On the Effect of Applying the Task Clustering for Identical Processor Utilization to Heterogeneous Systems

Kanemitsu, Hidehiro; Lee, Gilhyon; Nakazato, Hidenori; Hoshiai, Takashige; Urano, Yoshiyori

doi:10.5772/35842

Cited by 4 publications

(7 citation statements)

References 12 publications

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“…In our previous work [10,11], we presented a processor assignment strategy for processor utilization. The number of processors is limited by imposing the lower bound for each cluster size.…”

Section: Previous Workmentioning

confidence: 99%

“…We propose a task scheduling method after completion of task clustering and processor assignment to the clusters, leveraging worst schedule length (WSL) [10,11] affecting schedule length. It works with conventional clustering heuristics and improves their schedule length, using the actual processor time and the communication bandwidth of the assigned processor.…”

Section: Introductionmentioning

confidence: 99%

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A Task Scheduling Method after Clustering for Data Intensive Jobs in Heterogeneous Distributed Systems

Hajikano

Kanemitsu

Kim

et al. 2016

Journal of Computing Science and Engineering

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Several task clustering heuristics are proposed for allocating tasks in heterogeneous systems to achieve a good response time in data intensive jobs. However, one of the challenging problems is the process in task scheduling after task allocation by task clustering. We propose a task scheduling method after task clustering, leveraging worst schedule length (WSL) as an upper bound of the schedule length. In our proposed method, a task in a WSL sequence is scheduled preferentially to make the WSL smaller. Experimental results by simulation show that the response time is improved in several task clustering heuristics. In particular, our proposed scheduling method with the task clustering outperforms conventional list-based task scheduling methods.

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Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Task Scheduling Method after Clustering for Data Intensive Jobs in Heterogeneous Distributed Systems

Hajikano

Kanemitsu

Kim

et al. 2016

Journal of Computing Science and Engineering

Self Cite

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“…If a processor assignment can make WSL smaller, both the upper bound and the lower bound of the actual schedule length can be made smaller [13]. The schedule length cannot be obtained until every task execution order is determined.…”

Section: Wsl (Worst Schedule Length)mentioning

confidence: 99%

“…Then each task is clustered in order to minimize the schedule length by reducing the Worst Schedule Length (WSL). According to the literature in [13], minimizing WSL leads to the reduction of both the lower bound and the upper bound of the schedule length. Then each task is ordered by Ready Critical Path (RCP) scheduling [14] on each assigned processor.…”

Section: Introductionmentioning

confidence: 99%

On the Performance of a Clustering-based Task Scheduling in a Heterogeneous System

Kanemitsu¹,

Hanada²,

Weyulu³

et al. 2017

Adv. sci. technol. eng. syst. j.

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Recent task scheduling algorithms for a generalized workflow job in heterogeneous system adopt list-based scheduling. In those algorithms, the response time cannot be effectively reduced if the given workflow job is data-intensive. If the workflow job is computationally intensive, an attempt is made to assign tasks to many processors, which can lead to resource starvation. To this end, a task scheduling algorithm that is based on clustering tasks, called CMWSL (Clustering for Minimizing the Worst Schedule Length) has been proposed. In CMWSL, the lower bound of the assignment unit size for each processor is derived in order to suppress the total number of executing processors for effective use of processors. After the lower bound is derived, the processor as a assignment target is determined and then the assignment unit as a task cluster is generated. As a final phase of CMWSL, task ordering is performed for every assigned task. In this paper, we compare several task ordering methods in CMWSL in a real environment to find the best task ordering policy.

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“…Let us return to the definition of sl w (G R cls , ϕ R ), whose details of the derivation are defined in table. I(for more details, see [12]). As can be seen in table.…”

Section: B Cluster Selectionmentioning

confidence: 99%

Effective use of computational resources in multicore distributed systems

Kanemitsu

Hanada²,

Hoshiai

et al. 2014

16th International Conference on Advanced Communication Technology

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Abstract-In the last decades, many kinds of task execution models such as grid and cloud computing have been developed. In such distributed systems, each task is processed by respective processor in multicored computers e.g., household PCs which we can easily harness in recent years. If there is one policy to automatically decide the "best" combination and the number of processors (and computers), we effectively utilize those computational resources, thereby large number of jobs can be executed in parallel. In this paper, we propose a method for mapping of execution units for such environments. The method adopts a remapping technology after processor-execution unit mapping[11] is finished. Experimental comparisons by a simulation show the advantages of the proposed method.

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On the Effect of Applying the Task Clustering for Identical Processor Utilization to Heterogeneous Systems

Cited by 4 publications

References 12 publications

A Task Scheduling Method after Clustering for Data Intensive Jobs in Heterogeneous Distributed Systems

A Task Scheduling Method after Clustering for Data Intensive Jobs in Heterogeneous Distributed Systems

On the Performance of a Clustering-based Task Scheduling in a Heterogeneous System

Effective use of computational resources in multicore distributed systems

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