Efficient and scalable device management is crucial in modern IoT and enterprise environments, where the number of connected devices grows exponentially. This paper explores the utilization of Azure Serverless technologies, including Azure Functions and Logic Apps, to build an automated, scalable, and cost-effective device management framework. By integrating sustainable development principles, the proposed solution emphasizes energy efficiency, reduced operational overhead, and optimized resource usage throughout the device lifecycle. The study demonstrates how serverless architectures enable rapid scalability while minimizing environmental impact through pay-per-use models and automated resource deallocation. Quantitative results from real-world deployments illustrate improvements in system responsiveness, reduced latency, and lower carbon footprint compared to traditional device management methods. This research contributes to the advancement of sustainable IT practices in large-scale automated device management.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Automated Device Management at Scale: Leveraging Azure Serverless Technologies for Sustainable Development

  • Anup Rao

摘要

Efficient and scalable device management is crucial in modern IoT and enterprise environments, where the number of connected devices grows exponentially. This paper explores the utilization of Azure Serverless technologies, including Azure Functions and Logic Apps, to build an automated, scalable, and cost-effective device management framework. By integrating sustainable development principles, the proposed solution emphasizes energy efficiency, reduced operational overhead, and optimized resource usage throughout the device lifecycle. The study demonstrates how serverless architectures enable rapid scalability while minimizing environmental impact through pay-per-use models and automated resource deallocation. Quantitative results from real-world deployments illustrate improvements in system responsiveness, reduced latency, and lower carbon footprint compared to traditional device management methods. This research contributes to the advancement of sustainable IT practices in large-scale automated device management.