Where is the edge? · • Not all interactive applications require deployment at first network hop...

Where is the edge?On Artificial Intelligence and Application

Function Virtualization at the network edge

Telefónica Innovation

Diego Perino, Telefonica Research

Thanks to Xiaoyuan Yang, Joan Serra, Andra Lutu, Ilias Leontiadis, Harini Kolamunna, Yining Hu, Kanchana Thilakarathna, Dwight Makaroff, Xinlong Guan, Aruna Seneviratne, Alejandro Cartas, Martin Kocour, Aravindh Raman, Jordi Luque, Nishanth Sastry, Jose Nuñez-Martinez, Carlos Segura, Mariona Caros, Enrique Frias Martinez

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Where is the edge? Simplified view…

Edge Cloud

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Where is the edge? Things are getting more complex…

IoT and Mobile Devices

Home Devices

ISP and MNOand platforms

Peering points and Internet

Cloud, Platforms

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AGENDA

Virtualization “Before” the edgeApplication Function Virtualization on Personal Area Networks

Artificial Intelligence for (Very) Edge NetworksOperations in (un)-connected Remote communities

Edge networks for Artificial Intelligence Real time AI-based applications

Conclusions

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AGENDA




Conclusions

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Where is the edge? Things are getting more complex…


Home Devices

ISP and MNOand platforms


Cloud, Platforms

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…let’s look at Personal Area Network then J

Tablets & Phones

Watches

Glasses & Lenses

Ear buds

Armbands

Bio-patches & e-textile

Wristbands & Rings

Shoes & Soles

Personal Area Network (PAN)

Laptops & Computers

Extended PAN

Internet

Tier 2 DevicesTier 1 Devices

• Practical situation

• Network of low and high “capacity” devices

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Are we optimally utilize the available resources in a PAN?

• Common functions are available across PAN devices• E.g., step counter, heart rate monitor, …

• Most popular fitness tracking app• Random selection or run all functions• No context awareness

Waste of the limited resources and poor functionality

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Application Function Virtualization - Goals

Utilize the common functionalities available in a PAN

Adaptability UsabilityOptimality

Maximize the user quality of experience

Dynamically adapting to context changes.

Easier for app developers and

end users.

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Application Function Virtualization - Architecture– AFV APIs

– Function Managero Knowledge about PAN

o Manages the requests

– Context Monitoring

– Decision Engineo Functions Allocation

Problem (FAP)

– Communication Managero Manages all AFV

communication in the PAN

– Function Execution

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Implementation of the framework on Android and WearOS

Integration of AFV in to kernel User-level application

All the applications can take the service with minor changes.

Applications need to compile the library and service is requested via IPC calls.

Need special access to the OS for the installation.

No special access is needed for the installation.

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An example use case

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-10

-5

0

5

10

15

0 20 40 60 80 100 120 140 160

Acc

ele

ratio

n (

m/s

2)

Time (s)

WatchPhone

User started walking

Context Change• Function allocation for

information accuracy.

Fitness tracking application requesting accelerometer data.

o Sitting àSmartwatch

o Walking à Smartphone

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• PANs are network per se dependable of the rest of the network• Current apps do not utilize optimally common functionalities• AFV: enables automated dynamic function virtualization /scheduling across

devices, simplifying context-aware application development.• Objective for the optimization : Minimizing the total cost.• Implemented AFV as a user-level application.• In the process of designing more complex function allocation algorithms as

well as interaction with rest of the network

Lessons learned AFV

Harini Kolamunna, Yining Hu, Diego Perino, Kanchana Thilakarathna, Dwight Makaroff, Xinlong Guan, and Aruna Seneviratne. 2016. AFV: enabling application function virtualization and scheduling in wearable networks. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16). Association for Computing Machinery, New York, NY, USA, 981–991. DOI:https://doi.org/10.1145/2971648.2971727

H. Kolamunna, K. Thilakarathna, D. Perino, D. Makaroff and A. Seneviratne, "Seamless Resource Sharing in Wearable Networks by Application Function Virtualization," in IEEE Transactions on Mobile Computing, vol. 18, no. 6, pp. 1393-1406, 1 June 2019.

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AGENDA




Conclusions

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What is Artificial Intelligence?

Machine learning, Deep Learning, etc..• Large amount of data became available recently• More powerful of ad-hoc HW became available

What can edgenetworking do

for AI?

