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A job is executed when the request arrives from the controller.
This paper analyzes a dynamic and general scenario, in which: (i) workers can leave the network at any time; (ii) each job is executed multiple times, either to obtain better statistical accuracy or to perform parameter sweep analysis; and, (iii) input data is replicated and distributed to multiple data caches on-the-fly.
Table 1 Hadoop implementation results Compute Execution Per task Total execution nodes mode execution time time 1 local ∼0.01 sec ∼5 sec 1 cluster ∼15 sec ∼25 min 3 cluster ∼15 sec ∼12 min. In local mode, the job is executed on the local machine without contacting the Hadoop ResourceManager.
The other limitation is input data processing, which means the need to read the input data from the HDFS every time a MapReduce job is executed.
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The modified job was executed on the raw Avro data.
Tasks of the job are executed on the workers: job status set to "run" 4.
Overlapped partitioning can be implemented in a relaxed flavour, where only some parts of the job are executed redundantly.
It is clear from the resuls that Hadoop incurred a significant overhead when a job was executed on the cluster via the ResourceManager.
The constraint in (12) states the precedence constraint where a task cannot start until its predecessors are completed, and data has been communicated to it if the preceding job were executed on different node, illustrated as a constant value T c.
In this paper, the load is measured by the remaining time of jobs being executed, plus the execution times of waiting jobs.
Without Quality of Service (QoS), the jobs are executed without any guarantee on the execution time or throughput.
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