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The reason for this is the fact that our algorithm minimizes the execution rounds and it increases the number of tasks only if we have a performance gain.
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The main design goal of this architecture is to minimize the execution time.
We minimize the execution time for this by parallelizing the operation using OpenMP.
Pandey et al. [9] attempted to minimize the execution cost of workflow on cloud computing environment.
We also investigate the optimization of the involved parameters to minimize the execution time.
The main goal of our implementation is to minimize the execution time and the used resources without affecting the performance.
For this reason, minimizing the execution time of each element is a crucial task which directly impacts the emulator performance.
More specifically, we identify task assignments that guarantee maximum utilization of resources while minimizing the execution time of tasks.
Efficient use of computing resources to minimize the execution time requires a job dispatching algorithm that can appropriately determine the assignment of jobs.
Moreover, it proposes new task assignment that guarantees maximum utilization of resources while minimizing the execution time of tasks for virtualized IVR applications.
Another recent proposal for Hadoop autotuning was described in [25] where they tune parameters to minimize the execution time of a single job.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com