SlideShare a Scribd company logo
1 of 69
Download to read offline
The Role of Machine Learning in Fluid
Network Control and Data Planes
Prof. Dr. Christian Esteve Rothenberg
University of Campinas, Brazil
chesteve@dca.fee.unicamp.br
https://intrig.dca.fee.unicamp.br/christian/
https://www.dca.fee.unicamp.br/~chesteve/
TEWI-Kolloquium, 13. October 2022
Agenda
Disclaimer
“Fluid Network Planes” was first presented as a
Keynote of IEEE NetSoft'19, Paris, Jun .2019.
● Fluid Network Planes
○ The ‘Concept’
○ Instances
○ The Role of ML
● ML-based Youtube QoE
estimation in real 5G networks
3
SDN in 2015 – 2020 → Network Softwarization
(i.e. NFV + SDN + IBN + xyz)
Old / Existing
• CLIs & Manual labour
• Closed Source
• Vendor Lead
• Classic Network Appliances (HW)
New / Softwarized
• APIs & Automation
• Open Source
• Customer Lead
• Virtual Network Functions (NFV/SW)
Source: Adapted from Kyle Mestery, Next Generation Network Developer Skills
4
Different Network Softwarization Models Control plane component(s)
Canonical/Open
Traditional
Hybrid/Broker Overlay
Compiler
Source: C. Rothenberg (INTRIG/UNICAMP)
Whitebox / Baremetal
+
PISA / P4
Data plane component(s)
5
Legacy
Models & Approaches to Program / Refactor the Netsoft Stack
Data Plane
Mgm.APIs
Distributed
L2/L3
Control Plane
Managemt
Software
Southbound
Agent
(e.g. OF)
Network Controller / OS
Southbound
Protocol (e.g. OF)
Business / Control Apps
Northbound APIs
Mgm.
HAL APIs / Drivers
Orchestrator (SO/RO/LCM)
APIs
Compiler
Auto-Generated
Target Binary
SDN
VNF
GP-CPU
(x86, ARM)
HW Resources
Virtualization
DP
CP
M
g
m
NFV
VNFM
(Manager)
VIM
(Infra-M)
OSS/BSS
APIs
Southbound
APIs/Plugins
Mgm.
Apps
Network OS / Bare Metal Switches
Source: C. Rothenberg (INTRIG/UNICAMP)
The Fluid Networking landscape
The Fluid Networking landscape
Customer
Premises
BS MEC | Access Cloud | PoP DC
Edge
Cloud DCs
Core
HW
SW
+
-
The Fluid Networking landscape
Customer
Premises
BS MEC | Access Cloud | PoP DC
Edge
Cloud DCs
Core
HW
SW
+
-
Fluid Networking: HW-SW Continuum
Performance
Portability Programmability
HW
SW
Source: D. Meyer (Courtesy by J. Doyle) Source: G. Pongracz. "Cheap silicon". HotSDN13
Source: C. Rothenberg. P3 Trade-offs. 2017
HW
SW
• General-purpose CPU
• HW-accelerated features**
• FPGA
• GPU, TPU,
• Programmable NIC, ASIC
• Domain Specific
Architectures (DSAS)
e.g., P4 + PISA
• Containers
• User space
• Kernel space
• Drivers, I/O SDKs
Flexibility*
(programmability + portability)
Performance***
* M. He et al. Flexibility in Softwarized
Networks: Classifications and Research
Challenges. IEEE Survey & Tutorials, 2019
*** G. Bianchi. Back to the Future:
Hardware-specialized Cloud Networking.
2019
** Linguaglossa et al. Survey of Performance
Acceleration Techniques for Network
Function Virtualization. Proc. of IEEE, 2019
Fluid Networking: HW-SW Continuum
Customer
Premises
BS MEC | Access Cloud | PoP DC
Edge
Cloud DCs
Core
+
-
Fluid Networking: Quest for Latency
Source: Google Cloud Infrastructure
Customer
Premises
BS MEC | Access Cloud | PoP DC
Edge
Cloud DCs
Core
+
-
Source: EU FP7 UNIFY
Fluid Networking: Optimizing the E2E Compute Pool
Fluid Networking: Decoupling functionality / location
Customer
Premises
BS MEC - Access Cloud - PoP DC
Edge
Cloud DCs
Core
+
-
Latency
Capacity
Cost
Source: EU
Superfluidity
The Fluid Networking landscape
Customer
Premises
BS MEC - Access Cloud - PoP DC
Edge
Cloud DCs
Core
HW
SW
Optimize for Latency
(Latency-sensitive Source to Function)
Optimize for
Performance/Cost
Control plane component(s)
Data plane component(s)
The Fluid Networking landscape
Customer
Premises
BS MEC - Access Cloud - PoP DC
Edge
Cloud DCs
Core
HW
SW
?
?
?
?
The Fluid Networking landscape
Customer
Premises
BS MEC - Access Cloud - PoP DC
Edge
Cloud DCs
Core
HW
SW
?
?
?
?
The Fluid Networking landscape
Customer
Premises
BS MEC - Access Cloud - PoP DC
Edge
Cloud DCs
Core
HW
SW
The Fluid Networking landscape
Customer
Premises
BS MEC - Access Cloud - PoP DC
Edge
Cloud DCs
Core
HW
SW
The Fluid Networking landscape
Customer
Premises
BS MEC - Access Cloud - PoP DC
Edge
Cloud DCs
Core
HW
SW
ML
Instances of
Fluid Network Planes
RouteFlow (2010 - )
Customer
Premises
BS MEC - Access Cloud - PoP DC
Edge
Cloud DCs
Core
HW
SW
legacy
BGP
IXP
NFV layers of SW, Virtualization and HW platforms
HW
SW
Source: https://www.dpdk.org/wp-content/uploads/sites/35/2018/12/Kalimani-and-Barak-Accelerating-NFV-with-DPDK-and-SmartNICs.pdf
VNF offloading to Hardware
HW
SW
Source: https://www.dpdk.org/wp-content/uploads/sites/35/2018/12/Kalimani-and-Barak-Accelerating-NFV-with-DPDK-and-SmartNICs.pdf
HW
SW
VNF offloading to Hardware
Source: https://www.dpdk.org/wp-content/uploads/sites/35/2018/12/Kalimani-and-Barak-Accelerating-NFV-with-DPDK-and-SmartNICs.pdf
VNF offloading on multi-vendor P4 fabric
controlled by ONOS via P4Runtime
HW
SW
Source:
https://p4.org/assets/P4WS_2018/7_Carmelo_Cascone_VNF.pdf
Slicing IoT Analytics
Access Cloud
uRLLC GW eMBB
GW
vCDN
Edge Cloud Central
Cloud
5G CP
mMTC
vCDN
MEC/vCDN
Internet Internet
Midhaul
5G
AAU
5G
DU
5G CU
Fronthaul
5G
AAU
Transport
Network Slicing
Slice 1: 100M,
eMBB
Slice 2: 1G,
Video
Slice 3: 90G
Other Slices
Slice 1: 100M,
eMBB
Slice 2: 1G,
Video
Slice 3: 90G
Other Slices
Transport
Network Slicing
Cloud – Slice
Orchestr &
Management
System
Network – Slice
Orchestr &
Management System
DoJoT
DoJoT DoJoT
IoT
Monitoring & Control
Slice 5: mIoT – computing intensive
Slice 4: mIoT – latency
sensitive
Source: http://www.h2020-necos.eu/
In-Network Computing
* D. Ports and J. Nelson. When Should The Network Be
The Computer?. HotOS'19
IRTF Computation in the Network (COIN)
J. Vestin et al. In-Network Control and Caching for Industrial
Control Networks using Programmable Data Planes. 2018
X. Jin et al. Netcache: Balancing key-value
stores with fast in-network caching. SOSP'17
H. Tu Dang et al. P4xos: Consensus
as a Network Service. 2018
SwitchML: the network is the ML accelerator
A. Sapio et al. Scaling Distributed Machine
Learning with In-Network Aggregation. 2019
E. Costa Molero et al. Hardware-Accelerated
Network Control Planes. HotNets'18
* T. Holterbach et al. Blink: Fast Connectivity
Recovery Entirely in the Data Plane. NSDI'19
Control plane functions offloaded to Data plane / HW
S Chinchali et al. Network Offloading Policies for
Cloud Robotics: a Learning-based Approach.
Auton Robot 45, 997–1012 (2021).
Robot Offloading Problem
Robotic Arm Controller Offloading
Low Latency Industrial Robot Control in Programmable Data Planes
• P4 data plane
• parses robot 3D coordinates
• detects “stop region”
• accelerates stop command
by crafting a stop message
inside the TCP connection
F. Rodriguez et al. ."Towards Low Latency Industrial
Robot Control in Programmable Data Planes". In
IEEE NetSoft 2020, Ghent, Belgium, June 2020
Robotic Arm Controller Offloading
In-Network Centralized Done Collision Avoidance
Algorithm in Programmable Data Planes
• P4 Workshop 2021
(Award Winner of most novel non-networking use of P4)
ML & Fluid Network Planes
ML for Networking
A myriad of recent works (as in all areas, not just networking)
• Boutaba, R., Salahuddin, M.A., Limam, N. et al. “A comprehensive survey on machine
learning for networking: evolution, applications and research opportunities”. JISA, 2018.
• M. Usama et al., "Unsupervised Machine Learning for Networking: Techniques,
Applications and Research Challenges," in IEEE Access, vol. 7, pp. 65579-65615, 2019,.
• J. Xie et al., "A Survey of Machine Learning Techniques Applied to Software Defined
Networking (SDN): Research Issues and Challenges," in IEEE Communications Surveys &
Tutorials, vol. 21, no. 1, pp. 393-430, 2019.
• M. A. Ridwan, N. A. M. Radzi, F. Abdullah and Y. E. Jalil, "Applications of Machine
Learning in Networking: A Survey of Current Issues and Future Challenges," in IEEE
Access, vol. 9, pp. 52523-52556, 2021
• ...
ML for Networking
Source: Boutaba, R., Salahuddin, M.A., Limam, N. et al. “A comprehensive survey on machine learning for networking: evolution, applications and research opportunities”. JISA, 2018.
ML applications
Fluid Network Planes
• (design time) Optimization
– HW parameters / confirmation
– SW code optimization (e.g., P4 compiler)
• (offline) Virtual Network embedding like problems
– P4 code target selection
– HW offloading / acceleration
– VNF Placement process
– Network Service Chain placement
– HW/SW Performance estimation
• (online) Run-time operations
– HW/SW Performance prediction
• QoS / SLA violation prediction
• Scale up/down & out/in recommendations
(Dev/Ops) Learning
ML applications
Fluid Network Planes
In-network Machine Learning moving to the edge
Machine Learning offloading strategies:
Design space for offloading machine learning inference tasks from the end-host’s CPU.
Siracusano, Giuseppe, et al.
"Running neural networks on the NIC."
https://arxiv.org/abs/2009.02353
ML applications
Fluid Network Planes
ML applications
Fluid Network Planes
