Scheduling-Guided Automatic Processing of Massive Hyperspectral Image Classification on Cloud Computing Architectures

The large data volume and high algorithm complexity of hyperspectral image (HSI) problems have posed big challenges for efficient classification of massive HSI data repositories. Recently, cloud computing architectures have become more relevant to address the big computational challenges introduced...

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Vydané v:	IEEE transactions on cybernetics Ročník 51; číslo 7; s. 3588 - 3601
Hlavní autori:	Wu, Zebin, Sun, Jin, Zhang, Yi, Zhu, Yaoqin, Li, Jun, Plaza, Antonio, Benediktsson, Jon Atli, Wei, Zhihui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Classification Cloud computing distributed and parallel processing divisible task scheduling Heuristic methods hyperspectral image (HSI) classification Hyperspectral imaging Image classification Optimization Parallel processing partitioning factor Processor scheduling Scheduling Sparks Task analysis Task scheduling
ISSN:	2168-2267, 2168-2275, 2168-2275
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Abstract	The large data volume and high algorithm complexity of hyperspectral image (HSI) problems have posed big challenges for efficient classification of massive HSI data repositories. Recently, cloud computing architectures have become more relevant to address the big computational challenges introduced in the HSI field. This article proposes an acceleration method for HSI classification that relies on scheduling metaheuristics to automatically and optimally distribute the workload of HSI applications across multiple computing resources on a cloud platform. By analyzing the procedure of a representative classification method, we first develop its distributed and parallel implementation based on the MapReduce mechanism on Apache Spark. The subtasks of the processing flow that can be processed in a distributed way are identified as divisible tasks. The optimal execution of this application on Spark is further formulated as a divisible scheduling framework that takes into account both task execution precedences and task divisibility when allocating the divisible and indivisible subtasks onto computing nodes. The formulated scheduling framework is an optimization procedure that searches for optimized task assignments and partition counts for divisible tasks. Two metaheuristic algorithms are developed to solve this divisible scheduling problem. The scheduling results provide an optimized solution to the automatic processing of HSI big data on clouds, improving the computational efficiency of HSI classification by exploring the parallelism during the parallel processing flow. Experimental results demonstrate that our scheduling-guided approach achieves remarkable speedups by facilitating the automatic processing of HSI classification on Spark, and is scalable to the increasing HSI data volume.
AbstractList	The large data volume and high algorithm complexity of hyperspectral image (HSI) problems have posed big challenges for efficient classification of massive HSI data repositories. Recently, cloud computing architectures have become more relevant to address the big computational challenges introduced in the HSI field. This article proposes an acceleration method for HSI classification that relies on scheduling metaheuristics to automatically and optimally distribute the workload of HSI applications across multiple computing resources on a cloud platform. By analyzing the procedure of a representative classification method, we first develop its distributed and parallel implementation based on the MapReduce mechanism on Apache Spark. The subtasks of the processing flow that can be processed in a distributed way are identified as divisible tasks. The optimal execution of this application on Spark is further formulated as a divisible scheduling framework that takes into account both task execution precedences and task divisibility when allocating the divisible and indivisible subtasks onto computing nodes. The formulated scheduling framework is an optimization procedure that searches for optimized task assignments and partition counts for divisible tasks. Two metaheuristic algorithms are developed to solve this divisible scheduling problem. The scheduling results provide an optimized solution to the automatic processing of HSI big data on clouds, improving the computational efficiency of HSI classification by exploring the parallelism during the parallel processing flow. Experimental results demonstrate that our scheduling-guided approach achieves remarkable speedups by facilitating the automatic processing of HSI classification on Spark, and is scalable to the increasing HSI data volume. The large data volume and high algorithm complexity of hyperspectral image (HSI) problems have posed big challenges for efficient classification of massive HSI data repositories. Recently, cloud computing architectures have become more relevant to address the big computational challenges introduced in the HSI field. This article proposes an acceleration method for HSI classification that relies on scheduling metaheuristics to automatically and optimally distribute the workload of