1. Smart levee monitoring and flood decision support system: reference architecture and urgent computing management
Bartosz Baliś, Tomasz Bartynski, Marian Bubak, Daniel Harezlak,
Marek Kasztelnik, Maciej Malawski, Piotr Nowakowski, Maciej Pawlik, Bartosz Wilk
AGH University of Science and Technology
Department of Computer Science
Kraków, Poland
ICCS 2015, Reykjavik, Iceland

2. Agenda
Motivation
ISMOP project: IT system for smart levee monitoring and flood decision support
Holistic approach to urgent computing management
I will present the broader context of this research which is the ISMOP project.

3. Motivation Predict levee failures using smart levees
Typical flood scenario in Lesser Poland region: flood threat due to passing water wave
High water levels lasting up to several weeks
Need for urgent computing in large-scale scenarios

4. Levee monitoring: traditional approach
Piezometer: an instrument for measuring the pressure of a liquid or gas, or something related to pressure (such as the compressibility of liquid). Piezometers are often placed in boreholes to monitor the pressure or depth of groundwater.

5. Smart levees
Sensor monitor
temperature
pore pressure

6. Data analysis (1): anomaly detection
trend
anomaly

7. Data analysis (2): threat estimation via numerical modeling
Prediction of levee response to certaing water levels in current external conditions
Numerical models require geotechnical and geophysical levee examination

8. ISMOP project overview
ISMOP: an IT system for smart levee monitoring and flood decision support
Construction of an experimental levee
Design and installation of sensors
Innovative telemetry system
Model- and data-driven modeling of levee behavior
Monitoring and decision support system
We are addressing such flood threat scenarios in the research project ISMOP
(TODO: NCBIR)

9. ISMOP experimental smart levee
Size: 200mx50mx4m, 4 types of material 1200 sensors (temperature and pore pressure) Two optic fibre sensors

10. Controlled flooding experiments

11. Urgent computing scenario
Assess flood threat risk for large area (50+ km) of levees
Compute results by a specified deadline
Provide levee health maps for all levee sections
The urgent computing scenario occurs when we have to deal with a larger scale
The water wave can be quite long (50, 100km)
Even though we’re doing research on a small experimental levee, we’re designing the system to scale…
We divide the levees into logical sections. We assume in each there are sensors.

12. ISMOP IT system for smart levee monitoring and flood decision support

13. Decision support workflow
Multiple tiers of data analysis, activated as alert level rises
Anomaly detection: 1st indication about a potential problem
Threat estimation: more in-depth analysis of anomalous sections in order to assess threat of levee failure (e.g. levee breach simulations)
Risk assessment: estimate impact of levee breach (e.g. simulations of inundation, crowd behavior)

14. Decision support workflow: implementation

15. Holistic approach to urgent computing management
System reconfiguration loop
Isolated approach
Holistic approach

16. System management
SLA (service-level agreement): requried level of quality of service the system has to deliver
SLAs become optimization objectives
SLAs vary depending on system mode
Normal mode: conserve energy, save costs
Urgent mode: high performance, high accuracy of data measurement and analysis, deadlines

17. IT system for smart levees: reference architecture and objective functions
OPC (Operating Cost) SLT (System Lifetime) EE (Energy Efficiency) DPI (Data Processing Interval) DPT (Data Processing Time) DAT (Data Access Time) DTI (Data Transmission Interval) DMI (Data Measurement Interval)

18. Configurable properties

19. Service profiles in normal and urgent modes
Service profile defines:
SLAs: functional constraints imposed upon the objective functions
Trade-offs: which objectives should be preferred?

20. Objective functions models
Based on experimental research and literature review, we have created approximate models of three objective functions:
Function q1: OPC (Operating Cost)
Function q2: EE (Energy Efficiency)
Function q3: TML (Timeliness – aggregated measure of system performance and responsiveness)
where qi: (s,c)  [0,1]
s: system configuration (a vector of configuration options for all system components)
c: system context (e.g. weather conditions)
0: minimal possible value, 1: maximal possible value

21. Decision support workflow: implementation

22. Holistic urgent computing management: algorithm

23. Results
Pareto-optimal solutions, normal mode

24. Results
Pareto-optimal solutions, urgent mode

25. Conclusion
Holistic urgent computing management leads to more optimized system configuration than the isolated approach
Future work:
Evaluate public cloud services for urgent computing