ETSI ZSM Closed Loop Automation
•
N. Sousa et al.,"Machine Learning-Assisted Closed-Control Loops for Beyond 5G Multi-Domain
Zero-Touch Networks". In Journal of Network and Systems Management (JNSM), Special Issue on
Network Management in beyond 5G Systems, 2022
ML applications
Fluid Network Planes
ETSI ZSM Closed Loop Automation
•
Nathan F. Saraiva, Christian E. Rothenberg. "CLARA: Closed Loop-based Zero-touch Network Management
Framework". In IEEE NFV-SDN Doctoral Symposium, Nov. 2021. (Best Doctoral Symposium Paper Award)
Intent-based CCL for DASH Video SLA using ML-based Edge QoE Estimation
Recent and ongoing research
C. Rothenberg et al. "Intent-based Control Loop for DASH Video Service Assurance using
ML-based Edge QoE Estimation". In IEEE NetSoft 2020 Demo Session, Ghent, Belgium, June 2020.
Intent-based CCL for DASH Video SLA using ML-based Edge QoE Estimation
Recent and ongoing research
R. Mustafa et al. . "DASH QoE performance evaluation framework with 5G
datasets". In IEEE CNSM AnServApp. Nov. 2020.
Recent and ongoing research
https://github.com/razaulmustafa852/EFFECTOR
DASH QoE estimation from QoS-level metrics in 4G/5G scenarios
Recent and ongoing research
https://github.com/razaulmustafa852/EFFECTOR
DASH QoE estimation from QoS-level metrics in 4G/5G scenarios
ML-based Youtube QoE estimation in real 5G networks
Recent and ongoing research
Real 4G/5G trace collection in Youtube streaming
• Data sets to become public for open & reproducible research
Research Questions
• Evaluation of Youtube QoE KPIs for different mobile networks.
ie. how does Youtube perform in 5G (DSS vs NSA vs SA) compared to 4G?
• Can we predict future (e.g. 5 sec) Youtube QoE KPIs based on network-level
metrics (radio parameters and flow-level?)
– What to do after predicting a QoE downgrade (e.g., stalls, shift down)?
Recent and ongoing research
Real 4G/5G trace collection in Youtube streaming
• completed in Nice, France (4G vs 5G DSS)
• starting in Campinas, Brazil (4G vs 5G DSS) and Sao Paulo (5G NSA vs SA)
Detail of the solution (3/4)
Data Collection Use Cases
1) Pedestrian – Low Mobility
2) Driving – High Mobility
3) Static – Bus and Railway Terminals
4) Static Outdoor – High Crowd
Dataset Statistics
Mobility – Total Kilometers 300 +
Pedestrian – Total Kilometers 100 +
Number of Videos 10
Total Video Sessions 300 +, 1500 + Minutes Streaming
4G and 5G Data Consumed 300 + GB
5G Smartphone Samsung Galaxy S21 5G
4G Smartphone Samsung Galaxy S8
Results (1/4): 4G vs. 5G - CQI, RSRP, RSRQ, SNR
Results (2/4): 4G vs. 5G - CQI, RSRP, RSRQ, SNR
• We see a nice CQI (CQI is a feedback provided by UE to eNodeB), RSRP (RSRP is used
for measuring cell signal strength/coverage and therefore cell selection (dBm)) at Bus and
Railway Terminals, however, a massive difference RSRQ for Mobility, Pedestrian in 4G.
• 5G shows a similar pattern, but we see an opposite effect of RSRQ for Mobility and
Pedestrian
• Moreover, we experience better CQI in 5G as compared to 4G.
Results (3/4): 4G vs. 5G – Case Mobility Stalling Events - Handover
o 5G in mobility suffers
more stalling events as
compared to 4G.
o 5G also experience
more handover events.
o In 5G, it continues
streaming at higher
resolutions, even there
are stalling events as
compared to 4G, where
we see multiple quality
shifts.
o Most handover occurs
at CQI value 6.
4G vs. 5G QoE
Results (4/4): 4G vs. 5G – Resolutions (Mobility Cases)
o Mobility - High: 4G
experiences multiple quality
shifts during a video
session, thus less stalling
events, but 5G it is more
greedy with maximum
resolutions, thus cause
stalls.
o Mobility – Low: 4G suffers
with quality shifting as
compared to 5G. We see
95.3% hd2160 resolution
in 5G.
o Static cases experience
more stability in both
technology, i.e., hd1440
(10%) and hd2160 (90 %) in
4G and in 5G hd2160
(100%)
4G vs. 5G Resolutions
Mobility
-
High
Mobility
-
Pedestrian
Prev n-seconds Experience
● Look at the previous n-seconds
experience
● Look at the application QoE, and when
interruption came, see the previous
n-second channel metrics
○ Quality Shifts
○ Stalls
Interruptions: 4G Mobility
No Interruptions: 5G Mobility
References
● Kaljic, Enio, et al. "A Survey on Data Plane Flexibility and Programmability in Software-Defined
Networking." arXiv preprint arXiv:1903.04678 (2019).
● L. Linguaglossa et al., "Survey of Performance Acceleration Techniques for Network Function
Virtualization," in Proceedings of the IEEE, vol. 107, no. 4, pp. 746-764, April 2019.
● Edgar Costa Molero, Stefano Vissicchio, and Laurent Vanbever. 2018. Hardware-Accelerated
Network Control Planes. In Proceedings of the 17th ACM Workshop on Hot Topics in Networks
(HotNets '18). ACM, New York, NY, USA, 120-126.
● Huynh Tu Dang, Marco Canini, Fernando Pedone, and Robert Soulé. “Paxos Made Switch-y.” In
ACM SIGCOMM Computer Communication Review (CCR). April 2016.
● JIN, Xin et al. Netcache: Balancing key-value stores with fast in-network caching. In: Proceedings of
the 26th Symposium on Operating Systems Principles. ACM, 2017
● Yuta Tokusashi, Huynh Tu Dang, Fernando Pedone, Robert Soulé, and Noa Zilberman. “The Case
For In-Network Computing On Demand.” In European Conference on Computer Systems
(EuroSYS). March 2019.
References
● D. Ports and J. Nelson. When Should The Network Be The Computer?. In Proceedings of the
Workshop on Hot Topics in Operating Systems (HotOS '19)
● Atul Adya, Robert Grandl, Daniel Myers, and Henry Qin. 2019. Fast key-value stores: An idea whose
time has come and gone. In Proceedings of the Workshop on Hot Topics in Operating Systems
(HotOS '19)
● Theophilus A. Benson. 2019. In-Network Compute: Considered Armed and Dangerous. In
Proceedings of the Workshop on Hot Topics in Operating Systems (HotOS '19)
● Theo Jepsen, Daniel Alvarez, Nate Foster, Changhoon Kim, Jeongkeun Lee, Masoud Moshref, and
Robert Soulé. 2019. Fast String Searching on PISA. In Proceedings of the 2019 ACM Symposium
on SDN Research (SOSR '19)
● Thomas Holterbach, Edgar Costa Molero, Maria Apostolaki, Alberto Dainotti, Stefano Vissicchio,
Laurent Vanbever. Blink: Fast Connectivity Recovery Entirely in the Data Plane. NSDI 2019.
● A. Sapio, M. Canini, C.-Y. Ho, J. Nelson, P. Kalnis, C. Kim, A. Krishnamurthy, M. Moshref, D. R. K.
Ports, P. Richtarik. Scaling Distributed Machine Learning with In-Network Aggregation. KAUST
References
● A. Sapio et al. Scaling Distributed Machine Learning with In-Network Aggregation. 2019.
● Huynh Tu Dang, Pietro Bressana, Han Wang, Ki Suh Lee, Hakim Weatherspoon, Marco Canini,
Fernando Pedone, Noa Zilberman, Robert Soulé, "P4xos: Consensus as a Network Service", Tech
Report, University of Lugano 2018/01, May 2018
● H. Tu Dang et al. P4xos: Consensus as a Network Service. 2018
● Raphael Rosa and Christian Esteve Rothenberg. "The Pandora of Network Slicing: A Multi-Criteria
Analysis". ETT. 2019
● J. Vestin, A. Kassler, J. Åkerberg, FastReact: In-Network Control and Caching for Industrial Control
Networks using Programmable Data Planes. In 2018 IEEE 23rd International Conference on
Emerging Technologies and Factory Automation September 4th - 7th, 2018, Torino, Italy.
Credits
● http://www2.technologyreview.com/article/412194/tr10-software-defined-networking/
● Fluid 1 image source: https://www.trzcacak.rs/detail/199233/
● Fluid 2 image source: http://www.pngall.com/water-png/download/1933
● Intelligent Brain image source: https://ui-ex.com/explore/transparent-brain-artificial-intelligence/
● Orchestrator image source:
https://apievangelist.com/2015/02/06/when-you-are-ready-for-nuanced-discussion-about-who-has-a
ccess-to-your-api-i-am-here/
● Poison image source: https://www.stickpng.com/cat/miscellaneous/poison?page=1
Danke!
Questions?
BACKUP
E2E CCL
Recent and ongoing research
• Technique for Simplifying Management of a Service in a Cloud Computing Environment.
PCT/EP2019/058274. 2019
● Service-specific metrics:
○ Per domain (e.g., cloud and
transport)
○ E2E view (cloud + transport)
○ Represented by Graphs
○ Stored into Graph-based
Database (Neo4J)
Detail of the solution (1/4)
4G and 5G dataset collection using the most popular video streaming
website YouTube with 1-second granularity.
YOUTUBE FEATURES:
❑ Stalls, Resolution, Video Bytes Downloaded that can further
provide per-session Objective QoE (i) Total Stalling Event, ii)
Stalling Ratio, iii) Stalling Time, iv) Quality Shifts or Percentage of
Time in a single Resolution, v) Dominant Resolution, etc.
Detail of the solution (2/4)
CHANNEL LEVEL METRICS
1-second granularity, few features below
❑ Timestamp, Longitude, Latitude, Velocity, Operator Name, Cellid,
Network Mode, Download bitrate, Upload bitrate, RSRQ, RSRP,
SNR, RSSI, CQI, RSRQ and RSRP values for the neighbouring
cell, among 100 + other channel metrics *.
* https://gyokovsolutions.com/manual-g-nettrack/
Detail of the solution (4/4)
• YouTube IFRAME API for YouTube QoE Logs
• G-NetTrack Pro - Wireless network monitor and drive test tool

