1. Economic and On Demand Brain Activity Analysis on Global Grids A case study

2. Need for grid Two major problems commonly observed in scientific disciplines: scientific data The distribution of knowledge and technologies

3. Cont.. One such scientific discipline: Brain science The analysis of brain activity data gathered from the MEG (Magnetoencephalography) instrument is an important research topic in medical science

4. Introduction computational grids : Aggregations of such distributed resources, called computational grids. Biological science: Brain Activity is one such application Brain activity is measured by the Magnetoencephalography (MEG) measures the magnetic fields generated by the electrical activity in the brain.

5. Brain Activity Analysis - Advantage of MEG MEG instrument: consists of a number of sensors which record information about brain activity MEG helmets with over 200 sensors detect magnetic brain fields by means of a sensitive transducer technology called Superconducting Quantum Interference Device (SQUID)

6. limitation The high cost of equipment There are only limited numbers of MEG instruments around the world For example, a 64-sensor MEG instrument would produce 0.9GB of data over a period of an hour. Such a task generates 7,257,600 analysis jobs and would take 102 days on a commodity computer with a PentiumIII/500MHz processor and 256MB of memory.

7. Grid-based Analysis Model NeuroGrid project aims to convert the existing brain activity analysis application into a parameter sweep application for executing jobs which perform wavelet cross-correlation analysis for each pair of sensors in parallel on distributed resources

8. A Model for Brain Activity Analysis on Global Grids.

9. steps: 1. medical staff who is dealing with the diagnosis orders a MEG scan of the patient’s brain 2. request is sent to instrument which takes a MEG scan and collects data about the activity in the brain 3. This data is then collected and presented to the Grid Resource Broker for analyzing on the Grid QOS- deadline and the budget optimization method could be one of the three: cost, time or cost-time. 4,5- data and analysis code are dispatched to remote node and results collected

10. Architecture

11. Components parameterization tools (Nimrod-G parameter specification language) resource broker(Nimrod-G with Gridbus scheduler) grid market directory (Gridbus GMD) low-level grid middleware (Globus) Grid Enabling process: resources - Globus software deployed on them. application - parameter sweep application using the Nimrod-G parameter specification language. GMD used as a register for publication of resource providers and services.

12. Analysis Code developed by the Cybermedia Centre, Osaka University, Japan two phases Phase 1: raw data from the brain goes through wavelet transform operation time-frequency data of the output Phase 2: cross-correlation analysis is performed for each pair of wavelet transforms. output displays the similarity between a pair of brain data for every frequency spectrum.

13. Wavelet Cross Correlation Analysis

14. Grid Resource Broker and Scheduler Resource Broker – Nimrod G + Globus middleware. Gridbus Scheduler. performs resource discovery, selection, and dispatching of MEG jobs to remote resources. It also starts and manages the execution of jobs and gathers the results back at the home node. Components of Nimrod G A persistent task farming engine. A grid explorer for resource discovery.

15. Grid bus Scheduler Plugin scheduler- designed to use GMD. Nimrod G- Processing cost based on CPU Time. GMD allows GSP Ao Service+ Service Price. Gridbus Scheduler resource allocation based on Ao Cost model.

16. Gridbus Scheduler Algorithms Cost minimization. Time minimization. Cost – Time Optimization. Uses past performance of machines. Average job completion rates.

17. Grid Market Discovery Allows service providers to publish services with costs. Built on standard web service technologies. Client API.

18. Grid Enabling The Application Nimrod-G farming engine and dispatcher along with Gridbus scheduler used for deploying and processing it on Global Grids 2 programs. raw2wavelet. wavelet2cross. Meta meg. Time_offset_step.

19. Pseudo code for meta program

20. Nimrod –G plan for SPMD..$OS $HOME $HOME/alphawave

21. Plan file for brain activity analysis on the Grid.

22. Application Deployment and Evaluation

23. Scheduling Experiments and Result Deadline= 2 hrs. Budget=1990 Grid $. Summary of Experiment Statistics.

24. Scheduling with Time Minimization

25. Scheduling with Cost- optimization

26. Scheduling with Cost-Time optimization

27. Visualisation of wavelet analysis results for selected sensors.

29. Conclusion The economy based approach of processing brain activity data as illustrated in this paper would help in enforcing QoS requirements of medical applications Hence would enable adoption of Grid technologies by the bio-instrumentation field.