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(Download) "Efficient adaptive query processing on large database systems available in the cloud environment" by Clayton Maciel Costa & António Luís Pinto Ferreira de Sousa # eBook PDF Kindle ePub Free

Efficient adaptive query processing on large database systems available in the cloud environment

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eBook details

  • Title: Efficient adaptive query processing on large database systems available in the cloud environment
  • Author : Clayton Maciel Costa & António Luís Pinto Ferreira de Sousa
  • Release Date : January 15, 2020
  • Genre: Databases,Books,Computers & Internet,Programming,
  • Pages : * pages
  • Size : 20384 KB

Description

Nowadays, many companies are migrating their applications and data to cloud service providers, mainly because of their ability to answer quickly to business requirements. Thereby, the performance is an important requirement for most customers when they wish to migrate their applications to the cloud. Therefore, in cloud environments, resources should be acquired and released automatically and quickly at runtime. Moreover, the users and service providers expect to get answers in time to ensure the service SLA (Service Level Agreement). Consequently, ensuring the QoS (Quality of Service) is a great challenge and it increases when we have large amounts of data to be manipulated in this environment. To resolve this kind of problems, several researches have been focused on shorter execution time using adaptive query processing and/or prediction of resources based on current system status. However, they present important limitations. For example, most of these works does not use monitoring during query execution and/or presents intrusive solutions, i.e. applied to the particular context. The aim of this book is to present the development of new solutions/strategies to efficient adaptive query processing on large databases available in a cloud environment. It must integrate adaptive re-optimization at query runtime and their costs are based on the SRT (Service Response Time – SLA QoS performance parameter). Finally, the proposed solution will be evaluated on large scale with large volume of data, machines and queries in a cloud computing infrastructure. Finally, this work also proposes a new model to estimate the SRT for different request types (database access requests). This model will allow the cloud service provider and its customers to establish an appropriate SLA relative to the expected performance of the services available in the cloud.


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