Grid Computing:

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Grid Computing Introduction Activities
Lee Bu Sung / Cai Wentong School of Computer
Engineering Nanyang Technological
University Singapore
CeMNet http//www.cemnet.ntu.edu.sg/ PDPLab http/
/sentosa.sas.ntu.edu.sg8000/
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Grid Activities in SCE

• Grid Testbed in School of Computer Engineering
• Our Research Focus on Grid Computing
• middleware development
• application development

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Grid Infrastructure in SCE
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Applications
High-performance PC Cluster with Cluster Software
Grid Computing Middleware
High-speed Network
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Grid Clearing House
Grid computing has emerged as an important new
field, distinguished from conventional
distributed computing by its focus on
large- scale resource sharing,
innovative applications, and in some
cases, high- performance orientation.
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Access Control Allocation Accounting
Clearinghouse Server
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Resource Managementfor HLA-based Simulations
Distributed Simulation execution of a simulation
on geographically distributed computers
interconnected via a LAN and/or a WAN
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High Level Architecture
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Load Management System (LMS)
Federation
SimulationSurrogates
Passive Viewers
Simulations
Run-Time Infrastructure (RTI)
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MEG Data Analysis
MEG is a non-intrusive method for capturing the
electrical activity in the brain. It can help the
practitioner to diagnose certain brain illness.
In our system, data transfer, analysis and
diagnosis are applied in the computing grid. This
is a collaboration project with CyberMedia Center
of Osaka University in Japan
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GRID
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Protein Alignment
Discovered new protein sequence is usually
analyzed by comparison with genetic sequence
databases, which are growing exponentially. To
speedup the analysis, a sequence database is
partitioned and the tasks of sequence alignment
are distributed over a computational grid.
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DIS
Grid Resources
Job Submission
Scheduler
RSL Generator
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- Data are replicated - Globus GASS and GSIFTP
are used for data transfer
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Conclusions

• Grid Computing
• still in research and pilot stage
• global standards required
• Research Challenges
• security
• resource management
• programming models and tools
• network protocols and infrastructures
• algorithms and problem-solving methods