This document presents an approach called DICES for dynamically adapting software-defined networks to control congestion in IoT systems like an emergency management system. DICES uses SDN to monitor network traffic, analyzes congestion using a threshold, computes optimal traffic flow reconfigurations using a multi-objective search algorithm to minimize utilization, cost and delay, and applies the reconfiguration through SDN. An evaluation on an emergency management system case study shows DICES significantly outperforms baseline algorithms by transmitting data at least 3 times faster and reducing data loss by at least 70%.

1. .lusoftware verification & validation
VVS
Dynamic Adaptation of
Software-defined Networks for IoT Systems:
A Search-based Approach
Seung Yeob Shin1, Shiva Nejati2,1, Mehrdad Sabetzadeh2,1, Lionel C. Briand1,2,
Chetan Arora4,3,1, and Frank Zimmer3
1 University of Luxembourg, Luxembourg
2 University of Ottawa, Canada
3 SES Networks
4 Deakin University, Australia
SEAMS 2020

2. Internet of Things (IoT)
• Components
• Things and connectivity
• Why IoT is important?
• Intelligent systems
• Dynamic services
• Motivating case study
• Emergency Management System (EMS)
2
IoT

3. Motivating Case Study:
Emergency Management System
• Things
• Monitoring sensors
• Emergency monitoring centers
• Mobile communication facilities
• Satellite ground stations
• Connectivity
• Terrestrial network
• Satellite network
3

4. Motivating Case Study:
Emergency Management System
• Large volume of data
• Environmental uncertainty
• Risks of congestion
4

5. Large Volume of Data
• Monitored data streams
• High-bandwidth demanding video/audio streams
• Earth-observation images
5
data

6. Unpredictable and Changing
Environments
• Natural or man-made emergencies
• Increased volume of communication demand
➤Highly prone to congestion
6
tsunami

7. Problems Induced by Congestion
• Increased latency
• Information loss
• Inability to communicate with one or more sites
➤Life-threatening consequences
7

8. Problem Statement
How to resolve congestion?
(1) in an online manner
(2) while minimizing delays and costs
8
Large Volume of Data
• Monitored data streams
• High-bandwidth demanding video/audio streams
• Earth-observation images
data
Unpredictable and Changing
Environments
• Natural or man-made emergencies
• Increased volume of communication demand
➤Highly prone to congestion
tsunami
Problems Induced by Congestion
• Increased latency
• Information loss
• Inability to communicate with one or more sites
➤Life-threatening consequences

9. Software Engineering Challenges
• Managing potentially conflicting objectives
• Utilization, delay, and cost
• Taking time constraints into account
• Ensuring the usefulness of adaptation
• Achieving higher adaptivity
• Online optimization
9

10. DICES: Dynamic adaptIve CongEstion
control algorithm for SDN

11. Approach Overview
11
monitor
analyze
compute
apply SDN
user
environment
≪observes≫
≪reconfigures≫
dynamic
adaptive
configuration

12. Software Defined Network (SDN)
• Main characteristics
• Centralized and programmable
software controller
• Why SDN is important?
• Enabling flexible adaptation of
SDN in response to
environment changes
12
Traditional network
Software defined network

13. SDN Architecture
infrastructure layer
linkswitch
forwarding
application
control layer
failure
management
data
flow servicetopology service link serviceswitch service
SDN controller
≪controls via network interface≫
≪manipulates via application interface≫
security
analyzer
application layer
in
out
1..*
1..*≪forwards≫
network
1..* *

14. SDN Applications
• Abstracted network
topology
• Domain-specific data
forwarding
14
infrastructure layer
linkswitch
forwarding
application
control layer
failure
management
data
flow servicetopology service link serviceswitch service
SDN controller
≪controls via network interface≫
≪manipulates via application interface≫
security
analyzer
application layer
in
out
1..*
1..*≪forwards≫
network
1..* *

15. DICES: Dynamic Adaptive
Congestion Control
15
monitor
analyze
compute
apply SDN
user
environment
≪observes≫
≪reconfigures≫
dynamic
adaptive
configuration

