Evaluating the Integration of Edge Computing and Serverless Architectures for Enhancing Scalability and Sustainability in Cloud-based Big Data Management

This study evaluates the integration of edge computing and serverless architectures to enhance scalability and sustainability in cloud-based big data management systems. With the exponential increase in data from internet-connected devices, traditional cloud computing faces challenges in scalability, cost-efficiency, and environmental impact. This research utilized a simulated environment to compare traditional cloud setups with integrated edge and serverless architectures under conditions typical for smart city applications. The simulation focused on four key performance metrics: latency, operational costs, energy consumption, and throughput. Results indicated a substantial decrease in latency, with a reduction from 149.73 ms to 88.94 ms during peak hours, enhancing real-time data processing capabilities essential for time-sensitive applications. Operational costs were significantly lowered by approximately 30%, attributed to the dynamic resource allocation of serverless architectures that reduce financial waste. Additionally, the integration showed a notable reduction in energy consumption and carbon emissions, highlighting the potential for these technologies to contribute positively to environmental sustainability. Lastly, the enhanced scalability of the integrated system was evident, with throughput increasing by 50%, proving its effectiveness in handling large volumes of data and user requests efficiently. The findings suggest that the synergistic use of edge computing and serverless architectures could revolutionize big data management across various sectors, offering improvements in performance, cost-efficiency, and environmental sustainability.