More Related Content

Similar to The Role of Machine Learning in Fluid Network Control and Data Planes.pdf

CloudCamp Milan 2009: Telecom Italia
CloudCamp Milan 2009: Telecom ItaliaCloudCamp Milan 2009: Telecom Italia
CloudCamp Milan 2009: Telecom ItaliaGabriele Bozzi
 
IPv4 to IPv6 network transformation
IPv4 to IPv6 network transformationIPv4 to IPv6 network transformation
IPv4 to IPv6 network transformationNikolay Milovanov
 
IEEE HPSR 2017 Keynote: Softwarized Dataplanes and the P^3 trade-offs: Progra...
IEEE HPSR 2017 Keynote: Softwarized Dataplanes and the P^3 trade-offs: Progra...IEEE HPSR 2017 Keynote: Softwarized Dataplanes and the P^3 trade-offs: Progra...
IEEE HPSR 2017 Keynote: Softwarized Dataplanes and the P^3 trade-offs: Progra...Christian Esteve Rothenberg
 
Networking Challenges for the Next Decade
Networking Challenges for the Next DecadeNetworking Challenges for the Next Decade
Networking Challenges for the Next DecadeOpen Networking Summit
 
FPGA-based soft-processors: 6G nodes and post-quantum security in space
 FPGA-based soft-processors: 6G nodes and post-quantum security in space FPGA-based soft-processors: 6G nodes and post-quantum security in space
FPGA-based soft-processors: 6G nodes and post-quantum security in spaceFacultad de Informática UCM
 
Artificial intelligence in IoT-to-core network operations and management
Artificial intelligence in IoT-to-core network operations and managementArtificial intelligence in IoT-to-core network operations and management
Artificial intelligence in IoT-to-core network operations and managementADVA
 
Future Internet: Managing Innovation and Testbed
Future Internet: Managing Innovation and TestbedFuture Internet: Managing Innovation and Testbed
Future Internet: Managing Innovation and TestbedShinji Shimojo
 
Trends and Hot Topics in Networking 2023 - IA377 Seminar FEEC-UNICAMP
Trends and Hot Topics in Networking 2023 - IA377 Seminar FEEC-UNICAMPTrends and Hot Topics in Networking 2023 - IA377 Seminar FEEC-UNICAMP
Trends and Hot Topics in Networking 2023 - IA377 Seminar FEEC-UNICAMPChristian Esteve Rothenberg
 
SDN and NFV Value in Business Services - A Presentation By Cox Communications
SDN and NFV Value in Business Services - A Presentation By Cox CommunicationsSDN and NFV Value in Business Services - A Presentation By Cox Communications
SDN and NFV Value in Business Services - A Presentation By Cox CommunicationsCisco Service Provider
 
2013 09-22, transport sdn and ecoc, ping pan
2013 09-22, transport sdn and ecoc, ping pan2013 09-22, transport sdn and ecoc, ping pan
2013 09-22, transport sdn and ecoc, ping panpingpan
 
CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...
CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...
CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...IJCNCJournal
 
云计算及其应用
云计算及其应用云计算及其应用
云计算及其应用lantianlcdx
 
Im 2021 tutorial next-generation closed-loop automation - an inside view - ...
Im 2021 tutorial   next-generation closed-loop automation - an inside view - ...Im 2021 tutorial   next-generation closed-loop automation - an inside view - ...
Im 2021 tutorial next-generation closed-loop automation - an inside view - ...Ishan Vaishnavi
 
Next-Generation Closed-Loop Automation - an Inside View
Next-Generation Closed-Loop Automation - an Inside ViewNext-Generation Closed-Loop Automation - an Inside View
Next-Generation Closed-Loop Automation - an Inside ViewLaurent Ciavaglia
 
IEEE IM 2021 Tutorial - Next-generation closed-loop automation - an inside view
IEEE IM 2021 Tutorial - Next-generation closed-loop automation - an inside viewIEEE IM 2021 Tutorial - Next-generation closed-loop automation - an inside view
IEEE IM 2021 Tutorial - Next-generation closed-loop automation - an inside viewPedro Henrique Gomes
 
