Abstract

Optimizing data transfer and communication overheads is essential for enhancing the performance and efficiency of cloud-based big data processing systems, where large volumes of data are transferred and processed across distributed computing resources. This paper investigates strategies and techniques for minimizing data transfer and communication overheads in cloud-based big data processing environments. We explore various approaches, including data compression, network bandwidth optimization, data locality-aware scheduling, and efficient data serialization formats. Additionally, we examine the impact of factors such as network latency, data skew, and system heterogeneity on data transfer and communication overheads. Through empirical analysis and case studies, we evaluate the effectiveness of different optimization techniques in reducing data transfer and communication overheads while maintaining processing efficiency and scalability in real-world cloud-based big data processing scenarios. Furthermore, we discuss challenges, trade-offs, and future research directions in optimizing data transfer and communication overheads for cloud-based big data processing systems. This study aims to provide practical insights and guidelines for practitioners and researchers in mitigating performance bottlenecks and improving the efficiency of data transfer and communication in cloud-based big data processing environments.

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