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Each has been investigated and the most appropriate approach is selected to achieve a trade-off between recognition accuracy and computing execution time.
We propose a new formulation of this problem based on a cooperative distributed game-theory-based method applied using three algorithms with low time complexity for optimizing three important metrics in scientific computing: execution time, economic cost, and storage requirements.
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We also computed execution times for ten randomly drawn miRs shown in Table 9 [see Additional file 1].
This is useful if a user wants to save/use previously computed executions without having to define them again.
Based on this conceptualization, CPS resource management algorithms are categorized according to: (i) computing workload execution and arrival profiles supported, (ii) knowledge of workload profiles during management decision making, (iii) support of power management in the computing components, and (iv) assumptions on non-computing process behavior.
These idle slots make it possible for our pattern-finding procedure to find enough room to fit a job of a task when computing the execution pattern for a migrating task.
In order to precisely represent them in compute execution results, a two-dimension domain, called ''super-dense time," is used as the domain for defining signals.
The configurations used are shown in Table 2. Table 2 Spark implementation results Compute Execution Per task Total execution nodes mode execution time time 1 local ∼0.03 sec ∼6 sec 3 cluster ∼0.6 sec ∼40 sec.
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.
To that aim, we wrote the code implementing the 3TCA features and then we fixed simulation scenarios and we computed the execution cost of the scenarios implementation.
Here, we show how the abstract syntax for the Task Algebra may be parsed and then semantically analysed, by a suite of Haskell functions, to compute the execution traces of a system.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com