FedCloudJAMSPoC

General Information

• Status: active
• Start Date: 29/05/2014
• End Date: -
• EGI.eu contact: Diego Scardaci / diego.scardaci@egi.eu
• External contact: Christian Fischer / christian.fischer.2@uni-jena.de

Short Description

JAMS is an java-based, open-source software platform that has been especially designed to address the demands of a process-based hydrological model development and various aspects of model application (https://appdb.egi.eu/store/software/jams).

JAMS is a framework to build up complex models out of simple components. Several hydrological models were implemented within JAMS (e.g. J2000, J2000g). Usually those models are applied to simulate hydrological dynamics in catchments with a size of 1km² to 100,000 km² in a temporal time step of hours to months. Typically, the computing time of a single model run is in the range from minutes to hours (on a single workstation). However, those models have many parameters, which must be estimated indirectly during a calibration process. For model calibration an evolutionary optimization algorithms was adopted, which evaulate the model several thousand times. Therefore this calibration process takes days to weeks on a single workstation. To speed up this process, we implemented a thread-based parallelization in JAMS. This thread-based parallelization is used for example to evaluated several parameter combinations in parallel during optimization. The model calibration is carried out frequently. Sometimes more than 10 times a week.

http://jams.uni-jena.de/

Use Case

Use case

Run on the EGI Federated Cloud a system to support modeling of physiological systems in the phase of calibrating model parameters and in the phase of simulating different scenarios. The loosely coupled part of the system is deployed in a remote distributed computational capacity. The system capabilities is accessible via a web application and allows user to focus on experimental data, names of parameters, visual control of the calibration computation and hide unnecessary complexity of the remote subsystems computation. The data of real experiments and simulations are stored and provided for further research.

Requirements:

• 1 VM
• Number of CPU cores: at least 10 CPU per virtual machine - (12 CPU)
• RAM requirements per CPU core: at least 300 MB per core - (4 GB)
• Storage requirements (non persistent): 1 GB per core free, (12 GB free) - (24GB non persistent disc with operating system)
• Inbound/Outbound during execution - 10kB-100kB per sec
• Licensed software - MS Windows 2008 R2 Datacenter (the developer has an MSDN licence limited for development and testing purpose)
• Operating System - MS Windows 2008 R2 Datacenter: VM image already available, a remote desktop system has to be added.
• ports - 48048-48060 for task/results exchange for incoming communication, 80,443, for HTTP outgoing synchronization with web services (physiome.lf1.cuni.cz)
• public ip addresses

Additional Files

• Phisiome project, slide presented at the EGI CF 2014: https://indico.egi.eu/indico/contributionDisplay.py?sessionId=10&contribId=51&confId=1994