi Zheng China Unicom No.9, Shouti Nanlu, Haidian District Beijing 100048 China Email: zhengyi39@chinaunicom.cn Li, et al. Expires September 14, 2017 [Page 7] Internet-Draft An Architecture of NAI March 2017 Jinhui Zhang Huawei Technologies Huawei Bld., No.156 Beiqing Rd. Beijing 100095 China Email: jason.zhangjinhui@huawei.com Xu Shiping Huawei Technologies Huawei Bld., No.156 Beiqing Rd. Beijing 100095 P.R. China Email: xushiping7@huawei.com Li, et al. Expires September 14, 2017 [Page 8]