A new heuristic for minimizing schedule length in heterogeneous computing systems

Sirisha, D.; Kumari, G. Vijaya

doi:10.1109/icecct.2015.7226067

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…We also clarify their disadvantages when they are applied to systems with a large number of processors and when not all processors are used for execution. DAG scheduling for heterogeneous systems is categorized into two classes in terms of the task assignment policy: listbased heuristics [3,4,5,16,17] and task clustering [7,8,9,10,11]. For list-based heuristics, both HEFT [3] and Predict Earliest Finish Time (PEFT) [4] use the average processing time and average communication time to derive the scheduling priority for each free task whose predecessor tasks have been scheduled.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Sirisha and Kumari [25], proposed a new heuristic approach namely Minimizing Schedule Length (MSL) for list based task scheduling in Heterogeneous Computing Systems (HCS). The proposed algorithm (MSL) consists of two phases namely, the task ordering phase and task mapping phase.…”

Section: New Heuristic For Minimizing Schedule Lengthmentioning

confidence: 99%

Evaluation Performance of Task Scheduling Algorithms in Heterogeneous Environments

Yazdanpanah¹,

Shouraki²,

Jamali³

2016

IJCA

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A heterogeneous computing environment is a large-scale distributed data processing environment, it is depends to some extent parameters on the application and that classified in three main categories such as the hardware, the communication layer, and the software. A computer system is consists of hardware and software from two or more different manufacturers. Scheduling is one of the important factors in the heterogeneous environment and the aim of task scheduling in the processing environment is to move computation towards data. In order to achieve improve performance, increase the throughput and minimizing the makespan; scheduler must avoid unnecessary data transmission. Hence, different scheduling algorithms for heterogeneous computing environment are necessary to provide good performance. How to speedup scheduling the service resources to achieve the lowest cost becomes more and more important. This paper tries to illustrate and analyze the overview of eighteen different scheduling algorithms for heterogeneous computing environment and their scheduling issues and problems.

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Prior node selection for scheduling workflows in a heterogeneous system

Kanemitsu

Hanada

Nakazato

2017

Journal of Parallel and Distributed Computing

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A new heuristic for minimizing schedule length in heterogeneous computing systems

Cited by 7 publications

References 11 publications

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

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

Evaluation Performance of Task Scheduling Algorithms in Heterogeneous Environments

Prior node selection for scheduling workflows in a heterogeneous system

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