Emerging Computing Architectures
Emerging Computing ArchitecturesEmerging Computing Architectures
Emerging Computing ArchitecturesDaniel Holmberg
 
Edge Computing Platforms and Protocols - Ph.D. thesis
Edge Computing Platforms and Protocols - Ph.D. thesisEdge Computing Platforms and Protocols - Ph.D. thesis
Edge Computing Platforms and Protocols - Ph.D. thesisNitinder Mohan
 
Akash rajguru project report sem VI
Akash rajguru project report sem VIAkash rajguru project report sem VI
Akash rajguru project report sem VIAkash Rajguru
 

Similar to The Role of Machine Learning in Fluid Network Control and Data Planes.pdf (20)

CloudCamp Milan 2009: Telecom Italia
CloudCamp Milan 2009: Telecom ItaliaCloudCamp Milan 2009: Telecom Italia
CloudCamp Milan 2009: Telecom Italia
 
2017 dagstuhl-nfv-rothenberg
2017 dagstuhl-nfv-rothenberg2017 dagstuhl-nfv-rothenberg
2017 dagstuhl-nfv-rothenberg
 
IPv4 to IPv6 network transformation
IPv4 to IPv6 network transformationIPv4 to IPv6 network transformation
IPv4 to IPv6 network transformation
 
IEEE HPSR 2017 Keynote: Softwarized Dataplanes and the P^3 trade-offs: Progra...
IEEE HPSR 2017 Keynote: Softwarized Dataplanes and the P^3 trade-offs: Progra...IEEE HPSR 2017 Keynote: Softwarized Dataplanes and the P^3 trade-offs: Progra...
IEEE HPSR 2017 Keynote: Softwarized Dataplanes and the P^3 trade-offs: Progra...
 
Networking Challenges for the Next Decade
Networking Challenges for the Next DecadeNetworking Challenges for the Next Decade
Networking Challenges for the Next Decade
 
FPGA-based soft-processors: 6G nodes and post-quantum security in space
 FPGA-based soft-processors: 6G nodes and post-quantum security in space FPGA-based soft-processors: 6G nodes and post-quantum security in space
FPGA-based soft-processors: 6G nodes and post-quantum security in space
 
Artificial intelligence in IoT-to-core network operations and management
Artificial intelligence in IoT-to-core network operations and managementArtificial intelligence in IoT-to-core network operations and management
Artificial intelligence in IoT-to-core network operations and management
 
Future Internet: Managing Innovation and Testbed
Future Internet: Managing Innovation and TestbedFuture Internet: Managing Innovation and Testbed
Future Internet: Managing Innovation and Testbed
 
Trends and Hot Topics in Networking 2023 - IA377 Seminar FEEC-UNICAMP
Trends and Hot Topics in Networking 2023 - IA377 Seminar FEEC-UNICAMPTrends and Hot Topics in Networking 2023 - IA377 Seminar FEEC-UNICAMP
Trends and Hot Topics in Networking 2023 - IA377 Seminar FEEC-UNICAMP
 
SDN and NFV Value in Business Services - A Presentation By Cox Communications
SDN and NFV Value in Business Services - A Presentation By Cox CommunicationsSDN and NFV Value in Business Services - A Presentation By Cox Communications
SDN and NFV Value in Business Services - A Presentation By Cox Communications
 
2013 09-22, transport sdn and ecoc, ping pan
2013 09-22, transport sdn and ecoc, ping pan2013 09-22, transport sdn and ecoc, ping pan
2013 09-22, transport sdn and ecoc, ping pan
 
CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...
CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...
CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...
 
云计算及其应用
云计算及其应用云计算及其应用
云计算及其应用
 
Im 2021 tutorial next-generation closed-loop automation - an inside view - ...
Im 2021 tutorial   next-generation closed-loop automation - an inside view - ...Im 2021 tutorial   next-generation closed-loop automation - an inside view - ...
Im 2021 tutorial next-generation closed-loop automation - an inside view - ...
 
Next-Generation Closed-Loop Automation - an Inside View
Next-Generation Closed-Loop Automation - an Inside ViewNext-Generation Closed-Loop Automation - an Inside View
Next-Generation Closed-Loop Automation - an Inside View
 
IEEE IM 2021 Tutorial - Next-generation closed-loop automation - an inside view
IEEE IM 2021 Tutorial - Next-generation closed-loop automation - an inside viewIEEE IM 2021 Tutorial - Next-generation closed-loop automation - an inside view
IEEE IM 2021 Tutorial - Next-generation closed-loop automation - an inside view
 
Emerging Computing Architectures
Emerging Computing ArchitecturesEmerging Computing Architectures
Emerging Computing Architectures
 
Edge Computing Platforms and Protocols - Ph.D. thesis
Edge Computing Platforms and Protocols - Ph.D. thesisEdge Computing Platforms and Protocols - Ph.D. thesis
Edge Computing Platforms and Protocols - Ph.D. thesis
 
Akash rajguru project report sem VI
Akash rajguru project report sem VIAkash rajguru project report sem VI
Akash rajguru project report sem VI
 
Intro Cloud Computing
Intro Cloud ComputingIntro Cloud Computing
Intro Cloud Computing
 

More from Förderverein Technische Fakultät

The Digital Transformation of Education: A Hyper-Disruptive Era through Block...
The Digital Transformation of Education: A Hyper-Disruptive Era through Block...The Digital Transformation of Education: A Hyper-Disruptive Era through Block...
The Digital Transformation of Education: A Hyper-Disruptive Era through Block...Förderverein Technische Fakultät
 
Engineering Serverless Workflow Applications in Federated FaaS.pdf
Engineering Serverless Workflow Applications in Federated FaaS.pdfEngineering Serverless Workflow Applications in Federated FaaS.pdf
Engineering Serverless Workflow Applications in Federated FaaS.pdfFörderverein Technische Fakultät
 
Nonequilibrium Network Dynamics_Inference, Fluctuation-Respones & Tipping Poi...
Nonequilibrium Network Dynamics_Inference, Fluctuation-Respones & Tipping Poi...Nonequilibrium Network Dynamics_Inference, Fluctuation-Respones & Tipping Poi...
Nonequilibrium Network Dynamics_Inference, Fluctuation-Respones & Tipping Poi...Förderverein Technische Fakultät
 
East-west oriented photovoltaic power systems: model, benefits and technical ...
East-west oriented photovoltaic power systems: model, benefits and technical ...East-west oriented photovoltaic power systems: model, benefits and technical ...
East-west oriented photovoltaic power systems: model, benefits and technical ...Förderverein Technische Fakultät
 
Advances in Visual Quality Restoration with Generative Adversarial Networks
Advances in Visual Quality Restoration with Generative Adversarial NetworksAdvances in Visual Quality Restoration with Generative Adversarial Networks
Advances in Visual Quality Restoration with Generative Adversarial NetworksFörderverein Technische Fakultät
 
Industriepraktikum_ Unterstützung bei Projekten in der Automatisierung.pdf
Industriepraktikum_ Unterstützung bei Projekten in der Automatisierung.pdfIndustriepraktikum_ Unterstützung bei Projekten in der Automatisierung.pdf
Industriepraktikum_ Unterstützung bei Projekten in der Automatisierung.pdfFörderverein Technische Fakultät
 

More from Förderverein Technische Fakultät (20)

Supervisory control of business processes
Supervisory control of business processesSupervisory control of business processes
Supervisory control of business processes
 
The Digital Transformation of Education: A Hyper-Disruptive Era through Block...
The Digital Transformation of Education: A Hyper-Disruptive Era through Block...The Digital Transformation of Education: A Hyper-Disruptive Era through Block...
The Digital Transformation of Education: A Hyper-Disruptive Era through Block...
 
