Allocating Modules to Processors in a Distributed System
IEEE Transactions on Software Engineering
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IEEE Transactions on Parallel and Distributed Systems
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IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
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Measuring the Robustness of a Resource Allocation
IEEE Transactions on Parallel and Distributed Systems
Iterative list scheduling for heterogeneous computing
Journal of Parallel and Distributed Computing
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Static allocation of resources to communicating subtasks in a heterogeneous ad hoc grid environment
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IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
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Journal of Parallel and Distributed Computing
Robust resource allocation in a cluster based imaging system
Parallel Computing
List scheduling with duplication for heterogeneous computing systems
Journal of Parallel and Distributed Computing
Evaluation and Optimization of the Robustness of DAG Schedules in Heterogeneous Environments
IEEE Transactions on Parallel and Distributed Systems
Scheduling with uncertainties on new computing platforms
Computational Optimization and Applications
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In this study, we consider an environment composed of a heterogeneous cluster of multicore-based machines used to analyze satellite data. The workload involves large data sets and is subject to a deadline constraint. Multiple applications, each represented by a directed acyclic graph (DAG), are allocated to a dedicated heterogeneous distributed computing system. Each vertex in the DAG represents a task that needs to be executed and task execution times vary substantially across machines. The goal of this research is to assign the tasks in applications to a heterogeneous multicore-based parallel system in such a way that all applications complete before a common deadline, and their completion times are robust against uncertainties in execution times. We define a measure that quantifies robustness in this environment. We design, compare, and evaluate five static resource allocation heuristics that attempt to maximize robustness. We consider six different scenarios with different ratios of computation versus communication, and loose and tight deadlines.