A General Architecture for Scheduling on the Grid

In this paper we present a general architecture for scheduling on a Grid. A Grid scheduler (or broker) must make resource selection decisions in an environment where it has no control over the local resources, the resources are distributed, and information about the systems is often limited or dated. These interactions are also closely tied to the functionality of the Grid Information Services. The Grid scheduling architecture has three phases: resource discovery, system selection, and job execution. We detail the steps involved in each phase and give examples from current systems.

[1]  Francine Berman,et al.  Heuristics for scheduling parameter sweep applications in grid environments , 2000, Proceedings 9th Heterogeneous Computing Workshop (HCW 2000) (Cat. No.PR00556).

[2]  Francine Berman,et al.  Scheduling from the perspective of the application , 1996, Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing.

[3]  Warren Smith A Framework for Control and Observation in Distributed Environments , 2001 .

[4]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[5]  Francine Berman,et al.  Application-Level Scheduling on Distributed Heterogeneous Networks , 1996, Proceedings of the 1996 ACM/IEEE Conference on Supercomputing.

[6]  Richard Wolski,et al.  Dynamically forecasting network performance using the Network Weather Service , 1998, Cluster Computing.

[7]  Allen B. Downey Predicting queue times on space-sharing parallel computers , 1997, Proceedings 11th International Parallel Processing Symposium.

[8]  Jarek Nabrzyski,et al.  User preference driven multiobjective resource management in grid environments , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[9]  Jennifer M. Schopf,et al.  Grids: The top ten questions , 2002, Sci. Program..

[10]  Richard Wolski,et al.  The network weather service: a distributed resource performance forecasting service for metacomputing , 1999, Future Gener. Comput. Syst..

[11]  Jennifer M. Schopf,et al.  Predicting sporadic grid data transfers , 2002, Proceedings 11th IEEE International Symposium on High Performance Distributed Computing.

[12]  Ian Foster,et al.  A quality of service architecture that combines resource reservation and application adaptation , 2000, 2000 Eighth International Workshop on Quality of Service. IWQoS 2000 (Cat. No.00EX400).

[13]  Francine Berman,et al.  High-performance schedulers , 1998 .

[14]  Ruth A. Aydt,et al.  A Grid Monitoring Architecture , 2002 .

[15]  Warren Smith,et al.  Predicting Application Run Times Using Historical Information , 1998, JSSPP.

[16]  Ian T. Foster,et al.  Data management and transfer in high-performance computational grid environments , 2002, Parallel Comput..

[17]  Warren Smith,et al.  A directory service for configuring high-performance distributed computations , 1997, Proceedings. The Sixth IEEE International Symposium on High Performance Distributed Computing (Cat. No.97TB100183).

[18]  Rajesh Raman,et al.  Resource management through multilateral matchmaking , 2000, Proceedings the Ninth International Symposium on High-Performance Distributed Computing.

[19]  B. Coghlan,et al.  Time , Information Services and the Grid , 2001 .

[20]  John Shalf,et al.  The Cactus Worm: Experiments with Dynamic Resource Discovery and Allocation in a Grid Environment , 2001, Int. J. High Perform. Comput. Appl..

[21]  Rajesh Raman,et al.  Matchmaking: distributed resource management for high throughput computing , 1998, Proceedings. The Seventh International Symposium on High Performance Distributed Computing (Cat. No.98TB100244).

[22]  Ian T. Foster,et al.  Grid information services for distributed resource sharing , 2001, Proceedings 10th IEEE International Symposium on High Performance Distributed Computing.

[23]  Francine Berman,et al.  Toward a framework for preparing and executing adaptive grid programs , 2002, Proceedings 16th International Parallel and Distributed Processing Symposium.