Task scheduling based on swarm intelligence algorithms in high performance computing environment

Chai, Xuqing

doi:10.1007/s12652-020-01994-0

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…The previous DJS algorithms have been discussed by many researchers. All of the previous works used only continuous variables of the jobs (i.e., memory size) without considering the effect of the job's categorical variables such as Berger model, 53 particle swarm optimization, 54 energy optimization model, 55 firefly algorithm, 56 bees swarm, 57 and other models such as swarm intelligence algorithm [58][59][60] and data location aware model. 67 Moreover, the categorical variables of the jobs are used to make the job unique and special.…”

Section: Problem Statementmentioning

confidence: 99%

Scheduling of workflow jobs based on twostep clustering and lowest job weight

Al-Najjar

Alhady

Mohamad–Saleh

et al. 2021

Concurrency and Computation

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Scheduling is an important technique to improve the productivity of workflow applications in a grid system. Serving workflow applications added overhead on the scheduling system since the scheduler should choose the best zero-dependent job that improves the performance of a grid system. This paper proposes two dependent job scheduling algorithms based on analyzing the effect of categorical and continuous variables of the job, which are used in the calculation of job weight. To find the job weight, twostep clustering is used with 10 groups and the ranking equation. In addition, to verify the ability to apply the proposed algorithms in a real environment, weighted least squares estimation is used. The results showed that the prediction rate is equal to 99.88%, which indicates that the proposed algorithms could be applied in a real grid system with low overhead. Through simulations and after testing the proposed algorithms, the average results showed that the Dependent Job Scheduling (DJS) algorithm outperformed the previous algorithms, in total execution time and an average waiting time with an improvement value of 1.18 and 1.92 times, respectively. While DJS algorithm with weighting factor (DJSJP) outperformed the previous algorithms in total execution time only with an improvement value equal to 1.17 times. The overall results indicated that the proposed algorithms are efficient to be used in a grid system, besides four bits are sufficient to improve the performance of the job scheduling system.

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