IoT and Edge Computing. Satyam Vaghani VP & GM, IOT & AI

Transcription

1 IoT and Edge Computing Satyam Vaghani VP & GM, IOT & AI

2 Where is everybody? Enterprise IoT billion devices Private DCs million nodes Top-3 providers million nodes Enterprise IoT billion devices N O T P U B L I C C L O U D P U B L I C C L O U D Sources: Gartner, IDC

3 A New Paradigm Needs a New OS HUMANS DATACENTERS THINGS DESKTOP Windows Mac OS PRIVATE CLOUD vsphere Acropolis? MOBILE ios Android PUBLIC CLOUD AWS GCE

4 No, Not That OS Scope of an IoT OS

5 Key Change: Edge Trumps Cloud 600 ZB 256 ZB 8.6 ZB 15.3 ZB Cloud traffic IoT data Sources: Cisco Global Cloud Index, Memoori

6 Key Consequence: The Intelligent Edge BEFORE IoT GATEWAY SENSORS AFTER Data Ingestion Real-time Processing Long-term Processing CLOUD AFTER EDGE COMPUTING SENSORS CLOUD Data Ingestion Real-time Processing Long-term Processing

7 The Intelligent Edge is Not Ready TWO PROBLEMS PREVENT WIDESPREAD ADOPTION Distributed infrastructure burden Domain expertise burden

8 A Smart Airport Example TOPOLOGY 2000 x 100 x 250 x Airport 1 (of 10)

9 A Smart Airport Use Case OBJECT OF INTEREST Look for a red car at airport(s) SFO AIRPORT (EDGE)... 10:41AM: redcar=0 10:42AM: redcar=1 10:43AM: redcar=0 10:44AM: redcar= :41AM: SFO, redcar=0 10:42AM: SFO, redcar=1 10:43AM: SFO, redcar=0 10:44AM: SFO, redcar=0... CLOUD

10 IoT Infrastructure Burden: Apps Span Edge and Cloud EXPLICIT DISTRIBUTED SYSTEMS, MACHINE LEARNING, IMAGE RECOGNITION, SECURITY, STORAGE, NETWORK PROGRAMMING, AND MORE Persistence for surveillance feed Sampling surveillance feed to match model input requirements Runtime for car recognition model business logic Security Deploy model to selected edges Train model to recognize red car Persistence of image recognition time series output Data mover to move time series output to cloud infrastructure madness Persistence for time series data in cloud App runtime in cloud

11 IoT Development Burden: Domain Knowledge Analytics Machine Learning Security IoT APP CREATION REQUIRES DOMAIN EXPERTISE IN MULTIPLE AREAS Data Pipelining Cloud Services etc.

12 Don t try this at home Machine Learning

13 An Ideal IoT OS : Hyperconvergence ENABLE RAPID DEVELOPMENT OF IoT APPS MAKE IoT INFRASTRUCTURE SEAMLESS + INVISIBLE Reduce development burden Reduce infrastructure burden Sensor services App services Data services App services Data services EDGE CLOUD

14 Demo HIGHLIGHTS 1 MAKE IoT INFRASTRUCTURE SEAMLESS + INVISIBLE Multi-tenant application runtime across Edge and Cloud 1-click data pipelines across Edge and Cloud 2 ENABLE RAPID DEVELOPMENT OF IoT APPS Modular functions for rapid development of IoT apps

15 Modular Functions for Rapid Development of IoT Apps Datastream in Parameters FUNCTION Datastream out Live video VIDEO PROCESSING Image stream OBJECT RECOGNITION Time series DATA MOVER Global time series in cloud sample, crop redcar, presence Google, structured person, count

16 High level IoT OS Architecture Operations Console Developer Console Control Plane SENSORS Data Ingestion Sensor Actuation Machine Training Real-time Custom Code Structured Streaming Analytics Unstructured Runtime Data Bus Persistence Machine Training Streaming Custom Code Structured Long term Analytics Unstructured EDGE CLOUD INGRESS REAL TIME PROCESSING LONG TERM PROCESSING

17 The Long View: A Data and Code Hypervisor for Hybrid Apps MUCH BIGGER THAN IoT Operations Console Developer Console Control Plane DATA LAKE Data Ingestion Data Archival Machine Learning Streaming Custom Code Structured Streaming Analytics Unstructured Runtime Data Bus Persistence Machine Training Streaming Custom Code Structured Long term Analytics Unstructured LOCAL CLOUD OTHER CLOUDS INGRESS LOCAL PROCESSING REMOTE PROCESSING

18 Use cases A RANDOM WALK Your doctor s office At Great Mall The airline. And the government. And an airport authority ONG services The car company The county

19 GTM experiences

20 Thank You Satyam