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Titlebook: Optimized Cloud Based Scheduling; Rong Kun Jason Tan,John A. Leong,Amandeep S. Sidhu Book 2018 Springer International Publishing AG 2018 C

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書目名稱Optimized Cloud Based Scheduling
編輯Rong Kun Jason Tan,John A. Leong,Amandeep S. Sidhu
視頻videohttp://file.papertrans.cn/704/703334/703334.mp4
概述Presents an improved design for service provisioning and allocation models in a hybrid cloud environment.Proposes approaches for addressing scheduling and performance issues in big data analytics.Show
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Optimized Cloud Based Scheduling; Rong Kun Jason Tan,John A. Leong,Amandeep S. Sidhu Book 2018 Springer International Publishing AG 2018 C
描述.This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics..
出版日期Book 2018
關(guān)鍵詞Cloud Based Scheduling; Cloud Service Provisioning; Big Data; Big Data Analytics; Cloud Computing; Hybrid
版次1
doihttps://doi.org/10.1007/978-3-319-73214-5
isbn_softcover978-3-030-10333-0
isbn_ebook978-3-319-73214-5Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer International Publishing AG 2018
The information of publication is updating

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Benchmarking,TAR). NECTAR is an Australian Government project to provide public cloud resources to Australian Universities. The other player of choice among both Industry and Academic Institutions is Amazon Public Cloud or commonly known as Amazon EC2 which is a subsidiary of retail giant Amazon.com. The main re
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Computation of Large Datasets,proving performance with reduced additional overhead since additional storage and computing power become easily available on demand. This capability will be demonstrated by the prototype created at the end of the project.
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Optimized Online Scheduling Algorithms,ta intensive applications in cloud environment as all are considered as complex algorithm which consumes relatively high amount of memory, bandwidth and computational power to maintain its data structure. The outcome of maintaining these data structures will cause the time of scheduling tasks unboun
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Conclusion and Future Works,onal biology and others in boosting trend. Due to the various characteristics of data, it is needed to develop a new data processing architecture for data acquisition, data analysis, data mining, data transmission between service instances, data storage and others, aiming to schedule it into a serie
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978-3-030-10333-0Springer International Publishing AG 2018
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