Real-Time Data Processing in Cloud Computing: Challenges, Opportunities, and Performance Analysis

Abstract

Cloud computing has revolutionized the way data is stored, processed, and analyzed, enabling real-time applications across various domains such as healthcare, finance, and IoT. This study explores the capabilities and challenges of real-time data processing in cloud environments, focusing on performance metrics such as latency, scalability, and resource utilization. Hybrid cloud architecture was implemented to simulate real-time data streams, and experiments were conducted to evaluate the efficiency of processing frameworks like Apache Kafka and Apache Flink.  

The results indicate that cloud-based real-time processing systems achieve an average latency reduction of 42% compared to traditional on-premise solutions, with a throughput improvement of 35% under high data loads. However, challenges such as network latency, data security, and resource allocation were identified as critical bottlenecks, particularly in multi-tenant environments. Statistical analysis revealed a strong positive correlation (r = 0.85, p < 0.01) between the number of virtual machines allocated and system performance, highlighting the importance of dynamic resource scaling. Regression models further demonstrated that optimizing resource allocation could reduce processing delays by up to 28%.  

This study also highlights the potential of edge computing integration with cloud platforms to address latency issues, particularly for time-sensitive applications like autonomous vehicles and real-time analytics. The findings underscore the need for advanced load-balancing algorithms and security protocols to enhance the reliability and efficiency of real-time cloud systems. By addressing these challenges, cloud computing can unlock new opportunities for innovation in real-time data-driven applications