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Supporting data locality for multi-stage jobs that are characterized by multiple phases of execution may need to consider the possibility of one phase of execution sharing data with another phase of execution.
The tasks t4 and t5, which analyze and perform computation on the parsed data, are part of the second phase of execution.
In the sample job shown in Fig. 2, the first phase of execution comprises three tasks: t1, t2, and t3, and the function of these three tasks is to read and parse the input data.
The ED algorithm requires each task in a job to have an execution phase attribute, which is an integer (1, 2, 3, …) that indicates the phase of execution that the task belongs to.
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If data needs to be shared among these two phases of execution, the tasks in these two phases of execution should be assigned to execute on nodes that are as close to each other as possible to minimize the data transmission overhead.
As shown, the workflow has five phases of execution.
Fig. 4 shows the DAG of a sample LIGO workflow, which has 6 phases of execution.
Multi-stage jobs require service from multiple system resources and are characterized by multiple phases of execution.
This research confirms that malware can be detected during the early phases of execution, possibly prior to any malicious activity.
Task migration optimize the overall communication performance of NoCs since the changing phases of execution make static task mapping insufficient.
In our preliminary work [11], a resource allocation and scheduling technique for processing deadline-constrained MapReduce [7] jobs (comprising of only two phases of execution) is described.
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