Traditional supercomputing centers usually adopt rigid job scheduling, which requires users to specify the amount of processors to use upon job submission and then allocates computing resources to each job according to the specified processor requirement. However, if the specified amount of processors is larger than current available resources, the job would have to wait while the available resources are kept idle, resulting in degraded resource utilization and job turnaround time. Most modern parallel applications, e.g. those written with MPI, usually have the moldable property, which allows them to exploit different parallelisms for execution at runtime. In such cases, the traditional rigid job scheduling is inappropriate. Therefore, moldable job scheduling becomes an important research topic which aims to improve the overall system performance through adaptive processor allocation taking advantage of the moldable property.
Moreover, as cloud computing emerges, recently the concept of HPC as a Service (HPCaaS) was proposed to transform HPC facilities and applications into a more convenient and accessible service model. For HPCaaS, users simply want to get their jobs done as soon as possible and don’t want to or even have no idea on how to specify an appropriate amount of processors for the application’s execution. Therefore, moldable job scheduling can contribute to HPCaaS in two important aspects. Firstly, it relieves users’ burden of selecting an appropriate number of processors upon job submission, leading to a much easier and convenient user experience for HPCaaS. Secondly, moldable job scheduling has the potential to improve the average turnaround time of parallel applications and the overall resource utilization, benefiting both HPCaaS users and providers.
In this thesis, we classify previous research on moldable job scheduling into four quadrants according to two aspects: submit-time or schedule-time decision and having job
runtime information or not. Based on this classification, we make three contributions to moldable job scheduling by taking advantage of the information of applications’ speedup models. The first contribution, called auxiliary moldable job scheduling, is a feasible extension to the usage model in most current HPC centers, which improves the overall system performance through limiting the maximum allowable amount of processors for each job according to the knee value calculated based on applications’ speedup models. In the second contribution, we proposed a moldable job scheduling approach for HPCaaS, which can automatically select a most appropriate amount of processors for a job’s execution based on applications’ speedup models and workload conditions at the moment, relieving users’ burden of selecting an appropriate number of processors upon job submission. The proposed approaches in the previous two contributions do not require job runtime information. In the third contribution, we propose an advanced moldable job scheduling approach, taking advantage of job runtime information to further improve the overall system performance.
The proposed moldable job scheduling approaches were evaluated through a series of simulation experiments, and compared to previous methods in the literature. The experimental results indicate that our approaches outperform existing methods significantly, achieving up to 83%, 78%, and 89% performance improvement in terms of average turnaround time, respectively.
Based on the results and experience in this thesis, two research directions are worthy of further exploration in the future. The first is evaluating the effects of inaccurate runtime estimation on the proposed moldable job scheduling methods, since job runtime plays an important role on allocation decisions. Although accurate runtime estimation is more probable in the HPCaaS usage scenarios than in traditional HPC platforms, 100% accuracy is still not
possible. Therefore, it is desirable to evaluate the influence of runtime estimation accuracy.
The second future research direction is to extend the proposed moldable job scheduling approach, when the parallel allocation policy is adopted, by exploring different resource partitioning strategies. In this thesis, a simple equal-partition strategy is used when the parallel allocation policy is applied, which means that each of the jobs, e.g. n jobs, in queue is allocated 1/n resources equally. Other possible partitioning strategies considering the workload difference among the jobs are worthy of further exploration and evaluation.
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