A Game of Chess is Like a Swordfight.pdf
A Game of Chess is Like a Swordfight.pdfA Game of Chess is Like a Swordfight.pdf
A Game of Chess is Like a Swordfight.pdf
 
From Mind to Meta.pdf
From Mind to Meta.pdfFrom Mind to Meta.pdf
From Mind to Meta.pdf
 
Miniatures Design for Tabletop Games.pdf
Miniatures Design for Tabletop Games.pdfMiniatures Design for Tabletop Games.pdf
Miniatures Design for Tabletop Games.pdf
 
Distributed Systems in the Post-Moore Era.pptx
Distributed Systems in the Post-Moore Era.pptxDistributed Systems in the Post-Moore Era.pptx
Distributed Systems in the Post-Moore Era.pptx
 
Don't Treat the Symptom, Find the Cause!.pptx
Don't Treat the Symptom, Find the Cause!.pptxDon't Treat the Symptom, Find the Cause!.pptx
Don't Treat the Symptom, Find the Cause!.pptx
 
Engineering Serverless Workflow Applications in Federated FaaS.pdf
Engineering Serverless Workflow Applications in Federated FaaS.pdfEngineering Serverless Workflow Applications in Federated FaaS.pdf
Engineering Serverless Workflow Applications in Federated FaaS.pdf
 
Nonequilibrium Network Dynamics_Inference, Fluctuation-Respones & Tipping Poi...
Nonequilibrium Network Dynamics_Inference, Fluctuation-Respones & Tipping Poi...Nonequilibrium Network Dynamics_Inference, Fluctuation-Respones & Tipping Poi...
Nonequilibrium Network Dynamics_Inference, Fluctuation-Respones & Tipping Poi...
 
Towards a data driven identification of teaching patterns.pdf
Towards a data driven identification of teaching patterns.pdfTowards a data driven identification of teaching patterns.pdf
Towards a data driven identification of teaching patterns.pdf
 
Förderverein Technische Fakultät.pptx
Förderverein Technische Fakultät.pptxFörderverein Technische Fakultät.pptx
Förderverein Technische Fakultät.pptx
 
The Computing Continuum.pdf
The Computing Continuum.pdfThe Computing Continuum.pdf
The Computing Continuum.pdf
 
East-west oriented photovoltaic power systems: model, benefits and technical ...
East-west oriented photovoltaic power systems: model, benefits and technical ...East-west oriented photovoltaic power systems: model, benefits and technical ...
East-west oriented photovoltaic power systems: model, benefits and technical ...
 
Machine Learning in Finance via Randomization
Machine Learning in Finance via RandomizationMachine Learning in Finance via Randomization
Machine Learning in Finance via Randomization
 
IT does not stop
IT does not stopIT does not stop
IT does not stop
 
Advances in Visual Quality Restoration with Generative Adversarial Networks
Advances in Visual Quality Restoration with Generative Adversarial NetworksAdvances in Visual Quality Restoration with Generative Adversarial Networks
Advances in Visual Quality Restoration with Generative Adversarial Networks
 
Recent Trends in Personalization at Netflix
Recent Trends in Personalization at NetflixRecent Trends in Personalization at Netflix
Recent Trends in Personalization at Netflix
 
Industriepraktikum_ Unterstützung bei Projekten in der Automatisierung.pdf
Industriepraktikum_ Unterstützung bei Projekten in der Automatisierung.pdfIndustriepraktikum_ Unterstützung bei Projekten in der Automatisierung.pdf
Industriepraktikum_ Unterstützung bei Projekten in der Automatisierung.pdf
 
Introduction to 5G from radio perspective
Introduction to 5G from radio perspectiveIntroduction to 5G from radio perspective
Introduction to 5G from radio perspective
 
Förderverein Technische Fakultät
Förderverein Technische Fakultät Förderverein Technische Fakultät
Förderverein Technische Fakultät
 

Recently uploaded

FORENSIC CHEMISTRY ARSON INVESTIGATION.pdf
FORENSIC CHEMISTRY ARSON INVESTIGATION.pdfFORENSIC CHEMISTRY ARSON INVESTIGATION.pdf
FORENSIC CHEMISTRY ARSON INVESTIGATION.pdfSuchita Rawat
 
TEST BANK for Organic Chemistry 6th Edition.pdf
TEST BANK for Organic Chemistry 6th Edition.pdfTEST BANK for Organic Chemistry 6th Edition.pdf
TEST BANK for Organic Chemistry 6th Edition.pdfmarcuskenyatta275
 
EU START PROJECT. START-Newsletter_Issue_4.pdf
EU START PROJECT. START-Newsletter_Issue_4.pdfEU START PROJECT. START-Newsletter_Issue_4.pdf
EU START PROJECT. START-Newsletter_Issue_4.pdfStart Project
 
Efficient spin-up of Earth System Models usingsequence acceleration
Efficient spin-up of Earth System Models usingsequence accelerationEfficient spin-up of Earth System Models usingsequence acceleration
Efficient spin-up of Earth System Models usingsequence accelerationSérgio Sacani
 
Film Coated Tablet and Film Coating raw materials.pdf
Film Coated Tablet and Film Coating raw materials.pdfFilm Coated Tablet and Film Coating raw materials.pdf
Film Coated Tablet and Film Coating raw materials.pdfPharmatech-rx
 
PARENTAL CARE IN FISHES.pptx for 5th sem
PARENTAL CARE IN FISHES.pptx for 5th semPARENTAL CARE IN FISHES.pptx for 5th sem
PARENTAL CARE IN FISHES.pptx for 5th semborkhotudu123
 
Heat Units in plant physiology and the importance of Growing Degree days
Heat Units in plant physiology and the importance of Growing Degree daysHeat Units in plant physiology and the importance of Growing Degree days
Heat Units in plant physiology and the importance of Growing Degree daysBrahmesh Reddy B R
 
Factor Causing low production and physiology of mamary Gland
Factor Causing low production and physiology of mamary GlandFactor Causing low production and physiology of mamary Gland
Factor Causing low production and physiology of mamary GlandRcvets
 
Adaptive Restore algorithm & importance Monte Carlo
Adaptive Restore algorithm & importance Monte CarloAdaptive Restore algorithm & importance Monte Carlo
Adaptive Restore algorithm & importance Monte CarloChristian Robert
 
RACEMIzATION AND ISOMERISATION completed.pptx
RACEMIzATION AND ISOMERISATION completed.pptxRACEMIzATION AND ISOMERISATION completed.pptx
RACEMIzATION AND ISOMERISATION completed.pptxArunLakshmiMeenakshi
 
X-rays from a Central “Exhaust Vent” of the Galactic Center Chimney
X-rays from a Central “Exhaust Vent” of the Galactic Center ChimneyX-rays from a Central “Exhaust Vent” of the Galactic Center Chimney
X-rays from a Central “Exhaust Vent” of the Galactic Center ChimneySérgio Sacani
 
Information science research with large language models: between science and ...
Information science research with large language models: between science and ...Information science research with large language models: between science and ...
Information science research with large language models: between science and ...Fabiano Dalpiaz
 
Fun for mover student's book- English book for teaching.pdf
Fun for mover student's book- English book for teaching.pdfFun for mover student's book- English book for teaching.pdf
Fun for mover student's book- English book for teaching.pdfhoangquan21999
 
Warming the earth and the atmosphere.pptx
Warming the earth and the atmosphere.pptxWarming the earth and the atmosphere.pptx
Warming the earth and the atmosphere.pptxGlendelCaroz
 
Lubrication System in forced feed system
Lubrication System in forced feed systemLubrication System in forced feed system
Lubrication System in forced feed systemADB online India
 
GBSN - Microbiology (Unit 6) Human and Microbial interaction
GBSN - Microbiology (Unit 6) Human and Microbial interactionGBSN - Microbiology (Unit 6) Human and Microbial interaction
GBSN - Microbiology (Unit 6) Human and Microbial interactionAreesha Ahmad
 
Quantifying Artificial Intelligence and What Comes Next!
Quantifying Artificial Intelligence and What Comes Next!Quantifying Artificial Intelligence and What Comes Next!
Quantifying Artificial Intelligence and What Comes Next!University of Hertfordshire
 
PHOTOSYNTHETIC BACTERIA (OXYGENIC AND ANOXYGENIC)
PHOTOSYNTHETIC BACTERIA  (OXYGENIC AND ANOXYGENIC)PHOTOSYNTHETIC BACTERIA  (OXYGENIC AND ANOXYGENIC)
PHOTOSYNTHETIC BACTERIA (OXYGENIC AND ANOXYGENIC)kushbuR
 
Mining Activity and Investment Opportunity in Myanmar.pptx
Mining Activity and Investment Opportunity in Myanmar.pptxMining Activity and Investment Opportunity in Myanmar.pptx
Mining Activity and Investment Opportunity in Myanmar.pptxKyawThanTint
 

Recently uploaded (20)

FORENSIC CHEMISTRY ARSON INVESTIGATION.pdf
FORENSIC CHEMISTRY ARSON INVESTIGATION.pdfFORENSIC CHEMISTRY ARSON INVESTIGATION.pdf
FORENSIC CHEMISTRY ARSON INVESTIGATION.pdf
 
