Alea – Complex Job Scheduling Simulator

Klusáček, Dalibor; Soysal, Mehmet; Suter, Frédéric

doi:10.1007/978-3-030-43222-5_19

Cited by 20 publications

(8 citation statements)

References 17 publications

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“…The cluster consists of 184 nodes, with a total of 1,664 processors. We run simulations with the workload using a job scheduling simulator, called Alea [30], which is based on the GridSim toolkit [31]. Since no urgent jobs are defined in the workload, we randomly inject an urgent job at busy times in different simulated months.…”

Section: B a Motivating Example With Tsunami Simulationsmentioning

confidence: 99%

Preemptive Parallel Job Scheduling for Heterogeneous Systems Supporting Urgent Computing

et al. 2021

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Dedicated infrastructures are commonly used for urgent computations. However, using dedicated resources is not always affordable due to budget constraints. As a result, utilizing shared infrastructures becomes an alternative solution for urgent computations. Since the infrastructures are meant to serve many users, the urgent jobs may arrive when regular jobs are using the necessary resources. In such a case, it is necessary to preempt the regular jobs so that urgent jobs can be executed immediately. Most conventional methods for job scheduling have focused on reducing the response times and waiting times of all jobs. However, these methods can delay urgent jobs and hinder them from being completed within a stipulated deadline. Furthermore, in heterogeneous systems with coprocessors, preemption becomes more difficult because coprocessors rely on several system software functionalities provided by the host processor. In this paper, we propose a parallel job scheduling method to effectively use shared heterogeneous systems for urgent computations. Our method employs an in-memory process swapping mechanism to preempt jobs running on the coprocessor devices. The results of our simulations show that our method can achieve a significant reduction in the response time and slowdown of regular jobs without substantial delays of urgent jobs. INDEX TERMS job scheduling, urgent computing, heterogeneous systems, preemption, process swapping.

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Section: B a Motivating Example With Tsunami Simulationsmentioning

confidence: 99%

Preemptive Parallel Job Scheduling for Heterogeneous Systems Supporting Urgent Computing

et al. 2021

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“…The Alea [50] is a simulator that emulates scheduling in parallel HPC clusters based on the newest GridSim (GridSim v5.0) toolkit. It inherits the features of the GridSim simulator, making the job scheduling and mapping challenges on HPCs easier to set and run.…”

Section: Simulation and Evaluationmentioning

confidence: 99%

ROA-CONS: Raccoon Optimization for Job Scheduling

et al. 2021

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High-performance computing comprises thousands of processing powers in order to deliver higher performance computation than a typical desktop computer or workstation in order to solve large problems in science, engineering, or business. The scheduling of these machines has an important impact on their performance. HPC’s job scheduling is intended to develop an operational strategy which utilises resources efficiently and avoids delays. An optimised schedule results in greater efficiency of the parallel machine. In addition, processes and network heterogeneity is another difficulty for the scheduling algorithm. Another problem for parallel job scheduling is user fairness. One of the issues in this field of study is providing a balanced schedule that enhances efficiency and user fairness. ROA-CONS is a new job scheduling method proposed in this paper. It describes a new scheduling approach, which is a combination of an updated conservative backfilling approach further optimised by the raccoon optimisation algorithm. This algorithm also proposes a technique of selection that combines job waiting and response time optimisation with user fairness. It contributes to the development of a symmetrical schedule that increases user satisfaction and performance. In comparison with other well-known job scheduling algorithms, the simulation assesses the effectiveness of the proposed method. The results demonstrate that the proposed strategy offers improved schedules that reduce the overall system’s job waiting and response times.

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“…To assess the impact of these modications on the overall utilization and the waiting times experienced by jobs, we implemented a simulator using the Alea job scheduling simulator [1,10] which is based on the GridSim toolkit [13]. Alea allows for detailed simulations, supports several mainstream scheduling algorithms (e.g., FCFS, variants of Backlling, Fair-Share) and commonly used system restrictions such as queue limits and quotas.…”

Section: Revisiting the Conguration Of The Batch Systemmentioning

confidence: 99%

Improving Fairness in a Large Scale HTC System Through Workload Analysis and Simulation

Azevedo

Klusáček

Suter

2019

Lecture Notes in Computer Science

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Monitoring and analyzing the execution of a workload is at the core of the operation of data centers. It allows operators to verify that the operational objectives are satised or detect and react to any unexpected and unwanted behavior. However, the scale and complexity of large workloads composed of millions of jobs executed each month on several thousands of cores, often limit the depth of such an analysis. This may lead to overlook some phenomena that, while not harmful at a global scale, can be detrimental to a specic class of users. In this paper, we illustrate such a situation by analyzing a large High Throughput Computing (HTC) workload trace coming from one of the largest academic computing centers in France. The Fair-Share algorithm at the core of the batch scheduler ensures that all user groups are fairly provided with an amount of computing resources commensurate to their expressed needs. However, a deeper analysis of the produced schedule, especially of the job waiting times, shows a certain degree of unfairness between user groups. We identify the conguration of the quotas and scheduling queues as the main root causes of this unfairness. We thus propose a drastic reconguration of the system that aims at being more suited to the characteristics of the workload and at better balancing the waiting time among user groups. We evaluate the impact of this reconguration through detailed simulations. The obtained results show that it still satises the main operational objectives while signicantly improving the quality of service experienced by formerly unfavored users.

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Alea – Complex Job Scheduling Simulator

Cited by 20 publications

References 17 publications

Preemptive Parallel Job Scheduling for Heterogeneous Systems Supporting Urgent Computing

Preemptive Parallel Job Scheduling for Heterogeneous Systems Supporting Urgent Computing

ROA-CONS: Raccoon Optimization for Job Scheduling

Improving Fairness in a Large Scale HTC System Through Workload Analysis and Simulation

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