16. Monitor Network
• Periodic task to monitor network traffic flows
• Monitored information
• Network topology
and capacity
• Sender and receiver
• Traffic volume over
monitoring period
16
flow 1
flow 2
flow 3

17. Analyze Congestion
• Threshold-based congestion decision
IF a link utilization > threshold
THEN run the compute step
17
Utilized over 80%

18. Compute Reconfiguration
• Search-based congestion control algorithm
• Multiple objectives
• Utilization: Minimize the maximum link utilization across all
the links in a network
• Cost: Minimize the number of link updates
• Delay: Minimize the overall data transmission delays
18

19. Multi-objective Search
19
NSGA II
Evaluation
Crossover
Selection
Mutation
Initial solutions
A search algorithm
inspired by the process
of natural selection

20. Multi-objective Search
• Input
• A set of monitored traffic flows
• Output
• Sets of reconfigured traffic flows
• Optimizing the three objectives:
utilization, cost, and delay
20
flow 1
flow 2
flow 3

21. Equally Viable Solutions
• A set of non-dominated solutions
• The best tradeoffs found among the given three objectives:
utilization, cost, and delay
21
flow 1
flow 2
flow 3
best solution 1:
utilization
best solution 2:
cost
input flows

22. Selecting an Optimal Solution
• Selecting one solution to be used
for reconfiguration
• In our context, a knee solution
• Other solutions are biased
toward any objective
22
fitA()
fitB()
0
0
1
1
corner
knee
ideal
corner
Pareto front
solution

23. Apply Reconfiguration
• Reconfiguring traffic flows
• Reconfiguring link weights
23
monitor
analyze
compute
apply SDN
user
environment
≪observes≫
≪reconfigures≫
dynamic
adaptive
configuration

24. Empirical Evaluation
RQ1 (efficiency and effectiveness): Can our approach resolve
congestion caused by changes in network requests over time?
RQ2 (scalability): Can our approach resolve congestion
promptly for large-scale networks?
RQ3 (comparison with baselines): How does our approach
perform compared with baseline approaches?
24

25. EMS Characteristics: Topology

26. EMS Characteristics: Links
• Terrestrial links: 100 Mbps bandwidth and 25ms delay
• Satellite links: 10 Mbps bandwidth and 275ms delay
s1 s2 s3 s4 s5
s6 s7

27. Traffic Profile for
a Disaster Situation
RM (remote monitoring site), MC (emergency monitoring center),
CS (mobile communication facility site), ENN (external networks in normal areas), and
END (external network in a disaster area).
27
EMS entity Flow characteristics
Sender Receiver Type Protocol Throughput # Flows
RM MC Sensor TCP 100 Kbps 5
CS MC Audio UDP 64 Kbps 4
CS MC Video UDP 10 Mbps 2
MC CS Audio UDP 64 Kbps 4
MC CS Video UDP 10 Mbps 2
MC CS Map TCP 30 Mbps 1
ENN END External UDP 20 Mbps 5
END ENN External UDP 20 Mbps 5

28. Comparison with Baselines
• Reactive forwarding algorithm (RFWD)
• Shortest paths by hop-count
• Predefined reactive data forwarding application in ONOS
• Open shortest path first forwarding algorithm (OSPF)
• Weighted shortest paths
• Commonly used as a baseline for SDN research
28

29. Comparison Results
• DICES significantly outperforms
the baseline algorithms
• Data transmission rate:
At least thee times faster
• Data loss rate:
At least 70% reduced
29

30. Conclusions
• Dynamic adaptive congestion control algorithm
for SDN
• Multiple-objective search-based approach
• Industrial case study
• Emergency management system
• Comparison with baselines
• ▲data transmission rate and ▼data loss
30
Approach Overview
9
EMS Characteristics: Topology
Comparison Results
• DICES significantly outperforms
the baseline algorithms
• Data transmission rate:
At least thee times faster
• Data loss rate:
At least 70% reduced
31