Harnessing Elastic Resource Allocation in Cloud Computing for Scalable Real-Time Analytics in Distributed Systems
Keywords:
Elastic resource allocation, cloud computing, real-time analytics, distributed systems, scalability, workload prediction, container orchestrationAbstract
Real-time analytics in distributed systems has emerged as a cornerstone in modern data-driven applications, particularly in sectors like finance, healthcare, and IoT. As the demand for instant insights from massive data streams grows, the need for scalable, efficient infrastructure becomes critical. This paper explores how elastic resource allocation in cloud computing enhances the scalability and performance of real-time analytics in distributed systems. By dynamically adjusting computing resources based on workload fluctuations, cloud platforms ensure optimal performance and cost-efficiency. We examine architectural models, resource management frameworks, and performance trade-offs, culminating in a proposed framework that integrates predictive scaling techniques with container orchestration platforms
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