Real-time Scheduling for Grid Computing

Anwar Mamat

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Little about me

• I belong to an ethnic minority called “Uyghur” in China.

• Please google “Uyghur” for details.

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Background• As the size of a grid increases, so does the complexity of resource

management and maintenance.

• Automated performance control and resource management is crucial to sustain the evolution of grid computing.

• New scheduling algorithms are need to provide high performance with assured Quality of Service (QoS) while also simplifying the management of grid resources.

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Research Goal

Develoing fundamental autonomic theory and technology that leads to self-managing grids of computational and storage resources that support QoS attributes.

My Focus is on adaptive scheduling, real-time divisible load scheduling, resource reservation and cluster resource proportional share scheduling. I develop and apply real-time scheduling theory for grid computing and HPC, provide QoS for the gird and HPC applications.

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Current ResearchProviding assured QoS performance of cluster and grid applications through the development of new real-time grid scheduling theory and algorithms that will be implemented and tested in real-life environments.

My research focuses on:

1. Real-Time Divisible Load Scheduling for Clusters

2. Real-Time Divisible Load Scheduling for a Grid with Resource Reservations

3. Proportional share for the clusters:

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Projects• Divisible load real-time scheduling

• Intelligent data management

• Load balancing between heterogeneous clustes

• Advance resource reservation

• Front-end task partition

• Other MPI parallel programming simulations

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Previous Grid Schools• TeraGrid New user Workshop, University of Illinois at

Urbana-Champaign, September 2008

• Great Plains Grid School, University of Nebraska-Lincoln, August 2007

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Future Research

My work will be expected to break a new ground in grid computing, particularly in providing QoS to arbitrarily divisible applications such as CMS and ATLAS. The tools developed are expected to increase the utilization of clusters and simplify the resource management.

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