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Workload Management Systems for the Cloud Environment

Workload Management Systems for the Cloud Environment

Eman A. Maghawry, Rasha M. Ismail, Nagwa. L. Badr, Mohamed F. Tolba
Copyright: © 2017 |Pages: 20
ISBN13: 9781522522294|ISBN10: 1522522298|EISBN13: 9781522522300
DOI: 10.4018/978-1-5225-2229-4.ch005
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MLA

Maghawry, Eman A., et al. "Workload Management Systems for the Cloud Environment." Handbook of Research on Machine Learning Innovations and Trends, edited by Aboul Ella Hassanien and Tarek Gaber, IGI Global, 2017, pp. 94-113. https://doi.org/10.4018/978-1-5225-2229-4.ch005

APA

Maghawry, E. A., Ismail, R. M., Badr, N. L., & Tolba, M. F. (2017). Workload Management Systems for the Cloud Environment. In A. Hassanien & T. Gaber (Eds.), Handbook of Research on Machine Learning Innovations and Trends (pp. 94-113). IGI Global. https://doi.org/10.4018/978-1-5225-2229-4.ch005

Chicago

Maghawry, Eman A., et al. "Workload Management Systems for the Cloud Environment." In Handbook of Research on Machine Learning Innovations and Trends, edited by Aboul Ella Hassanien and Tarek Gaber, 94-113. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2229-4.ch005

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Abstract

Workload Management is a performance management process in which an autonomic database management system on a cloud environment efficiently makes use of its virtual resources. Workload management for concurrent queries is one of the challenging aspects of executing queries over the cloud. The core problem is to manage any unpredictable overload with respect to varying resource capabilities and performances. This chapter proposes an efficient workload management system for controlling the queries execution over a cloud. The chapter presents architecture to improve the query response time. It handles the user's queries then selecting the suitable resources for executing these queries. Furthermore, managing the life cycle of virtual resources through responding to any load that occurs on the resources. This is done by dynamically rebalancing the queries distribution load across the resources in the cloud. The results show that applying this Workload Management System improves the query response time by 68%.

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