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The original objective is measured by the weighted discounted total completion time and the deviation is measured by the variances in jobs' completion times.
However, the designed model can be used for predicting the jobs' completion time on a large Hadoop cluster and be applied for its sizing to achieve desirable service level objectives (SLOs).
In the area of task scheduling, in which the main focus was on the jobs' completion time, we propose in this work a scheduling optimization mechanism that aims to reduce the cost of task scheduling.
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The experimental results are evident for the minimization of job completion time for the submitted job.
Our experimental results reveal that RC2 significantly reduces delay in job completion.
Cybernetics functions are created to ensure that 100% job completion rate for Big Data processing.
The objective is to find a schedule that minimizes the total job completion time.
Resource allocation was performed to optimize job completion time and data center power consumption.
After each job completion, the client submits the output to the server, as a normal application.
It is observed that the proposed framework improves the number of successful job completion.
We conclude that the Small Median Large layout coupled with Shortest Processing Time (SPT) sequencing rule yield the best design with respect to both the maximum job completion time (makespan) and the total (average) job completion time criteria.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com