What can AI do for edge

networking?

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Where is the edge? Things are already fairly complex…


Home Devices

ISP and MNO and plafrforms


Cloud, Platforms

AI-based management

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Where is the edge? Things are already fairly complex…


Home Devices

ISP and MNO


Cloud

ARtificial Intelligence for Automatic Network Actions

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Let’s look at rural areas J

Internet Para Todos program is working to provide sustainable mobile broadband to unconnected people in Latin America • open and flexible cellular networks • heterogeneous third-party backhaul transport networks • operational procedures based on a software-defined approach, and automated

operation with ML tools• open-access technology and a revenue-sharing model • thousands of small communities

MOUNTAIN 61%COAST 14% JUNGLE 15%

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Micro-cell network operation

Active Alarm CollectorNMS1

NMS2

NMS3

Passive Alarm Collector

CORBA

SNMP

VendorDependent

Log Parser

Cleaner

Publisher

Pub/sub Msg Queue

Subscriber

Alarm Data Store

REST API

Alarm Backend

Alarm collector

Deployed Network(multi vendor/technology)

Network Operation

Subscriber

Alarm Data Store

REST API

Monitor Dashboard

Rural BoT Huawei(Small Cell)

Huawei(Macro)

Ericsson(Macro)

Model x Tech 1

Model x Tech 2

Model x Tech 3

REST

API

Alarm Backend

Diagnostic Dashboard

OSS Applications Decision Engine

Ticking System APINMSs API Action Repository

• Detect permanent failures

• Detect sites permanently failing

• …

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Micro-cell network operation • 5 months of data from

our operation system• Most failures are

temporal, mainly caused by backhaul saturation or battery outages.

• We need control and predictive mechanisms, in order to avoid costly and unnecessary intervention on temporal failures

• It is important to understand the nature of the failure

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Micro-cell network operation (cont’d)

Active Alarm CollectorNMS1

NMS2

NMS3

Passive Alarm Collector

CORBA

SNMP

VendorDependent

Log Parser

Cleaner

Publisher

Pub/sub Msg Queue

Subscriber

Alarm Data Store

REST API

Alarm Backend

Alarm collector

Deployed Network(multi vendor/technology)

Network Operation

Subscriber

Alarm Data Store

REST API

Monitor Dashboard

Rural BoT Huawei(Small Cell)

Huawei(Macro)

Ericsson(Macro)

Model x Tech 1

Model x Tech 2

Model x Tech 3RE

ST A

PI

Alarm Backend

Diagnostic Dashboard

OSS Applications Decision Engine

Ticking System APINMSs API Action Repository

• AI can reduce operational cost from 5% to 20%

• ML techniques works fine in many use cases

• AI helps the definition of heuristics

Diego Perino, Xiaoyuan Yang, Joan Serra, Andra Lutu, and Ilias Leontiadis. 2020. Experience: advanced network operations in (Un)-connected remote communities. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking (MobiCom ’20). Association for Computing Machinery, New York, NY, USA, Article 16, 1–10. DOI:https://doi.org/10.1145/3372224.3380893

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AGENDA




Conclusions

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Edge for AI-based applications

https://www.telefonica.com/documents/737979/144981357/whitepaper-telefonica-opa-mec-feb-2019.pdfhttps://www.opennetworking.org/wp-content/uploads/2019/09/Connect-2019-Who-Dares.pdf

• AI for video analytics

• 360 video concert

https://www.telefonica.com/documents/737979/144981357/whitepaper-telefonica-opa-mec-feb-2019.pdf

https://www.opennetworking.org/wp-content/uploads/2019/09/Connect-2019-Who-Dares.pdf

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Latency Bandwidth Resiliency

• AR/VR, 360, computer vision• Autonomous remote driving cars• Disaster relief• Industrial IoT• Video Surveillance

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• AR/VR, 360, computer vision• Autonomous remote driving cars• Disaster relief• Industrial IoT• Video Surveillance

Where is the bottleneck? A.k.a where is the right edge?

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Inference algorithm example

Person 99.18

Person 98.9

Person 56.5

Wine glass 36.4

Wine glass 42.86Dining table 42.86

Cup 43.9 Cup 43.0 Cup 36.0

Chair 51.07

Speech recognition• Bot • On-line translation• Transcript for automatic feedback

Object detection• AR/VR• Autonomous car

https://play.google.com/store/apps/details?id=ee.ioc.phon.android.speak

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Faster RCNN [Ren et al 2015] Same model as Gabriel but slower performance.

