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Wireless Mesh Networks and Cloud Computing for Real Time Environmental Simulations
Dr. Eryk Schiller
CDS Group
Institute of Computer Science and Applied Mathematics
University of Bern
20 October 2014, CDS Group Seminar
OUTLINE
Personal Presentation,
The introduction to the A4-Mesh project,
eA4-Mesh, Swiss Academic Compute Cloud project, what next?
Short summary & results.
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MY CV
Graduated from the Networking Department, University of Science and Technology, Cracow, Poland, 2006 (MSc).
Graduated from the Theoretical Physics Department of the Jagiellonian University, Cracow, Poland, 2007 (MSc).
PhD in Computer Science from the University of Grenoble, France, 2010.
Post-Doc in the Distributed Computing Group of the University of Neuchatel, 2010 – 2014.
Recently joined the CDS Group in August 2014.
Please remember about the APERO, WED, 22/10/2014, Meeting Room!!
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CRACOW, POLAND
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GRENOBLE, FRANCE
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Grenoble
NEUCHATEL, SWITZERLAND
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Neuchatel
Switzerland
A4-MESH, EA4-MESH, SWISS ACC
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The following part of the presentation is about my work as a post-doc in the Distributed Computing Group of Peter Kropf at the University of Neuchatel, Switzerland.
Cooperation with CDS on various tasks & subjects.
WIRELESS MESH NETWORKS
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Application Scenarios
1. Campus Network Extension
2. Environmental Monitoring
ENVIRONMENTAL MONITORING
Support for scientists (hydrology researchers) to collect sensor data from environmental measurements.
Scientists use data for generating and verifying models of the environment.
Specific measurements to cover certain areas or to collect specific sensor data are needed.
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Requirements :
WMNS FOR ENVIRONMENTAL MONITORING
In remote locations, wired networks are not available,
We have thus to foresee some other access methods,
Cellular telephony has reception problems, equipment may thus not be placed appropriately,
Satellite access is very costly and requires substantial energy resources,
Wireless Mesh Networks (WMN) do not require substantial amounts of energy and therefore may operate solar powered in remote locations.
The installation process of WMNs is cheap, they do not require building expensive infrastructures,
Equipment is recyclable, i.e., we can take equipment from one location and reuse it in another setup.
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WMNS FOR ENVIRONMENTAL MONITORING
QuiRiNet : Design of the wide area WMN based system for environmental data harvesting
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Quail Ridge Reserve in Napa County, California, 3x4 km area
QuRiNet: A wide-area wireless mesh test-bed for research and experimental evaluations, D. Wu; D. Gupta and P. Mohapatra,
Ad Hoc Networks, Volume 9, Issue 7, pp. 1221–1237, 2011
WMNS FOR ENVIRONMENTAL MONITORING
Predicting Floods through Ensemble Kalman Filter based Modeling
Heterogeneous Data Harvesting: GSM, IEEE 802.11, IEEE 802.15.4
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Wrexham County Borough, Wales, UK, 53.10 N, 2.91 W
Towards the provision of site specific flood warnings using wireless sensor networks, P. J. Smith; D. Hughes; K. J. Beven; P.
Cross; W. Tych; G. Coulson and G. Blair, Meteorological Applications, pp. 57–64, 2009
The Emmental
characteristics:
• Watershed: 175 km2
• Abstraction: 640 m3/d
• Precipitation: 1370 mm/year
• Runoff: 8000 m3/d
drinking
water
station
DRINKING WATER SUPPLY FOR CITY OF BERN
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Graphics provided by D. Käser
RIVER EMME – A DYNAMIC ECOSYSTEM
Groundwater from the valley is used as a water source for the city of Bern,
Sometimes, sections of the river can run completely dry,
Subsequently, water pumping rates of the groundwater wells have to be reduced to comply with the environmental laws of Switzerland, which require minimal flows to be maintained.
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River Emme, Photos provided by D. Käser
GROUNDWATER-RIVER INTERACTIONS
Problems:
• Groundwater pump
management
• Variations in the river
flow rate
• Large water discharge
changes the river’s
structure
Hydrograph
1999 (m3/s)
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ENVIRONMENTAL MONITORING SCENARIOS
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A HARDWARE PLATFORM
ALIX3D2 provided by PCEngines.CH,
Contains an i586 compatible processor at 512 MHz,
256 MB DDR,
On-board CompactFlash card reader,
2 miniPCI slots,
2 USB ports,
1 Ethernet card 100 Mbit/s,
Power: 7 – 20 V.
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It is powerful enough to run embedded Linux
(e.g., OpenWRT, ADAM, etc.)
SOLAR POWERED NODES
A solar panel which is large enough can provide an uninterrupted operation of the Wireless Station at a remote location. Badaway et al.specify a Linear Program which provides the minimum solar panel size to maintain an uninterruptable operation of a solar-powered node.
