| 书目名称 | Optimized Cloud Based Scheduling |
| 编辑 | Rong Kun Jason Tan,John A. Leong,Amandeep S. Sidhu |
| 视频video | |
| 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 |
| 丛书名称 | Studies in Computational Intelligence |
| 图书封面 |  |
| 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 |
| 关键词 | Cloud Based Scheduling; Cloud Service Provisioning; Big Data; Big Data Analytics; Cloud Computing; Hybrid |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-73214-5 |
| isbn_softcover | 978-3-030-10333-0 |
| isbn_ebook | 978-3-319-73214-5Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer International Publishing AG 2018 |
发表于 2025-3-21 17:10:51