HIV AND INFULENZA VIRUS PPT HIV PPT INFULENZA VIRUS PPT
HIV AND INFULENZA VIRUS PPT HIV PPT  INFULENZA VIRUS PPTHIV AND INFULENZA VIRUS PPT HIV PPT  INFULENZA VIRUS PPT
HIV AND INFULENZA VIRUS PPT HIV PPT INFULENZA VIRUS PPT
 
TEST BANK for Organic Chemistry 6th Edition.pdf
TEST BANK for Organic Chemistry 6th Edition.pdfTEST BANK for Organic Chemistry 6th Edition.pdf
TEST BANK for Organic Chemistry 6th Edition.pdf
 
EU START PROJECT. START-Newsletter_Issue_4.pdf
EU START PROJECT. START-Newsletter_Issue_4.pdfEU START PROJECT. START-Newsletter_Issue_4.pdf
EU START PROJECT. START-Newsletter_Issue_4.pdf
 
Efficient spin-up of Earth System Models usingsequence acceleration
Efficient spin-up of Earth System Models usingsequence accelerationEfficient spin-up of Earth System Models usingsequence acceleration
Efficient spin-up of Earth System Models usingsequence acceleration
 
Film Coated Tablet and Film Coating raw materials.pdf
Film Coated Tablet and Film Coating raw materials.pdfFilm Coated Tablet and Film Coating raw materials.pdf
Film Coated Tablet and Film Coating raw materials.pdf
 
PARENTAL CARE IN FISHES.pptx for 5th sem
PARENTAL CARE IN FISHES.pptx for 5th semPARENTAL CARE IN FISHES.pptx for 5th sem
PARENTAL CARE IN FISHES.pptx for 5th sem
 
Heat Units in plant physiology and the importance of Growing Degree days
Heat Units in plant physiology and the importance of Growing Degree daysHeat Units in plant physiology and the importance of Growing Degree days
Heat Units in plant physiology and the importance of Growing Degree days
 
Factor Causing low production and physiology of mamary Gland
Factor Causing low production and physiology of mamary GlandFactor Causing low production and physiology of mamary Gland
Factor Causing low production and physiology of mamary Gland
 
Adaptive Restore algorithm & importance Monte Carlo
Adaptive Restore algorithm & importance Monte CarloAdaptive Restore algorithm & importance Monte Carlo
Adaptive Restore algorithm & importance Monte Carlo
 
RACEMIzATION AND ISOMERISATION completed.pptx
RACEMIzATION AND ISOMERISATION completed.pptxRACEMIzATION AND ISOMERISATION completed.pptx
RACEMIzATION AND ISOMERISATION completed.pptx
 
X-rays from a Central “Exhaust Vent” of the Galactic Center Chimney
X-rays from a Central “Exhaust Vent” of the Galactic Center ChimneyX-rays from a Central “Exhaust Vent” of the Galactic Center Chimney
X-rays from a Central “Exhaust Vent” of the Galactic Center Chimney
 
Information science research with large language models: between science and ...
Information science research with large language models: between science and ...Information science research with large language models: between science and ...
Information science research with large language models: between science and ...
 
Fun for mover student's book- English book for teaching.pdf
Fun for mover student's book- English book for teaching.pdfFun for mover student's book- English book for teaching.pdf
Fun for mover student's book- English book for teaching.pdf
 
Warming the earth and the atmosphere.pptx
Warming the earth and the atmosphere.pptxWarming the earth and the atmosphere.pptx
Warming the earth and the atmosphere.pptx
 
Lubrication System in forced feed system
Lubrication System in forced feed systemLubrication System in forced feed system
Lubrication System in forced feed system
 
GBSN - Microbiology (Unit 6) Human and Microbial interaction
GBSN - Microbiology (Unit 6) Human and Microbial interactionGBSN - Microbiology (Unit 6) Human and Microbial interaction
GBSN - Microbiology (Unit 6) Human and Microbial interaction
 
Quantifying Artificial Intelligence and What Comes Next!
Quantifying Artificial Intelligence and What Comes Next!Quantifying Artificial Intelligence and What Comes Next!
Quantifying Artificial Intelligence and What Comes Next!
 
PHOTOSYNTHETIC BACTERIA (OXYGENIC AND ANOXYGENIC)
PHOTOSYNTHETIC BACTERIA  (OXYGENIC AND ANOXYGENIC)PHOTOSYNTHETIC BACTERIA  (OXYGENIC AND ANOXYGENIC)
PHOTOSYNTHETIC BACTERIA (OXYGENIC AND ANOXYGENIC)
 
Mining Activity and Investment Opportunity in Myanmar.pptx
Mining Activity and Investment Opportunity in Myanmar.pptxMining Activity and Investment Opportunity in Myanmar.pptx
Mining Activity and Investment Opportunity in Myanmar.pptx
 

