Grid application development software

The GrADS project is exploring the scientific and technical problems that must be solved to make Grid application development and performance tuning for real applications an everyday practice. This requires research in four key areas, each validated in a prototype infrastructure that will make programming on grids a routine task:.

In addition, the GrADS project is developing MicroGrid testbeds to systematically explore, demonstrate and characterize the effectiveness of the technologies developed, while using the evolving national Grid testbeds for full-scale experimentation and demonstration.

First, the principal investigators will work directly with application developers to understand the mechanisms that would be useful for bringing up their applications on the Grid.

Second, the project will collab rate with industrial partners to encourage the adoption and standardization of system software technologies that arise from the research. In this paper, our contributions are two-fold: First, we enhance the Min-Min and Sufferage heuristics under three risk modes driven by security concerns.

Under our new m Abstract - Cited by 21 3 self - Add to MetaCart In this paper, our contributions are two-fold: First, we enhance the Min-Min and Sufferage heuristics under three risk modes driven by security concerns.

Under our new model, a job can possibly fail if the site security level is lower than the job security demand. We consider three security-driven heuristic modes: secure, risky, and f -risky. The secure mode always dispatches jobs to secure sites meeting the job security demands. The risky mode allocates jobs to any available resource site, taking whatever the risk it may face. The f -risky mode tries to limit the risk to be at most certain probability f.

The STGA outperforms the Min-Min and Sufferage heuristics under three risk modes, in terms of a wide range of performance metrics including makespan, average response time, site utilization, slowdown ratio, and job failure rate.

Developing grid applications is a challenging endeavor, which at the moment requires both extensive labor and expertise. This system incorporates tools at all stages of the applicati Abstract - Cited by 18 2 self - Add to MetaCart Developing grid applications is a challenging endeavor, which at the moment requires both extensive labor and expertise.

This system incorporates tools at all stages of the application development and execution cycle. In this chapter we focus on application scheduling, and present the three scheduling approaches developed in GRADS: development of an initial application schedule launch-time scheduling , modification of the execution platform during execution rescheduling , and negotiation between multiple applications in the system metascheduling.

These approaches have been developed and evaluated for platforms that consist of distributed networks of shared workstations, and applied to real-world parallel applications.

A novel approach to resource scheduling for parallel query processing on computational grids. Abstract - Cited by 14 9 self - Add to MetaCart processing on computational grids.

In multicluster systems, and more generally in grids, jobs may require co-allocation, that is, the simultaneous or coordinated access of single applications to resources of possibly multiple types in multiple locations managed by different resource managers. Co-allocation presents new challenges to Abstract - Cited by 11 3 self - Add to MetaCart In multicluster systems, and more generally in grids, jobs may require co-allocation, that is, the simultaneous or coordinated access of single applications to resources of possibly multiple types in multiple locations managed by different resource managers.

Co-allocation presents new challenges to resource management in grids, such as locating sufficient resources in geographically distributed sites, allocating and managing resources in multiple, possibly heterogeneous sites for single applications, and coordinating the execution of single jobs at multiple sites. Moreover, as single jobs now may have to rely on multiple resource managers, co-allocation introduces reliability problems.

In this paper, we present the design and implementation of a co-allocating grid scheduler named KOALA that meets these co-allocation challenges. In addition, we report on the results of an analysis of the performance in our multicluster testbed of the co-allocation policies built into KOALA. We also include the results of a performance and reliability test of. In multicluster grid systems, parallel applications may benefit from processor co-allocation, that is, the simultaneous allocation of processors in multiple clusters.

Although co-allocation allows the allocation of more processors than available in a single cluster, it may severely increase the exec In this chapter we focus on application scheduling, and present the three scheduling approaches developed in GrADS: development of an initial application schedule launch-time scheduling , modification of the execution platform during execution rescheduling , and negotiation between multiple applications in the system metascheduling.

These approaches have been developed and evaluated for platforms that consist of distributed networks of shared workstations, and applied to real-world parallel applications.

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