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Alix3
D2Solar Panel Size
Solar Powered WLAN Mesh Network Provisioning for Temporary Deployments, G. H. Badawy; A. A. Sayegh and T. D.
Todd, IEEE WCNC 2008, pp. 2271–2276, 2008
HARVESTING ENERGY
Energy Measurements of battery power; a solar powered node in the A4-Mesh WMN in the Swiss Alps.
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Real-World Energy Measurements of a Wireless Mesh Network, A. Jamakovic; D. C. Dimitrova; M. Anwander; T. Macicas; T.
Braun; J. Schwanbeck; T. Staub and B. Nyffenegger, European Conference on Energy Efficiency in Large Scale Distributed
Systems, EE-LSDS, 2013
UNINTERRUPTED LONG TERM OPERATION
Energy measurements of battery power – long term operation
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Towards Enabling Uninterrupted Long-Term Operation of Solar Energy Harvesting Embedded Systems, B. Buchli, F. Sutton,
J. Beutel, and L. Thiele, 11th European Conference, EWSN 2014, Oxford, UK, February 17-19, 2014, Lecture Notes in
Computer Science, vol. 8354.
WIRELESS SETUP
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ALIX3D2
WNC DNMA-92
IEEE 802.11abgn
MIMO ANTENNA
Top MCS
300 Mbit/s
WMN NETWORK OPERATIONS
Ordinary TCP/IP network stack for transport data,
The WMN requires routing protocols,
There are various routing protocols options for WMNs,
OLSR,
IEEE 802.11s (link layer),
B.A.T.M.A.N. (not tested so far).
IEEE 802.11s based on the AirTime metric used,
IEEE 802.11s is link based, so it does not support nodes with multiple interfaces,
OLSR required to connect IEEE802.11s domains.
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OLSR + IEEE 802.11S
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OLSR ZONE
Mesh 2
Mesh 1
Nodes with multiple interfaces cannot reside in one
IEEE 802.11s mesh domain.
THE A4-MESH SETUP IN THE EMMENTAL
Wireless Mesh Networks and Cloud Computing for Real Time Environmental Simulations, Peter Kropf, Eryk Schiller, Philip Brunner , Oliver Schilling, Daniel Hunkeler, Andrei Lapin, Proceedings of the 10th International Conference on Computing and Information Technology (IC2IT2014),
Real-time Environmental Monitoring for Cloud-based Hydrogeological Modeling with HydroGeoSphere, Andrei Lapin, Eryk Schiller, Peter Kropf, Oliver Schilling, Philip Brunner, Almerima Jamakovic-Kapic, TorstenBraun, and Sergio Maffioletti, Proceedings of The APP Workshop associated with the High Performance Computing and Communications Conference, (IEEE HPCC2014).
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EMMENTAL MESH NETWORK TOPOLOGY
The Gateway is in Aebnitegg,
A multi-hop relay resides at the water pumping station,
Three solar-powered measuring nodes close to the Emme river,
Redundant links to improve reliability of our network.
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ÄBNITEGG
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The node is installed on a small chalet which belongs to a farmer in the Emmental,
It has access to the Internet through the ADSL connection,
It uses the electrical grid.
WATER PUMPING STATION
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Multi-hop relay
MESH NODE IN THE RIVER VALLEY
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Solar powered station,
It has environmental sensors attached.
Distributed Temperature Sensing (DTS):
GROUNDWATER-SURFACE INTERACTION
850n
m
1064n
m
1300n
m
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NETWORK MONITORING & MANAGEMENT
Network Monitoring is implemented as a periodical check of the state of a particular resource,
It provides information for management and planning,
There are numerous monitoring systems:
SNMP //RFC3411-3418,
Nagios http://www.nagios.org
Zabbix http://www.zabbix.com
Zabbix used due to the monolithic architecture & easy adaptation to Linux distributions for embedded devices such as ADAM.
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A4-MESH SENSOR DATA STORAGE
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Zabbix monitoring agent running on a mesh node to gather scientific sensor data.
Zabbix server running on a lab Linux machine to store sensor data.
Zabbix web interface shows detailed environmental plots.
WMN IN EMMENTAL
Problem :
• Management of groundwater pumping
• Forecast ?
ENVIRONMENTAL FORECASTING
Quantifying the influence of groundwater abstraction on river water levels requires a lot of data and quantitative models,
Adjusting the groundwater pumping rates must be based on good predictions of future water levels,
The quantitative model needs to be calibrated against measurements
With time, conventionally calibrated models deviate from the real system,
Real-Time Modeling can improve environmental predictions by adjusting states and re-calibrating model parameters when the provided predictions deviate from the real system.