The Role of Machine Learning in Fluid Network Control and Data Planes.pdf

  • 1. The Role of Machine Learning in Fluid Network Control and Data Planes Prof. Dr. Christian Esteve Rothenberg University of Campinas, Brazil chesteve@dca.fee.unicamp.br https://intrig.dca.fee.unicamp.br/christian/ https://www.dca.fee.unicamp.br/~chesteve/ TEWI-Kolloquium, 13. October 2022
  • 2. Agenda Disclaimer “Fluid Network Planes” was first presented as a Keynote of IEEE NetSoft'19, Paris, Jun .2019. ● Fluid Network Planes ○ The ‘Concept’ ○ Instances ○ The Role of ML ● ML-based Youtube QoE estimation in real 5G networks
  • 3. 3 SDN in 2015 – 2020 → Network Softwarization (i.e. NFV + SDN + IBN + xyz) Old / Existing • CLIs & Manual labour • Closed Source • Vendor Lead • Classic Network Appliances (HW) New / Softwarized • APIs & Automation • Open Source • Customer Lead • Virtual Network Functions (NFV/SW) Source: Adapted from Kyle Mestery, Next Generation Network Developer Skills
  • 4. 4 Different Network Softwarization Models Control plane component(s) Canonical/Open Traditional Hybrid/Broker Overlay Compiler Source: C. Rothenberg (INTRIG/UNICAMP) Whitebox / Baremetal + PISA / P4 Data plane component(s)
  • 5. 5 Legacy Models & Approaches to Program / Refactor the Netsoft Stack Data Plane Mgm.APIs Distributed L2/L3 Control Plane Managemt Software Southbound Agent (e.g. OF) Network Controller / OS Southbound Protocol (e.g. OF) Business / Control Apps Northbound APIs Mgm. HAL APIs / Drivers Orchestrator (SO/RO/LCM) APIs Compiler Auto-Generated Target Binary SDN VNF GP-CPU (x86, ARM) HW Resources Virtualization DP CP M g m NFV VNFM (Manager) VIM (Infra-M) OSS/BSS APIs Southbound APIs/Plugins Mgm. Apps Network OS / Bare Metal Switches Source: C. Rothenberg (INTRIG/UNICAMP)
  • 7. The Fluid Networking landscape Customer Premises BS MEC | Access Cloud | PoP DC Edge Cloud DCs Core HW SW + -
  • 8. The Fluid Networking landscape Customer Premises BS MEC | Access Cloud | PoP DC Edge Cloud DCs Core HW SW + -
  • 9. Fluid Networking: HW-SW Continuum Performance Portability Programmability HW SW Source: D. Meyer (Courtesy by J. Doyle) Source: G. Pongracz. "Cheap silicon". HotSDN13 Source: C. Rothenberg. P3 Trade-offs. 2017
  • 10. HW SW • General-purpose CPU • HW-accelerated features** • FPGA • GPU, TPU, • Programmable NIC, ASIC • Domain Specific Architectures (DSAS) e.g., P4 + PISA • Containers • User space • Kernel space • Drivers, I/O SDKs Flexibility* (programmability + portability) Performance*** * M. He et al. Flexibility in Softwarized Networks: Classifications and Research Challenges. IEEE Survey & Tutorials, 2019 *** G. Bianchi. Back to the Future: Hardware-specialized Cloud Networking. 2019 ** Linguaglossa et al. Survey of Performance Acceleration Techniques for Network Function Virtualization. Proc. of IEEE, 2019 Fluid Networking: HW-SW Continuum
  • 11. Customer Premises BS MEC | Access Cloud | PoP DC Edge Cloud DCs Core + - Fluid Networking: Quest for Latency Source: Google Cloud Infrastructure
  • 12. Customer Premises BS MEC | Access Cloud | PoP DC Edge Cloud DCs Core + - Source: EU FP7 UNIFY Fluid Networking: Optimizing the E2E Compute Pool
  • 13. Fluid Networking: Decoupling functionality / location Customer Premises BS MEC - Access Cloud - PoP DC Edge Cloud DCs Core + - Latency Capacity Cost Source: EU Superfluidity
  • 14. The Fluid Networking landscape Customer Premises BS MEC - Access Cloud - PoP DC Edge Cloud DCs Core HW SW Optimize for Latency (Latency-sensitive Source to Function) Optimize for Performance/Cost Control plane component(s) Data plane component(s)
  • 15. The Fluid Networking landscape Customer Premises BS MEC - Access Cloud - PoP DC Edge Cloud DCs Core HW SW ? ? ? ?
  • 16. The Fluid Networking landscape Customer Premises BS MEC - Access Cloud - PoP DC Edge Cloud DCs Core HW SW ? ? ? ?
  • 17. The Fluid Networking landscape Customer Premises BS MEC - Access Cloud - PoP DC Edge Cloud DCs Core HW SW
  • 18. The Fluid Networking landscape Customer Premises BS MEC - Access Cloud - PoP DC Edge Cloud DCs Core HW SW
  • 19. The Fluid Networking landscape Customer Premises BS MEC - Access Cloud - PoP DC Edge Cloud DCs Core HW SW ML
  • 21. RouteFlow (2010 - ) Customer Premises BS MEC - Access Cloud - PoP DC Edge Cloud DCs Core HW SW legacy BGP IXP
  • 22. NFV layers of SW, Virtualization and HW platforms HW SW Source: https://www.dpdk.org/wp-content/uploads/sites/35/2018/12/Kalimani-and-Barak-Accelerating-NFV-with-DPDK-and-SmartNICs.pdf
  • 23. VNF offloading to Hardware HW SW Source: https://www.dpdk.org/wp-content/uploads/sites/35/2018/12/Kalimani-and-Barak-Accelerating-NFV-with-DPDK-and-SmartNICs.pdf
  • 24. HW SW VNF offloading to Hardware Source: https://www.dpdk.org/wp-content/uploads/sites/35/2018/12/Kalimani-and-Barak-Accelerating-NFV-with-DPDK-and-SmartNICs.pdf
  • 25. VNF offloading on multi-vendor P4 fabric controlled by ONOS via P4Runtime HW SW Source: https://p4.org/assets/P4WS_2018/7_Carmelo_Cascone_VNF.pdf
  • 26. Slicing IoT Analytics Access Cloud uRLLC GW eMBB GW vCDN Edge Cloud Central Cloud 5G CP mMTC vCDN MEC/vCDN Internet Internet Midhaul 5G AAU 5G DU 5G CU Fronthaul 5G AAU Transport Network Slicing Slice 1: 100M, eMBB Slice 2: 1G, Video Slice 3: 90G Other Slices Slice 1: 100M, eMBB Slice 2: 1G, Video Slice 3: 90G Other Slices Transport Network Slicing Cloud – Slice Orchestr & Management System Network – Slice Orchestr & Management System DoJoT DoJoT DoJoT IoT Monitoring & Control Slice 5: mIoT – computing intensive Slice 4: mIoT – latency sensitive Source: http://www.h2020-necos.eu/
  • 27. In-Network Computing * D. Ports and J. Nelson. When Should The Network Be The Computer?. HotOS'19 IRTF Computation in the Network (COIN)
  • 28. J. Vestin et al. In-Network Control and Caching for Industrial Control Networks using Programmable Data Planes. 2018
  • 29. X. Jin et al. Netcache: Balancing key-value stores with fast in-network caching. SOSP'17
  • 30. H. Tu Dang et al. P4xos: Consensus as a Network Service. 2018
  • 31. SwitchML: the network is the ML accelerator A. Sapio et al. Scaling Distributed Machine Learning with In-Network Aggregation. 2019
  • 32. E. Costa Molero et al. Hardware-Accelerated Network Control Planes. HotNets'18 * T. Holterbach et al. Blink: Fast Connectivity Recovery Entirely in the Data Plane. NSDI'19 Control plane functions offloaded to Data plane / HW
  • 33. S Chinchali et al. Network Offloading Policies for Cloud Robotics: a Learning-based Approach. Auton Robot 45, 997–1012 (2021). Robot Offloading Problem
  • 34. Robotic Arm Controller Offloading Low Latency Industrial Robot Control in Programmable Data Planes • P4 data plane • parses robot 3D coordinates • detects “stop region” • accelerates stop command by crafting a stop message inside the TCP connection F. Rodriguez et al. ."Towards Low Latency Industrial Robot Control in Programmable Data Planes". In IEEE NetSoft 2020, Ghent, Belgium, June 2020
  • 35. Robotic Arm Controller Offloading In-Network Centralized Done Collision Avoidance Algorithm in Programmable Data Planes • P4 Workshop 2021 (Award Winner of most novel non-networking use of P4)
  • 36. ML & Fluid Network Planes
  • 37. ML for Networking A myriad of recent works (as in all areas, not just networking) • Boutaba, R., Salahuddin, M.A., Limam, N. et al. “A comprehensive survey on machine learning for networking: evolution, applications and research opportunities”. JISA, 2018. • M. Usama et al., "Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges," in IEEE Access, vol. 7, pp. 65579-65615, 2019,. • J. Xie et al., "A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges," in IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 393-430, 2019. • M. A. Ridwan, N. A. M. Radzi, F. Abdullah and Y. E. Jalil, "Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges," in IEEE Access, vol. 9, pp. 52523-52556, 2021 • ...
  • 38. ML for Networking Source: Boutaba, R., Salahuddin, M.A., Limam, N. et al. “A comprehensive survey on machine learning for networking: evolution, applications and research opportunities”. JISA, 2018.
  • 39. ML applications Fluid Network Planes • (design time) Optimization – HW parameters / confirmation – SW code optimization (e.g., P4 compiler) • (offline) Virtual Network embedding like problems – P4 code target selection – HW offloading / acceleration – VNF Placement process – Network Service Chain placement – HW/SW Performance estimation • (online) Run-time operations – HW/SW Performance prediction • QoS / SLA violation prediction • Scale up/down & out/in recommendations (Dev/Ops) Learning
  • 40. ML applications Fluid Network Planes In-network Machine Learning moving to the edge Machine Learning offloading strategies: Design space for offloading machine learning inference tasks from the end-host’s CPU. Siracusano, Giuseppe, et al. "Running neural networks on the NIC." https://arxiv.org/abs/2009.02353
