Allocating Modules to Processors in a Distributed System
IEEE Transactions on Software Engineering
IEEE Transactions on Parallel and Distributed Systems
On Exploiting Task Duplication in Parallel Program Scheduling
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Improving Scheduling of Tasks in a Heterogeneous Environment
IEEE Transactions on Parallel and Distributed Systems
Scientific workflow management and the Kepler system: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Programming scientific and distributed workflow with Triana services: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Operating Systems Concepts
Supporting Distributed Application Workflows in Heterogeneous Computing Environments
ICPADS '08 Proceedings of the 2008 14th IEEE International Conference on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
SERVICES '11 Proceedings of the 2011 IEEE World Congress on Services
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Next-generation e-science applications feature large-scale data-intensive workflows comprised of many interrelated computing modules. The end-to-end performance of such scientific workflows depends on both the mapping scheme, which determines module assignment, and the scheduling policy, which determines resource allocation if multiple modules are mapped to the same node. These two aspects of workflow optimization are traditionally treated as two separated topics, and the interactions between them have not been fully explored by any existing efforts. As the scale of scientific workflows and the complexity of network environments rapidly increase, each individual aspect of performance optimization alone can only meet with limited success. We conduct an in-depth investigation into workflow execution dynamics of both mapping and scheduling, and propose an integrated solution, referred to as Mapping and Scheduling Interaction (MSI), to achieve a higher level of resource utilization and workflow performance. The efficacy of MSI is illustrated by extensive simulation-based workflow experiments.