YOLO [Redmon and Farhadi, 2016] the fastest model available on GPU

Tiny YOLO [Redmon and Farhadi, 2016] a cheaper version of the fastest model for GPU

Object Detection

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Object detection – Tuning and benchmark

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• Darknet still thefastest on GPU• One order of

magnitude faster than CPU

Object DetectionGPU

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Testbed

eNodeB

client

Centraloffice

ISP-DC

Internet

AWS Paris

AWS US (Ohio)

AWS Ireland

• Multi-site mobile testbed of a large European ISP/AWS

• eNodeBs -Intel NUC • CORD-like NFVI

Likely co-located in dense area

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Processing vs Delivery DelayOBJECT RECOGONITION CPU

• Application requirements met at all locations

• Better location is ISP-datacentre or centralized cloud

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Processing vs Delivery DelayOBJECT RECOGONITION GPU

• Application requirements only met at eNodeB/CO with GPUs or high number of cores and specific technologies

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Processing vs Delivery DelaySPEECH RECOGONITION CPU

• Different location meet application requirements with CPU

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• Current ML/DL enables inference in few tens/hundreds of milliseconds: network latency is bottleneck

• Not all interactive applications require deployment at first network hop

• Operators and device manufacturers should dynamically take advantage of heterogeneous deployment

• Novel/enhanced architecture for dynamic resource/function allocation

• 5G networks will make edge computing even more appealing

Some of these future work will be carried on the ACCORDION project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme] under grant agreement No 871793”

Alejandro Cartas, Martin Kocour, Aravindh Raman, Ilias Leontiadis, Jordi Luque, Nishanth Sastry, Jose Nuñez-Martinez, Diego Perino, and Carlos Segura. 2019. A Reality Check on Inference at Mobile Networks Edge. In Proceedings of the 2nd International Workshop on Edge Systems, Analytics and Networking (EdgeSys ’19). Association for Computing Machinery, New York, NY, USA, 54–59. DOI:https://doi.org/10.1145/3301418.3313946

Lessons learned

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More on what edge can do for AI

• Security• Distributed attack detection

• Privacy• Privacy preserving distributed algorithms

• Distributed training• In-network aggregation or processing• Model splitting• …

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• The edge is a complex distributed system– Multiple technologies– Multiple actors

• There is not a single edge – It depends on how you look at it

• Artificial Intelligence and edge computing can and will mutually benefits

..lots of interesting research and innovation in front of us!

Conclusion

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References• Harini Kolamunna, Yining Hu, Diego Perino, Kanchana Thilakarathna, Dwight Makaroff, Xinlong Guan,

and Aruna Seneviratne. 2016. AFV: enabling application function virtualization and scheduling in wearable networks. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’16). Association for Computing Machinery, New York, NY, USA, 981–991. DOI:https://doi.org/10.1145/2971648.2971727

• H. Kolamunna, K. Thilakarathna, D. Perino, D. Makaroff and A. Seneviratne, "Seamless Resource Sharing in Wearable Networks by Application Function Virtualization," in IEEE Transactions on Mobile Computing, vol. 18, no. 6, pp. 1393-1406, 1 June 2019

• Diego Perino, Xiaoyuan Yang, Joan Serra, Andra Lutu, and Ilias Leontiadis. 2020. Experience: advanced network operations in (Un)-connected remote communities. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking (MobiCom ’20). Association for Computing Machinery, New York, NY, USA, Article 16, 1–10. DOI:https://doi.org/10.1145/3372224.3380893

• Alejandro Cartas, Martin Kocour, Aravindh Raman, Ilias Leontiadis, Jordi Luque, Nishanth Sastry, Jose Nuñez-Martinez, Diego Perino, and Carlos Segura. 2019. A Reality Check on Inference at Mobile Networks Edge. In Proceedings of the 2nd International Workshop on Edge Systems, Analytics and Networking (EdgeSys ’19). Association for Computing Machinery, New York, NY, USA, 54–59. DOI:https://doi.org/10.1145/3301418.3313946

Where is the edge? · • Not all interactive applications require deployment at first network hop...

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