Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Condor-G: A Computation Management Agent for Multi-Institutional Grids
Cluster Computing
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
New grid scheduling and rescheduling methods in the GrADS project
International Journal of Parallel Programming - Special issue: The next generation software program
Autonomic Computing
On the Advantages of an Alternative MPI Execution Model for Grids
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Distributed and dynamic self-scheduling of parallel MPI Grid applications: Research Articles
Concurrency and Computation: Practice & Experience - Middleware for Grid Computing: A “Possible Future”
IEEE Transactions on Parallel and Distributed Systems
Dynamic self-scheduling for parallel applications with task dependencies
Proceedings of the 6th international workshop on Middleware for grid computing
EasyGrid Enabling of Iterative Tightly-Coupled Parallel MPI Applications
ISPA '08 Proceedings of the 2008 IEEE International Symposium on Parallel and Distributed Processing with Applications
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Grid and Internet Computing have proved their worth executing large-scale bag-of-task class applications. Numerous middlewares have been developed to manage their execution in either dedicated environments or opportunistic and shared ad-hoc grids. While job dependencies are now being resolved by middleware capable of scheduling workflows, these environments have yet to be shown beneficial for message passing parallel applications. Obtaining high performance in these widely available environments without rewriting existing parallel applications is of up most importance to e-Science . The key to an efficient solution may be an alternative execution model and the efficient dynamic scheduling of application processes. This paper presents a hierarchical scheme for dynamically scheduling parallel DAG applications across a set of non-dedicated heterogeneous resources. In order to efficiently tackle process dependencies and adapt to varying system characteristics, dynamic schedulers are distributed within the application and operate in a collaborative and pro-active fashion to keep overheads low.