This document describes a system that uses big data telemetry from networks to enable trend-based networking decisions in SDN and traditional networks. It presents the design of the system, including functional block diagrams and descriptions of the design solution and its levels. It also discusses the implementation including data collection, processing, storage and analysis, and how trends are identified and used to make network configuration changes to balance traffic load. Future work areas are identified such as using machine learning on historical data and connecting traditional and SDN network topologies in hybrid network configurations.

1. IJNGN - Vol.11, No.1, March 2019
TREND-BASED NETWORKING
DRIVEN BY BIG DATA TELEMETRY FOR SDN AND
TRADITIONAL NETWORKS
JAIN, Ankur
GEDIA, Dewang
GUPTA, Arohi
GUPTA, Ashutosh
MURTHY, Sanjay
PÉREZ, Leidy
PERIGO, Levi
Authors
DOI : 10.5121/ijngn.2019.11101
International Journal of Next - Generation Network
( IJNGN)
http://www.airccse.org/journal/ijngn/ijngn.html
ISSN : 0975-7023 Online); 0975-7252 ( Print )
http://aircconline.com/ijngn/V11N1/11119ijngn01.pdf

2. Future Work
3
4
AGENDA
1
2
System Overview
Design Solution
Implementation
5
Q&A

3. To design an intelligent system that leverages
Big Data Telemetry Analysis in PNDA
(Platform for Network Data Analytics) to enable
comprehensive trend-based networking
decisions.
System Overview
Purpose
4 51 32
Automation and Orchestration
Shortcomings of Existing Solutions
Network Management Challenges
Impact
Purpose and
Impact

4. Design Solution
Functional
Block
Diagram
4 51 32

5. 1
Design Solution
4 51 32

6. 2
Design Solution
4 51 32

7. 3
Design Solution
Level
1
4 51 32

8. 4
Design Solution
Level
2
4 51 32

9. 5
Design Solution
4 51 32

10. TREND - LINK CONGESTIONBEFORE
LOAD BALANCEDAFTER
Tests Results End-to-End Testing: Traditional
4 51 32
Full System

11. TREND - LINK CONGESTIONBEFORE
LOAD BALANCEDAFTER
Tests Results End-to-End Testing: SDN
4 51 32
Full System

12. 4 51 32
Future Work
Future Work
Network Metrics
used for trend
identification
CPU Utilization
Bandwidth
Latency
Machine Learning
for network trends
Trend analysis historical
data enables machine
learning capabilities
Predict vs React
Multipath Load
Balancing vs prefix
load balancing
Implement Multipath
BGP to enhance load
balancing capabilities
Traditional and
SDN network
configurations
Connect Traditional
and SDN topologies
Hybrid networks

13. THE END
4 51 32
Q&A

14. Appendix

15. Appendix
Requirements
1 2 3 4 5
 Design SDN
and
Traditional
topologies
 Set up data
collection
protocols
 .py for SDN
 CollectD for
Traditional
 Deploy Indexing
Instance
 Write
configuration files
for input
 Design filters for
relevant data
points
 Link between VM
and Kafka
 Integrate CollectD
and Logstash
 Integrate Logstash
and PNDA
 Design algorithm to
aggregate data to big
data analytics tool for
processing, initial
analysis and storage.
 Setting up streaming
context
 Jupyter notebook to read
from Kafka to
OpenTSDB
 Implement
statistical analysis
for benchmarking
and threshold
setting.
 Two integrated
algorithms to
perform all tasks
 Layer trend
identification to
statistical analysis.
 Design reactive
algorithm to push
networking decisions
 Push route maps to
Traditional
 Push flows to SDN

16. Benchmarking
Data pushed to TSDB through Jupyter
TSDB rest API call from browser to retrieve samples
Traditional: OutOctets sample SDN: Tx_bytes sample
iPerf Traffic
Appendix

17. Trend-Based Decision
SDN Flows pushed to rereoute traffic
Traditional Route Maps to reroute traffic
Load balacing
achieved
Before
After
Appendix

18. Test Case Components Verification Validation
Test Case
Results
Topology
Traceroute from R1 to
Host 1 and Host 2
Traffic has been divided
between two (2) unique
paths at the time of the
trending event
PASSED
GNS3 Network
CollectD
Indexing and Filtering
Logstash
PNDA
Kafka Interface
Jupyter Notebook
OpenTSDB - Impala
Back-End
Jupyter - GNS3
Appendix

19. Test Case Components Verification Validation
Test Case
Results
Topology
Flows on OVS reflect
network change
Successful PING: Host 3
to Host 5 and Host 5 to
Host 3
Host 1 has “out_port” of
‘2’
Host 5 has “out_port” of ‘3’
PASSED
RYU Controller
Mininet Topology
Indexing and Filtering
Data fetching script
Logstash
PNDA
Kafka Interface
Jupyter Notebook
OpenTSDB - Impala
Back-End
Jupyter – RYU - Mininet
Appendix