Towards Self Driven Networks / 2018年4月10-12日 / Marriott Paris
4月10日 (火)
4月11日 (水)
4月12日 (木)
技術講座
カンファレンス1日目
カンファレンス2日目
展示会
展示会/レセプション
展示会
2018年4月12日 (木) |カンファレンス2日目

  • 07:30 登録手続き、コーヒー
  • 09:00 カンファレンス2日目開始
  • 08:00~19:00 展示会開催
  • 12:30 昼食
  • 17:00 カンファレンス終了

 
CHAIRMAN
Jean-Marc Uze Consultant
ケーススタディとサービスプロバイダーの戦略に関するセッション
09.00
大規模なAIの導入、ネットワークの仮想化と自動化
Looking at how specific AI techniques, combined with an abstracted network model, can solve real world issues related to optimization and automation at scale in multi-layer, multi-vendor networks. In particular, how this combination ultimately delivers the vision of adaptable, dynamic, intelligent, business-driven networks based on SDN and NFV. Discussing case studies and lessons learned from real world projects with global network operators.

Robert Curran,
Strategic Marketing, Aria Networks
09.30
インテントドリブンなコグニティブネットワークの実現に向けた取り組み
Managing communication networks will never be a trivial task, however it does not have to remain as complex as it is today. Designing, developing and deploying innovative technologies in the realm of Intent-Driven Networking will generate enormous gains in performance (doing things better and faster) and in functionality (enabling things previously impossible). But the task will not be easy. This talk will highlight the main challenges ahead and the key enablers in the realization of this vision of future networks. The presentation will make a synthesis of definitions and elements composing an archetypal solution for intent-driven networks by analyzing the work in standards, open source and research initiatives. Finally, an applicability to an IoT use case will illustrate some of the concepts developed in the talk.

Laurent Ciavaglia, Senior Research Manager, Nokia, Bell Labs
10.00
BTのAI戦略

Prof. Dr
Detlef Nauck, Chief Research Scientist for Data Science, BT
10.30
休憩/展示会/関連イベント
AIとIoTについてのセッション
11.00
鳥の群れの模倣:AI技術を利用したIoTの大規模なセキュリティ対策
Fortunately, IoT devices are not like humans, the vast majority of IoT devices are/will be simple, single-minded, and generally predictable. The problem is how to monitor and learn each of those IoT devices, or rather classes and types of IoT devices. Fortunately #2, IoT devices are more like flocks of birds. They move in large groups in the same direction, although unlike flocks of birds, they rarely make sudden changes in their direction or behavior.
Analytics and machine learning enhanced security of IoT networks is like an ornithologist watching flocks of birds: observe, identify, predict, and protect.

Steve Kohalmi,
Mobile Network Security Architect, Office of the CTO,
Juniper Networks

11.30 パネルディスカッション
IoTと5Gに対応するAI:技術とアーキテクチャの課題
MODERATOR
Dean Bubley,
Disruptive Analysis

12.30
昼食


 
ネットワークの分析とテレメトリーに関するセッション
14.00
インサイトドリブンな自動化ネットワーク
Describing a solutions that provides visibility and control that allows to respond to traffic dynamics and security threats. This new operational model uses the enhanced packet intelligence and control capabilities of the groundbreaking FP4 routing silicon. It lets you build a smart network fabric that supports granular traffic management, scalable flow optimization, and highly effective in-line DDoS mitigation.

Roland Thienpont,
IP Division Product Manager, Nokia
14.30
ネットワークのテレメトリー:ビッグデータ分析の基盤
Introducing telemetry, a new technology for freeing network data. Discussing the origins and applications of telemetry, some hard-won lessons and the eco-system of open-source and commercial tools that enable you to turn data into insights. Telemetry brings speed and scale to network monitoring, transforming the landscape into a big data analytics playground.

Kumar Reddy,
Director, Technical Marketing Engineering, Cisco

Kumar Reddy is Senior Director in charge of SP Network Software and Automation architecture. He is responsible for driving new software technology and products around IOS-XR.

15.00
休憩/展示会/関連イベント
15.30
Analytics Edgeの基盤とLambda@Edgeベースのアプリケーションレイヤー
Sharing results on analytics-edge based underlay awareness for minimizing latency/loss maximizing capacity.
As well as lambda-edge based application layer deep analytics.

Sharon Barkai,
HPe
16.00
完全に自動化された分析機能の実現に向けた取り組み
Considering how we can efficiently build pipelines for network data analytics, that enable us to apply advanced analytics at scale with zero-touch. Presenting real-world use cases to illustrate how this can be achieved, using open source software to dynamically analyse telemetry data and provide insights in real-time.

John Evans, Distinguished Engineer, Cisco

John is a Distinguished Engineering at Cisco, focussed on network and technology transformation; defining what network architectures will look like post the transformation to cloud-based services, virtualisation, SDx, NFV, IOT and Big Data Analytics.


16.30 締め括りのパネルディスカッション
理論、研究、発展
What has been realised
What remains to be done
What are the perspectives

MODERATOR

Dean Bubley,
Disruptive Analysis

17.00
カンファレンス閉幕



* 不測の事態により、事前の予告なしにプログラムが変更される場合があります。