Abstract
In the present day, cloud computing plays a vital role towards technologies associated with service. The primary objective of cloud computing is to make people compute and store resources properly and effectively. Therefore to improve the performance in the cloud, it may require optimization towards processing data. It is obvious that cloud computing enhances sharing computing power as well as storage for a number of applications towards the database with heterogeneity. But it has been observed that the way a number of applications is influenced by various cloud platforms, the high scale generated data the data generated may be increased as well as consumed during the applications. Accordingly with the availability of virtual machines, cloud computing may enable users for the usage of resources to execute complex queries efficiently on large-scale data. The complete autonomy towards each node in the large database environments may be expected towards the services through external communication along with the experimentation towards optimizing query terms. Accordingly, unifying and authorization linked with the desired problem may be partially linked with specific points towards information retrieval along with its characteristics. In that scenario, the large database may be linked with the virtual server towards providing services to the relevant data. Also the database associated with the cloud may be associated with the various instances linked with different heterogeneous databases. Many techniques have already been presented linked to processing queries in cloud databases. In this paper, it has been proposed to optimize query processing linked with virtual data associated with virtual servers. Accordingly, the generation of queries along with the execution query plans may also attempt to optimize the performance of virtual databases.
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Prakash, M.J., Sourav, P.S., Kumar, M.S. (2020). Strategies and Performance Analysis of Queries Associated with Cloud Database. In: Borah, S., Emilia Balas, V., Polkowski, Z. (eds) Advances in Data Science and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-15-0978-0_22
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DOI: https://doi.org/10.1007/978-981-15-0978-0_22
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