  • 42. ML applications Fluid Network Planes ETSI ZSM Closed Loop Automation • N. Sousa et al.,"Machine Learning-Assisted Closed-Control Loops for Beyond 5G Multi-Domain Zero-Touch Networks". In Journal of Network and Systems Management (JNSM), Special Issue on Network Management in beyond 5G Systems, 2022
  • 43. ML applications Fluid Network Planes ETSI ZSM Closed Loop Automation • Nathan F. Saraiva, Christian E. Rothenberg. "CLARA: Closed Loop-based Zero-touch Network Management Framework". In IEEE NFV-SDN Doctoral Symposium, Nov. 2021. (Best Doctoral Symposium Paper Award)
  • 44. Intent-based CCL for DASH Video SLA using ML-based Edge QoE Estimation Recent and ongoing research C. Rothenberg et al. "Intent-based Control Loop for DASH Video Service Assurance using ML-based Edge QoE Estimation". In IEEE NetSoft 2020 Demo Session, Ghent, Belgium, June 2020.
  • 45. Intent-based CCL for DASH Video SLA using ML-based Edge QoE Estimation Recent and ongoing research R. Mustafa et al. . "DASH QoE performance evaluation framework with 5G datasets". In IEEE CNSM AnServApp. Nov. 2020.
  • 46. Recent and ongoing research https://github.com/razaulmustafa852/EFFECTOR DASH QoE estimation from QoS-level metrics in 4G/5G scenarios
  • 47. Recent and ongoing research https://github.com/razaulmustafa852/EFFECTOR DASH QoE estimation from QoS-level metrics in 4G/5G scenarios
  • 48. ML-based Youtube QoE estimation in real 5G networks
  • 49. Recent and ongoing research Real 4G/5G trace collection in Youtube streaming • Data sets to become public for open & reproducible research Research Questions • Evaluation of Youtube QoE KPIs for different mobile networks. ie. how does Youtube perform in 5G (DSS vs NSA vs SA) compared to 4G? • Can we predict future (e.g. 5 sec) Youtube QoE KPIs based on network-level metrics (radio parameters and flow-level?) – What to do after predicting a QoE downgrade (e.g., stalls, shift down)?
  • 50. Recent and ongoing research Real 4G/5G trace collection in Youtube streaming • completed in Nice, France (4G vs 5G DSS) • starting in Campinas, Brazil (4G vs 5G DSS) and Sao Paulo (5G NSA vs SA)
  • 51. Detail of the solution (3/4) Data Collection Use Cases 1) Pedestrian – Low Mobility 2) Driving – High Mobility 3) Static – Bus and Railway Terminals 4) Static Outdoor – High Crowd
  • 52. Dataset Statistics Mobility – Total Kilometers 300 + Pedestrian – Total Kilometers 100 + Number of Videos 10 Total Video Sessions 300 +, 1500 + Minutes Streaming 4G and 5G Data Consumed 300 + GB 5G Smartphone Samsung Galaxy S21 5G 4G Smartphone Samsung Galaxy S8
  • 53. Results (1/4): 4G vs. 5G - CQI, RSRP, RSRQ, SNR
  • 54. Results (2/4): 4G vs. 5G - CQI, RSRP, RSRQ, SNR • We see a nice CQI (CQI is a feedback provided by UE to eNodeB), RSRP (RSRP is used for measuring cell signal strength/coverage and therefore cell selection (dBm)) at Bus and Railway Terminals, however, a massive difference RSRQ for Mobility, Pedestrian in 4G. • 5G shows a similar pattern, but we see an opposite effect of RSRQ for Mobility and Pedestrian • Moreover, we experience better CQI in 5G as compared to 4G.
  • 55. Results (3/4): 4G vs. 5G – Case Mobility Stalling Events - Handover o 5G in mobility suffers more stalling events as compared to 4G. o 5G also experience more handover events. o In 5G, it continues streaming at higher resolutions, even there are stalling events as compared to 4G, where we see multiple quality shifts. o Most handover occurs at CQI value 6. 4G vs. 5G QoE
  • 56. Results (4/4): 4G vs. 5G – Resolutions (Mobility Cases) o Mobility - High: 4G experiences multiple quality shifts during a video session, thus less stalling events, but 5G it is more greedy with maximum resolutions, thus cause stalls. o Mobility – Low: 4G suffers with quality shifting as compared to 5G. We see 95.3% hd2160 resolution in 5G. o Static cases experience more stability in both technology, i.e., hd1440 (10%) and hd2160 (90 %) in 4G and in 5G hd2160 (100%) 4G vs. 5G Resolutions Mobility - High Mobility - Pedestrian
  • 57. Prev n-seconds Experience ● Look at the previous n-seconds experience ● Look at the application QoE, and when interruption came, see the previous n-second channel metrics ○ Quality Shifts ○ Stalls
  • 60. References ● Kaljic, Enio, et al. "A Survey on Data Plane Flexibility and Programmability in Software-Defined Networking." arXiv preprint arXiv:1903.04678 (2019). ● L. Linguaglossa et al., "Survey of Performance Acceleration Techniques for Network Function Virtualization," in Proceedings of the IEEE, vol. 107, no. 4, pp. 746-764, April 2019. ● Edgar Costa Molero, Stefano Vissicchio, and Laurent Vanbever. 2018. Hardware-Accelerated Network Control Planes. In Proceedings of the 17th ACM Workshop on Hot Topics in Networks (HotNets '18). ACM, New York, NY, USA, 120-126. ● Huynh Tu Dang, Marco Canini, Fernando Pedone, and Robert Soulé. “Paxos Made Switch-y.” In ACM SIGCOMM Computer Communication Review (CCR). April 2016. ● JIN, Xin et al. Netcache: Balancing key-value stores with fast in-network caching. In: Proceedings of the 26th Symposium on Operating Systems Principles. ACM, 2017 ● Yuta Tokusashi, Huynh Tu Dang, Fernando Pedone, Robert Soulé, and Noa Zilberman. “The Case For In-Network Computing On Demand.” In European Conference on Computer Systems (EuroSYS). March 2019.
  • 61. References ● D. Ports and J. Nelson. When Should The Network Be The Computer?. In Proceedings of the Workshop on Hot Topics in Operating Systems (HotOS '19) ● Atul Adya, Robert Grandl, Daniel Myers, and Henry Qin. 2019. Fast key-value stores: An idea whose time has come and gone. In Proceedings of the Workshop on Hot Topics in Operating Systems (HotOS '19) ● Theophilus A. Benson. 2019. In-Network Compute: Considered Armed and Dangerous. In Proceedings of the Workshop on Hot Topics in Operating Systems (HotOS '19) ● Theo Jepsen, Daniel Alvarez, Nate Foster, Changhoon Kim, Jeongkeun Lee, Masoud Moshref, and Robert Soulé. 2019. Fast String Searching on PISA. In Proceedings of the 2019 ACM Symposium on SDN Research (SOSR '19) ● Thomas Holterbach, Edgar Costa Molero, Maria Apostolaki, Alberto Dainotti, Stefano Vissicchio, Laurent Vanbever. Blink: Fast Connectivity Recovery Entirely in the Data Plane. NSDI 2019. ● A. Sapio, M. Canini, C.-Y. Ho, J. Nelson, P. Kalnis, C. Kim, A. Krishnamurthy, M. Moshref, D. R. K. Ports, P. Richtarik. Scaling Distributed Machine Learning with In-Network Aggregation. KAUST
  • 62. References ● A. Sapio et al. Scaling Distributed Machine Learning with In-Network Aggregation. 2019. ● Huynh Tu Dang, Pietro Bressana, Han Wang, Ki Suh Lee, Hakim Weatherspoon, Marco Canini, Fernando Pedone, Noa Zilberman, Robert Soulé, "P4xos: Consensus as a Network Service", Tech Report, University of Lugano 2018/01, May 2018 ● H. Tu Dang et al. P4xos: Consensus as a Network Service. 2018 ● Raphael Rosa and Christian Esteve Rothenberg. "The Pandora of Network Slicing: A Multi-Criteria Analysis". ETT. 2019 ● J. Vestin, A. Kassler, J. Åkerberg, FastReact: In-Network Control and Caching for Industrial Control Networks using Programmable Data Planes. In 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation September 4th - 7th, 2018, Torino, Italy.
  • 63. Credits ● http://www2.technologyreview.com/article/412194/tr10-software-defined-networking/ ● Fluid 1 image source: https://www.trzcacak.rs/detail/199233/ ● Fluid 2 image source: http://www.pngall.com/water-png/download/1933 ● Intelligent Brain image source: https://ui-ex.com/explore/transparent-brain-artificial-intelligence/ ● Orchestrator image source: https://apievangelist.com/2015/02/06/when-you-are-ready-for-nuanced-discussion-about-who-has-a ccess-to-your-api-i-am-here/ ● Poison image source: https://www.stickpng.com/cat/miscellaneous/poison?page=1
  • 66. E2E CCL Recent and ongoing research • Technique for Simplifying Management of a Service in a Cloud Computing Environment. PCT/EP2019/058274. 2019 ● Service-specific metrics: ○ Per domain (e.g., cloud and transport) ○ E2E view (cloud + transport) ○ Represented by Graphs ○ Stored into Graph-based Database (Neo4J)
  • 67. Detail of the solution (1/4) 4G and 5G dataset collection using the most popular video streaming website YouTube with 1-second granularity. YOUTUBE FEATURES: ❑ Stalls, Resolution, Video Bytes Downloaded that can further provide per-session Objective QoE (i) Total Stalling Event, ii) Stalling Ratio, iii) Stalling Time, iv) Quality Shifts or Percentage of Time in a single Resolution, v) Dominant Resolution, etc.
  • 68. Detail of the solution (2/4) CHANNEL LEVEL METRICS 1-second granularity, few features below ❑ Timestamp, Longitude, Latitude, Velocity, Operator Name, Cellid, Network Mode, Download bitrate, Upload bitrate, RSRQ, RSRP, SNR, RSSI, CQI, RSRQ and RSRP values for the neighbouring cell, among 100 + other channel metrics *. * https://gyokovsolutions.com/manual-g-nettrack/
  • 69. Detail of the solution (4/4) • YouTube IFRAME API for YouTube QoE Logs • G-NetTrack Pro - Wireless network monitor and drive test tool