Euro-Par 2012: Parallel Processing Workshops

Annis, Io Annis C Ar Agi; Ander, Mich Ael Alex; A, Ros A M Ari A B Adi; Aro, Matti; An, Alex Andru Cost; Anelutto, M Arco D; Desprez, Fr d ric; Ammer, Bettin A Kr; Sahuquillo, Julio; Scott, Stephen L.; Weidendorfer, Josef

doi:10.1007/978-3-642-36949-0

Cited by 5 publications

(1 citation statement)

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“…26 Ostermann and Prodan proposed the "DCP-C" strategy, which considers Amazon EC2 spot instances and assigns the workflow tasks onto them on an on-demand basis. 27 The budget, cost, VM latency, and reliability are considered by DCP-C. However, it avoids the data transfer cost.…”

Section: Related Workmentioning

confidence: 99%

Cost‐effective deadline‐aware stochastic scheduling strategy for workflow applications on virtual machines in cloud computing

Haidri

Katti

Saxena

2018

Concurrency and Computation

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Summary This paper addresses the problems in scheduling the workflow tasks on cloud computing systems such as minimizing the total price for execution (TPE) and total execution time (TET) of the workflow while meeting the deadline constraints in a stochastic environment. Scheduling such precedence‐constrained stochastic tasks on the cloud with virtual machines of different computing capabilities is a difficult problem. However, instead of TPE and TET, the virtual machine's acquisition delay is one of the primary cloud's characteristics. The current paper first formulates the problem as a stochastic scheduling model on cloud. Then, a stochastic cost‐effective deadline‐aware (S‐CEDA) resource scheduler is developed. S‐CEDA incorporates the expected value and variance of the task's processing time and inter‐task communication time into the workflow scheduling. The experimental results show that S‐CEDA outperforms the existing state‐of‐the‐art algorithms such as stochastic heterogeneous earliest finish time (SHEFT) and cost‐effective deadline‐aware (CEDA) scheduling algorithms in terms of the TPE and TET of the workflow.

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Section: Related Workmentioning

confidence: 99%

Cost‐effective deadline‐aware stochastic scheduling strategy for workflow applications on virtual machines in cloud computing

Haidri

Katti

Saxena

2018

Concurrency and Computation

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A Micro-benchmark Suite for Evaluating HDFS Operations on Modern Clusters

Islam

Wasi-ur-Rahman

et al. 2014

Specifying Big Data Benchmarks

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GPU acceleration of splitting schemes applied to differential matrix equations

2019

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We consider differential Lyapunov and Riccati equations, and generalized versions thereof. Such equations arise in many different areas and are especially important within the field of optimal control. In order to approximate their solution, one may use several different kinds of numerical methods. Of these, splitting schemes are often a very competitive choice. In this article, we investigate the use of graphical processing units (GPUs) to parallelize such schemes and thereby further increase their effectiveness. According to our numerical experiments, large speed-ups are often observed for sufficiently large matrices. We also provide a comparison between different splitting strategies, demonstrating that splitting the equations into a moderate number of subproblems is generally optimal.

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Euro-Par 2012: Parallel Processing Workshops

Cited by 5 publications

References 0 publications

Cost‐effective deadline‐aware stochastic scheduling strategy for workflow applications on virtual machines in cloud computing

Cost‐effective deadline‐aware stochastic scheduling strategy for workflow applications on virtual machines in cloud computing

A Micro-benchmark Suite for Evaluating HDFS Operations on Modern Clusters

GPU acceleration of splitting schemes applied to differential matrix equations

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