Toward a Framework for Preparing and Executing Adaptive Grid Programs
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Predicting bounds on queuing delay for batch-scheduled parallel machines
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Task scheduling strategies for workflow-based applications in grids
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Scheduling strategies for mapping application workflows onto the grid
HPDC '05 Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium
Falkon: a Fast and Light-weight tasK executiON framework
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Adaptive Workflow Processing and Execution in Pegasus
GPC-WORKSHOPS '08 Proceedings of the 2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops
The cost of doing science on the cloud: the Montage example
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
VGrADS: enabling e-Science workflows on grids and clouds with fault tolerance
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Building the Trident Scientific Workflow Workbench for Data Management in the Cloud
ADVCOMP '09 Proceedings of the 2009 Third International Conference on Advanced Engineering Computing and Applications in Sciences
Communications of the ACM
Influences between performance based scheduling and service level agreements
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing - Volume 2
A cost-effective cloud computing framework for accelerating multimedia communication simulations
Journal of Parallel and Distributed Computing
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Cloud computing is increasingly considered as an additional computational resource platform for scientific workflows. The cloud offers opportunity to scale-out applications from desktops and local cluster resources. Each platform has different properties (e.g., queue wait times in high performance systems, virtual machine startup overhead in clouds) and characteristics (e.g., custom environments in cloud) that makes choosing from these diverse resource platforms for a workflow execution a challenge for scientists. Scientists are often faced with deciding resource platform selection trade-offs with limited information on the actual workflows. While many workflow planning methods have explored resource selection or task scheduling, these methods often require fine-scale characterization of the workflow that is onerous for a scientist. In this paper, we describe our early exploratory work in using blackbox characteristics for a cost-benefit analysis of using different resource platforms. In our blackbox method, we use only limited high-level information on the workflow length, width, and data sizes. The length and width are indicative of the workflow duration and parallelism. We compare the effectiveness of this approach to other resource selection models using two exemplar scientific workflows on desktop, local cluster, HPC center, and cloud platforms. Early results suggest that the blackbox model often makes the same resource selections as a more fine-grained whitebox model. We believe the simplicity of the blackbox model can help inform a scientist on the applicability of a new resource platform, such as cloud resources, even before porting an existing workflow.