The Role of Planning in Grid Computing

Grid computing gives users access to widely distributed networks of computing resources to solve large-scale tasks such as scientific computation. These tasks are defined as standalone components that can be combined to process the data in various ways. We have implemented a planning system to generate task workflows for the Grid automatically, allowing the user to specify the desired data products in simple terms. The planner uses heuristic control rules and searches a number of alternative complete plans in order to find a high-quality solution. We describe an implemented test case in gravitational wave interferometry and show how the planner is integrated in the Grid environment. We discuss promising future directions of this work. We believe AI planning will play a crucial role in developing complex application workflows for the Grid.

[1]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[2]  Kristian J. Hammond,et al.  Case-based planning: an integrated theory of planning, learning and memory , 1986 .

[3]  James A. Hendler,et al.  Flexible reuse and modification in hierarchical planning: a validation structure-based approach , 1989 .

[4]  Manuela M. Veloso,et al.  Planning and Learning by Analogical Reasoning , 1994, Lecture Notes in Computer Science.

[5]  Amy L. Lansky,et al.  The COLLAGE/KHOROS Link: Planning for Image Processing Tasks , 1995 .

[6]  Satyandra K. Gupta,et al.  AI Planning Versus Manufacturing-Operation Planning: A Case Study , 1995, IJCAI.

[7]  Eugene Fink,et al.  Integrating planning and learning: the PRODIGY architecture , 1995, J. Exp. Theor. Artif. Intell..

[8]  Maria Alicia Perez.  Learning search control knowledge to improve plan quality , 1996 .

[9]  Steve A. Chien,et al.  Automating Image Processing for Scientific Data Analysis of a Large Image Database , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

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

[11]  T. Dean,et al.  Planning under uncertainty: structural assumptions and computational leverage , 1996 .

[12]  Stephen F. Smith,et al.  An Ontology for Constructing Scheduling Systems , 1997 .

[13]  Craig A. Knoblock,et al.  Planning by Rewriting: E ciently Generating High-Quality Plans , 1999 .

[14]  Tara A. Estlin,et al.  Learning to Improve both Efficiency and Quality of Planning , 1997, IJCAI.

[15]  Jim Blythe,et al.  Decision-Theoretic Planning , 1999, AI Mag..

[16]  Milind Tambe,et al.  Building Agent Teams Using an Explicit Teamwork Model and Learning , 1999, Artif. Intell..

[17]  Yolanda Gil,et al.  PLANET: A Shareable and Reusable Ontology for Representing Plans , 2000 .

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

[19]  Stephen F. Smith,et al.  Integrating Planning and Scheduling through Adaptation of Resource Intensity Estimates , 2001 .

[20]  Bernhard Nebel,et al.  The FF Planning System: Fast Plan Generation Through Heuristic Search , 2011, J. Artif. Intell. Res..

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

[22]  Ian T. Foster,et al.  The anatomy of the grid: enabling scalable virtual organizations , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[23]  Yong Zhao,et al.  Chimera: a virtual data system for representing, querying, and automating data derivation , 2002, Proceedings 14th International Conference on Scientific and Statistical Database Management.

[24]  Yong Zhao,et al.  Applying Chimera Virtual Data Concepts to Cluster Finding in the Sloan Sky Survey , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[25]  Peter Z. Kunszt,et al.  Giggle: A Framework for Constructing Scalable Replica Location Services , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[26]  Yolanda Gil,et al.  Pegasus: Planning for Execution in Grids , 2002 .

[27]  Tran Cao Son,et al.  Adapting Golog for Composition of Semantic Web Services , 2002, KR.

[28]  Drew McDermott,et al.  Estimated-Regression Planning for Interactions with Web Services , 2002, AIPS.

[29]  Keith Golden,et al.  Universal Quantification in a Constraint-Based Planner , 2002, AIPS.

[30]  Carl Kesselman,et al.  GriPhyN and LIGO, building a virtual data Grid for gravitational wave scientists , 2002, Proceedings 11th IEEE International Symposium on High Performance Distributed Computing.

[31]  Ricardo Aler,et al.  On Control Knowledge Acquisition by Exploiting Human-Computer Interaction , 2002, AIPS.

[32]  Adam Arbree,et al.  Mapping Abstract Complex Workflows onto Grid Environments , 2003, Journal of Grid Computing.

[33]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.