Developing Cloud-Native Autonomous Systems for Real-Time Edge Analytics
Ayisha Tabbassum, Siddharth Parakh -, Arun Pandiyan Perumal, Pradeep Chintale · 2024
The cloud-native data analytics platforms for autonomous systems are thoroughly examined in this research, together with their theoretical underpinnings, methodological frameworks, empirical findings, and practical consequences. We analyze the landscape of current architectures, frameworks, and implementations using qualitative desk research, illuminating significant trends, obstacles, and possibilities in this field. Our research highlights how crucial cloud-Nativestrategies are to providing adaptable, scalable, and interoperable data analytics solutions, giving businesses the ability to avoid vendor lock-in and maximize resource use. We emphasize how crucial industry partnerships and standardization initiatives are to promoting interoperability and accelerating innovation. We also examine the mutually beneficial link that exists between theoretical frameworks and real-world applications, emphasizing the necessity of co-creating and verifying theoretically based and empirically proven solutions. In the future, we explore developments that might change the autonomous system environment, such edge analytics and federated learning. Through the utilization of the study's findings, interested parties may effectively negotiate the intricacies involved in platform design, deployment, and management. This can propel innovation and enhance the potential of autonomous systems in a variety of sectors.