Research on Dynamic Scheduling of Grid Monitoring Data Processing Tasks Based Storm

Shao Guang Yuan; Yong Li Zhu; Guo Liang Zhou; Ming Kun Wang

doi:10.4028/www.scientific.net/AMM.651-653.1051

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 651-653Research on Dynamic Scheduling of Grid Monitoring...

Research on Dynamic Scheduling of Grid Monitoring Data Processing Tasks Based Storm

Abstract:

Development of smart grid spawned the big data in electric power industry, the cloud computing platform provided the solution for the big data in electric power industry, it has a significant effect for batch jobs, but its real-time is not guaranteed. For the real-time problem of cloud computing platform, the Storm platform will be introduced to monitor the grid power. This paper studies the fair share scheduling algorithm under the Storm platform. It introduced the concept of Storm framework briefly, then, proposed the fair share scheduling algorithm according to the lack of current Storm scheduling algorithm, finally, the experiment proved that the scheduling algorithm based on fair share improved the resource utilization of Storm cluster and reduced the processing delay of the data.

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Periodical:

Applied Mechanics and Materials (Volumes 651-653)

Pages:

1051-1055

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.651-653.1051

Citation:

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Online since:

September 2014

Authors:

Shao Guang Yuan*, Yong Li Zhu, Guo Liang Zhou, Ming Kun Wang

Keywords:

Fair Share, Power Big Data, Scheduling, Smart Grid (SG), Storm

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* - Corresponding Author

References

[1] Song Ya-Qi, Zhou Guo-Liang, Zhu Yong-Li. Present Status and Challenges of Big Data Processing in Smart Grid[J] Power System Technology, 2013, 37(4): 927-935.

Google Scholar

[2] YAN Jing, WU Kai-Gui. Scheduling Algorithm for Instance-intensive Cloud Workflow[J]. Journal of Computer Applications, 2010(11): 2864-2866.

DOI: 10.3724/sp.j.1087.2010.02864

Google Scholar

[3] Kenneth P, Birman et. al. Running Smart Grid Control Software on Cloud Computing Architectures[C]. Workshop on Computational Needs for the Next Generation Electric Grid, Cornell University, April 19-20, 2011. Ithaca, NY.

Google Scholar

[4] Bertogna, M. Scuola Superiore Sant Anna Pisa. al Schedulability Analysis of Global Scheduling Algorithms on Mutiprocessor Platforms[J] Paralel and Distributed System . 2009, 20(4): 553-566.

DOI: 10.1109/tpds.2008.129

Google Scholar

[5] Anderson, J. H, Bud . al An EDF-Based Scheduling Algorithm for Multiprocessor Soft real-time Systems[C] Real-Time Systems, 2005. (ECRTS 2005). Proceedings. 17th Euromicro Conference on. 2005: 199-209.

DOI: 10.1109/ecrts.2005.6

Google Scholar