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Review Papers | Computer Science and Information Technology | United States of America | Volume 13 Issue 5, May 2024 | Popularity: 5.7 / 10

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Navigating Key-Value Stores in Cloud Edge Computing and IoT

Shyam Burkule, Ramprasad Chinthekindi, Senthilbharanidhar Boganavijayakumar

Abstract: Embedded key-value stores play a crucial role in enabling efficient data management in Internet of Things(IoT) and edge computing contexts, where local processing and storage capabilities are vital. The primary objective of this study is to investigate the management of key-value stores in cloud edge computing and the IoT. The study utilized the Systematic Literature Review technique. This study conducted a thorough analysis of various embedded key-value stores, such as RocksDB, LevelDB, and LMDB, to assess their applicability and performance in limited contexts. In addition, the study examined optimization tactics specifically designed for embedded systems, including compression algorithms, wear levelling approaches for non-volatile memory, and energy-efficient data operations that are crucial for battery-powered devices. The results demonstrated clear trade-offs between performance and resource utilization among the examined key-value stores. This provides valuable guidance for developers and system architects when choosing and optimizing embedded databases based on specific application needs and environmental limitations. This study enhanced the field by offering a comprehensive analysis of the performance characteristics of embedded key-value stores in IoT and edge environments. This analysis lays the foundation for developing data management solutions that are optimized, resilient, and resource conscious. Consequently, in future, it could help in creating more intelligent and efficient IoT ecosystems.

Keywords: IoT, Key Value Stores, Cloud Edge Computing, Data Storage, Data Management

Edition: Volume 13 Issue 5, May 2024

Pages: 1324 - 1328

DOI: https://www.doi.org/10.21275/SR24521024239

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