Dynamic Variant Rank HEFT Task Scheduling Algorithm Toward Exascle Computing

Sandokji, Suhelah; Eassa, Fathy

doi:10.1016/j.procs.2019.12.131

Cited by 12 publications

(14 citation statements)

References 16 publications

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“…Therefore, the computation cost of the task may vary, depending on the efficiency and the performance of the machine on which the task runs. Consequently, in the heterogeneous setting, there is a variety of different approaches to compute the node's weight, Thus, the scheme to compute the weight of a node Wi could be obtained as ad hock choice that may, in some cases, improve the execution time, but does not necessarily improve other cases [5,25]. Consequently, we obtained three schemes for computing the upward rank of the tasks.…”

Section: A the Static Partmentioning

confidence: 99%

“…Each one of the schemas of equations (2), (3), (4) give a different order task list. As a result, when having multiple choices of rank function (and the values it returns), the quality of the schedule produced would improve [5,25]. Thus, we suggest that the performance of HEFT can be improved by considering the three variant upward rank in the stage of prioritizing the tasks [5,6].…”

Section: )mentioning

confidence: 99%

“…As a result, when having multiple choices of rank function (and the values it returns), the quality of the schedule produced would improve [5,25]. Thus, we suggest that the performance of HEFT can be improved by considering the three variant upward rank in the stage of prioritizing the tasks [5,6]. Then we check the make span of the schedules produced by each scheme and take the shortest make span's schedule list and set it as the selected schedule.…”

Section: )mentioning

confidence: 99%

“…It also minimizes the overhead preprocessing computing of the scheduling algorithm itself. Therefore, in our previous work, we introduce the new hybrid scheduling algorithm dynamic variant heterogenous early finish time (DVR HEFT) [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Communication and Computation Aware Task Scheduling Framework Toward Exascale Computing

Sandokji¹,

Eassa²

2019

IJACSA

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The race for Exascale Computing has naturally led computer architecture to transit from the multicore era and into the heterogeneous era. Exascale Computing within the heterogenous environment necessarily use the best-fit scheduling and resource utilization improvement. Task scheduling is the main critical aspect in managing the challenges of Exascale in the heterogenous computing environment. In this paper, a Communication and Computation Aware task scheduler framework (CCATSF) is introduced. The CCATSF framework consists of four parts; the first of which is the resource monitor, the second is the resources manager, the third is the task scheduler and the fourth is the dispatcher. The framework is based on a new hybrid task scheduling algorithm for a heterogenous computing environment. Our results are based on the random job generator that we implemented, and they indicate that the CCATSF framework, based on the proposed dynamic variant heterogenous early finish time (DVR-HEFT) algorithm is able to reduce the scheduler's makespan and increase the efficiency without increasing the algorithm's time complicity.

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Section: A the Static Partmentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Communication and Computation Aware Task Scheduling Framework Toward Exascale Computing

Sandokji¹,

Eassa²

2019

IJACSA

Self Cite

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“…It also minimizes the scheduling algorithm's own latency and overhead pre-processing computation. We are therefore incorporating in our last work the hybrid scheduling algorithm dynamic variant heterogenous early finish time (DVR HEFT) [3] . In In this paper, we are continuing our DVR HEFT algorithm research and development with the introduction of the Task Scheduler (CCATSF).…”

Section: Introductionmentioning

confidence: 99%

Communication and Computation Aware Task Scheduling Framework for Heterogeneous Computing

Sandokji¹

2019

JKAU Comp IT Sci

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The heterogeneous Computing (HC) is the promised paradigm for high performance computing. In HC the vastly different architectures and programming models of each type of the computing unit, present several challenges in achieving collaborative computing. Task scheduling is the main critical aspect in managing these challenges. In this paper, a Communication and Computation Aware task scheduler framework (CCATSF) is introduced. The proposed task scheduling framework consist of four parts; the first of which is the resource monitor, the second is the resources manager, the third is the task scheduler and the fourth the dispatcher. We also introduce DVR-HEFT algorithm a new hybrid task scheduling algorithm, on which the framework is based. Our results indicate that CCATSF framework based on algorithm is able to reduce the scheduler's makespan without increasing the algorithm's time complicity.

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Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach

Shirvani

Talouki

2021

Complex Intell. Syst.

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Scheduling of scientific workflows on hybrid cloud architecture, which contains private and public clouds, is a challenging task because schedulers should be aware of task inter-dependencies, underlying heterogeneity, cost diversity, and virtual machine (VM) variable configurations during the scheduling process. On the one side, reaching a minimum total execution time or makespan is a favorable issue for users whereas the cost of utilizing quicker VMs may lead to conflict with their budget on the other side. Existing works in the literature scarcely consider VM’s monetary cost in the scheduling process but mainly focus on makespan. Therefore, in this paper, the problem of scientific workflow scheduling running on hybrid cloud architecture is formulated to a bi-objective optimization problem with makespan and monetary cost minimization viewpoint. To address this combinatorial discrete problem, this paper presents a hybrid bi-objective optimization based on simulated annealing and task duplication algorithms (BOSA-TDA) that exploits two important heuristics heterogeneous earliest finish time (HEFT) and duplication techniques to improve canonical SA. The extensive simulation results reported of running different well-known scientific workflows such as LIGO, SIPHT, Cybershake, Montage, and Epigenomics demonstrate that proposed BOSA-TDA has the amount of 12.5%, 14.5%, 17%, 13.5%, and 18.5% average improvement against other existing approaches in terms of makespan, monetary cost, speed up, SLR, and efficiency metrics, respectively.

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Dynamic Variant Rank HEFT Task Scheduling Algorithm Toward Exascle Computing

Cited by 12 publications

References 16 publications

Communication and Computation Aware Task Scheduling Framework Toward Exascale Computing

Communication and Computation Aware Task Scheduling Framework Toward Exascale Computing

Communication and Computation Aware Task Scheduling Framework for Heterogeneous Computing

Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach

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