QoS-Based Network Service Broker for High-Performance Grid Applications

Author(s):  
Qiang Duan ◽  
Michelle Talley ◽  
Neeta Seetha
2019 ◽  
Vol 9 (21) ◽  
pp. 4541
Author(s):  
Syed Asif Raza Shah ◽  
Seo-Young Noh

Large scientific experimental facilities currently are generating a tremendous amount of data. In recent years, the significant growth of scientific data analysis has been observed across scientific research centers. Scientific experimental facilities are producing an unprecedented amount of data and facing new challenges to transfer the large data sets across multi continents. In particular, these days the data transfer is playing an important role in new scientific discoveries. The performance of distributed scientific environment is highly dependent on high-performance, adaptive, and robust network service infrastructures. To support large scale data transfer for extreme-scale distributed science, there is the need of high performance, scalable, end-to-end, and programmable networks that enable scientific applications to use the networks efficiently. We worked on the AmoebaNet solution to address the problems of a dynamic programmable network for bulk data transfer in extreme-scale distributed science environments. A major goal of the AmoebaNet project is to apply software-defined networking (SDN) technology to provide “Application-aware” network to facilitate bulk data transfer. We have prototyped AmoebaNet’s SDN-enabled network service that allows application to dynamically program the networks at run-time for bulk data transfers. In this paper, we evaluated AmoebaNet solution with real world test cases and shown that how it efficiently and dynamically can use the networks for bulk data transfer in large-scale scientific environments.


2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Mukhtaj Khan ◽  
Zhengwen Huang ◽  
Maozhen Li ◽  
Gareth A. Taylor ◽  
Phillip M. Ashton ◽  
...  

The rapid deployment of Phasor Measurement Units (PMUs) in power systems globally is leading to Big Data challenges. New high performance computing techniques are now required to process an ever increasing volume of data from PMUs. To that extent the Hadoop framework, an open source implementation of the MapReduce computing model, is gaining momentum for Big Data analytics in smart grid applications. However, Hadoop has over 190 configuration parameters, which can have a significant impact on the performance of the Hadoop framework. This paper presents an Enhanced Parallel Detrended Fluctuation Analysis (EPDFA) algorithm for scalable analytics on massive volumes of PMU data. The novel EPDFA algorithm builds on an enhanced Hadoop platform whose configuration parameters are optimized by Gene Expression Programming. Experimental results show that the EPDFA is 29 times faster than the sequential DFA in processing PMU data and 1.87 times faster than a parallel DFA, which utilizes the default Hadoop configuration settings.


Smart Grids ◽  
2017 ◽  
pp. 511-532
Author(s):  
Yousu Chen ◽  
Huang Zhenyu (Henry) ◽  
Yousu Chen ◽  
Zhenyu (Henry) Huang

Author(s):  
Chao Li ◽  
Yuebin Bai ◽  
Yujun Chen ◽  
Shujuan Liu ◽  
Lei Gong ◽  
...  

2003 ◽  
Vol 1 (4) ◽  
pp. 329-343 ◽  
Author(s):  
Miguel Rio ◽  
Andrea di Donato ◽  
Frank Saka ◽  
Nicola Pezzi ◽  
Richard Smith ◽  
...  

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