Autonomic Execution of Web Service Compositions
ICWS '05 Proceedings of the IEEE International Conference on Web Services
Taverna: lessons in creating a workflow environment for the life sciences: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Nimrod/K: towards massively parallel dynamic grid workflows
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Flexible Scientific Workflow Modeling Using Frames, Templates, and Dynamic Embedding
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
An Autonomic Approach to Integrated HPC Grid and Cloud Usage
E-SCIENCE '09 Proceedings of the 2009 Fifth IEEE International Conference on e-Science
The JOpera visual composition language
Journal of Visual Languages and Computing
Adaptive exception handling for scientific workflows
Concurrency and Computation: Practice & Experience
Pattern based workflow design using reference nets
BPM'03 Proceedings of the 2003 international conference on Business process management
Vega: a service-oriented grid workflow management system
OTM'07 Proceedings of the 2007 OTM confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part II
Towards Reliable, Performant Workflows for Streaming-Applications on Cloud Platforms
CCGRID '11 Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Autonomic streaming pipeline for scientific workflows
Concurrency and Computation: Practice & Experience
Hi-index | 0.00 |
There is emerging interest in many scientific disciplines to deal with "dynamic" data, arising from sensors and scientific instruments, which require workflow graphs that can be dynamically adapted - as new data becomes available. Additionally, the elastic nature of many Cloud environments subsequently enable such dynamic workflow graphs to be enacted more efficiently. One of the challenges of scientific work-flows is that they must be designed with the needed level of dynamism to take account of the availability of data and the variability of the execution environment, which can be dynamically scaled out based on demand (and budget). In this paper, we present a novel approach for specifying scientific workflows with the two main requirements of: (i) dynamic / adaptive workflow structure well suited for and responsive to change, and (ii) support for large-scale and variable parallelism. We utilise the superscalar pipeline as a model of computation and the well-known Montage workflow for illustrating our approach.