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The input required by the DBW algorithm is a multi-stage job j and two integer arguments: setOpt to indicate the approach used to calculate the sample execution time of the job j (SET j ) and laxDistOpt to specify how the laxity (or slack time) of the job j (L j ) is to be distributed among its constituent tasks.
The normalized laxity of a job j (denoted NL j ) is calculated as follows: {NL}_j=frac{L_j}{SET_j} (3)where L j is the laxity of job j and SET j is the sample execution time of job j (recall Section "Deadline Budgeting Algorithm for Workflows").
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In this problem of few samples, classification execution time is low, therefore a wrapper method is a good option.
The comparison results of current methods also showed that the proposed method has some advantages in aspects of accuracy rate, sample robustness and execution time.
For comparison we also show the average execution time per sample of the SSA using a finite sampling rate.
Our algorithm starts using a single sample (Z ‾ = 1 ) and adapts the number of samples dynamically, depending on the execution time of the solution evaluation.
Software profiling necessarily introduces some overhead on occupied area in memory (due to application instrumentation), and some overhead on execution time (because the sampling is done in an interrupt service routine executed by the processor).
Under this parallelized design, the real execution time for N samples (N * t) is reduced to t, where t represents execution time for one sample.
The global operations, the weight normalization and the resampling, indeed cost over 99% execution time while the sampling costs extremely little.
To estimate the total execution time for a given sample size, we summed median execution over all LD blocks.
However, when the particle number reduction slows down, larger P results in execution time overhead of MP-sampling operation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com