HSI applications across multiple computing resources on a cloud platform. By analyzing the procedure of a representative classification method, we first develop its distributed and parallel implementation based on the MapReduce mechanism on Apache Spark. The subtasks of the processing flow that can be processed in a distributed way are identified as divisible tasks. The optimal execution of this application on Spark is further formulated as a divisible scheduling framework that takes into account both task execution precedences and task divisibility when allocating the divisible and indivisible subtasks onto computing nodes. The formulated scheduling framework is an optimization procedure that searches for optimized task assignments and partition counts for divisible tasks. Two metaheuristic algorithms are developed to solve this divisible scheduling problem. The scheduling results provide an optimized solution to the automatic processing of HSI big data on clouds, improving the computational efficiency of HSI classification by exploring the parallelism during the parallel processing flow. Experimental results demonstrate that our scheduling-guided approach achieves remarkable speedups by facilitating the automatic processing of HSI classification on Spark, and is scalable to the increasing HSI data volume.The large data volume and high algorithm complexity of hyperspectral image (HSI) problems have posed big challenges for efficient classification of massive HSI data repositories. Recently, cloud computing architectures have become more relevant to address the big computational challenges introduced in the HSI field. This article proposes an acceleration method for HSI classification that relies on scheduling metaheuristics to automatically and optimally distribute the workload of HSI applications across multiple computing resources on a cloud platform. By analyzing the procedure of a representative classification method, we first develop its distributed and parallel implementation based on the MapReduce mechanism on Apache Spark. The subtasks of the processing flow that can be processed in a distributed way are identified as divisible tasks. The optimal execution of this application on Spark is further formulated as a divisible scheduling framework that takes into account both task execution precedences and task divisibility when allocating the divisible and indivisible subtasks onto computing nodes. The formulated scheduling framework is an optimization procedure that searches for optimized task assignments and partition counts for divisible tasks. Two metaheuristic algorithms are developed to solve this divisible scheduling problem. The scheduling results provide an optimized solution to the automatic processing of HSI big data on clouds, improving the computational efficiency of HSI classification by exploring the parallelism during the parallel processing flow. Experimental results demonstrate that our scheduling-guided approach achieves remarkable speedups by facilitating the automatic processing of HSI classification on Spark, and is scalable to the increasing HSI data volume.
Author	Li, Jun Zhu, Yaoqin Sun, Jin Wu, Zebin Benediktsson, Jon Atli Zhang, Yi Plaza, Antonio Wei, Zhihui
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Cites_doi	10.1109/TPDS.2009.62 10.1023/A:1020958815308 10.1109/TCYB.2018.2825598 10.1109/JSTARS.2019.2951725 10.1007/BF01009452 10.1007/s10732-010-9136-0 10.1109/TPWRS.2010.2059716 10.1109/JSTARS.2016.2542193 10.1109/LGRS.2015.2441631 10.1109/36.3001 10.1109/TEVC.2002.804320 10.1109/TCYB.2015.2453359 10.1109/TGRS.2004.831865 10.1109/WHISPERS.2010.5594963 10.1109/CVPR.2011.5995323 10.1109/TGRS.2011.2176341 10.1109/JPROC.2016.2598228 10.1109/TGRS.2011.2162649 10.1109/TCYB.2018.2810806 10.1109/JSTARS.2014.2341811 10.1109/TGRS.2013.2286195 10.1109/LGRS.2010.2046618 10.1109/TCYB.2016.2605044 10.1109/TCYB.2015.2484324 10.1109/TCYB.2018.2864670 10.1145/1327452.1327492 10.1109/JSTARS.2015.2413831 10.1016/j.sysarc.2020.101799 10.1109/TPDS.2010.204 10.1109/TGRS.2018.2890513 10.1109/TGRS.2017.2691906 10.1007/978-3-030-01449-0_21 10.1109/TPWRS.2008.2004743 10.1109/JSTARS.2015.2413931 10.1109/JSTARS.2016.2603120 10.1109/JSTARS.2019.2911987 10.1016/j.rse.2007.07.028
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References	ref13 ref34 ref12 ref15 ref36 ref31 ref30 magoules (ref24) 2012 ref33 ref11 ref10 kennedy (ref28) 2002 ref2 ref1 ref39 ref17 ref16 ref19 wu (ref14) 2015; 12 ref18 ref23 ref26 ref20 ref42 ref41 ref22 ref44 el-rewini (ref25) 2007 haut (ref32) 2017 xun (ref35) 2007; 34 ref21 ref43 ref27 ref29 ref8 ref7 zaharia (ref38) 2012 ref9 ref4 ref3 ryza (ref37) 2015 ref6 ref5 ref40
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SubjectTerms	Algorithms Classification Cloud computing distributed and parallel processing divisible task scheduling Heuristic methods hyperspectral image (HSI) classification Hyperspectral imaging Image classification Optimization Parallel processing partitioning factor Processor scheduling Scheduling Sparks Task analysis Task scheduling
Title	Scheduling-Guided Automatic Processing of Massive Hyperspectral Image Classification on Cloud Computing Architectures
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