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The HydroGeoSphere is used as the numerical application to simulate and quantify dynamics of the environmental system.(HydroGeoSphere: A Three-dimensional Numerical Model Describing Fully-integrated Subsurface and Surface Flow and Solute Transport, R. Therrien; R. G. Mclaren; E. A. Sudicky and S. M. Panday, 2007)
NUMERICAL MODEL: HYDROGEOSPHERE
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HydroGeoSphere allows the
numerical simulation of all the
relevant surface water and
groundwater processes that are
important in a pre-alpine valley
like the Emmental
CONVENTIONAL VS REAL-TIME MODELING
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Time
Variable
Time
Variable
Observation Observation
Model
Model
graphics provided by W. Kinzelbach
ENSEMBLE KALMAN FILTER
Ensemble Kalman Filter (EnKF) is an elegant method for real-time modeling; it is a Monte-Carlo based method running a large number of models with slightly different initial states and model parameters in parallel.(Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the
filter inbreeding problem, H. J. Hendricks Franssen and W. Kinzelbach, J. Water Resources Research, 2008)
The EnKF is used to provide a whole ensemble of predictions, allowing:
the computation of statistical moments
Continuous recalibration whenever new data becomes available.
EnKF is thus highly suited for parallelization!
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PUTTING IT TOGETHER
Real-time environmental modeling through the EnKF,
Requires: Real-time data harvesting,
EnKF is a Monte Carlo method which requires a large number of independent simulation runs,
Good forecasting is required to steer the water pumping station.
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Ensemble
Kalman
Filter
water source
pumping
station
gather environmental
data
laun
ch s
imulta
ne
ou
s
insta
nces o
f H
GS
drive pumping
water source for the
city of Bern
RUNTIMES OF THE HGS MODELER
Runtime example for the simplest Emmental model:
About 3.5 hrs on an Intel Xeon, 2.9 GHz, on 2 cores.
Conventional modeling requirements:
10 step optimization requiring 18 parallel model runs per step
6 models can be run in parallel,
Optimization time approx. 100 – 150 hours
Real Time modeling requirements:
Continuous optimization, whenever new data becomes available
Ensemble Kalman Filter forecast requires at least 100 parallel model runs per update step
impossible on a desktop machine… cluster or computing cloud required!
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CLOUD COMPUTING
Externalisation
Everything is a service
Infrastructure, Platform, Application
Dematerialisation
Minimise physical intervention: virtualisation
Scalability
Compute centers
Numerous ressources(ex. 100’000 CPUs)
CLOUD OPERATING SYSTEMS
OpenNebula, Eucalyptus, OpenStack, HP, Citrix,
Hypervisors: OpenStack, Xen, KVM, VMWare, Hyper-V,
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AMAZON S3 OBJECT STORE
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CLOUD
Object Store
BUCKET BUCKET
OBJECT OBJECTCLIENT
GC3PIE
GC3Pie is a Python framework to execute external commands in diverse computing resources,
It can distribute load among cloud-based VMs, clusters, computational Grids, and any Linux based machines with SSH access,
GC3Pie nicely integrates with the OpenStack Clouds,
GC3Pie is implemented by the Grid Computing Competence Center (GC3) of the University of Zurich.
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GC3Pie: A Python framework for high-throughput computing, S. Maffioletti and R. Murri; EGI Community Forum, Proceedings
of Science (EGICF12-EMITC2), 2012.
CLOUD BASED COMPUTING ARCHITECTURE
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Integration of WMNs & Cloud-based Infrastructures for computing,
Zabbix for environmental monitoring,
GC3Pie for cloud orchestrating,
Object Store is used for installing states & keeping Input/Output.
WORKFLOW DIAGRAM
A user directly interacts with the web-interface,
The web-interface installs input/output and jobs in the S3-based Object Store,
The Cloud Task Manager periodically checks the Object Store for new tasks,
GC3Pie orchestrates the VMs in the Cloud,
VMs run simultaneous instances of the HydroGeoSphere application simulating different environmental models [EnKF is easily parallelizable]
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INFORMATION FLOW
The user provides input files,
Dynamic data interfaces to fetch data from external resources (planned),
Assembled data reside in the Object Store,
The Cloud Task Manager takes input from the Object Store and distributes it among HydroGeoSphere workers on VMs.
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HYDROGEOSPHERE OUTPUT
Saturation of the soil:
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HYDROGEOSPHERE OUTPUT
River water levels:
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FUTURE OF THE A4-MESH SETUP
Integration with laser-based distributed temperature sensors,
Integration of the cloud architecture with the Distributed Kalman Filter currently under development in Nechatel, CH and Jülich, DE.
Hybrid solution with small embedded systems integrated with the A4-Mesh measuring architecture for providing uninterruptable 24/24 operation.
Getting funds for the future operation?
CUS-P2, e.g., Swiss eScience Support Team (becoming a node),
Other ideas?
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