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In the realm of Big Data processing, efficient data management is paramount for achieving optimal performance. Distributed caching plays a crucial role in mitigating data transfer overhead and enhancing computation speed in MapReduce frameworks. This paper proposes a novel approach for Efficient Distributed Cache Management in Big Data Processing with MapReduce. Our solution focuses on addressing the challenges associated with large-scale data processing by leveraging a distributed cache mechanism. By strategically managing and distributing cached data across the cluster, we aim to reduce the data transfer latency and improve the overall efficiency of MapReduce jobs. Key components of our approach include intelligent cache placement, dynamic cache updating, and adaptive eviction policies. We employ intelligent algorithms to determine the optimal placement of cached data based on the computation patterns and data access frequencies. Additionally, our system dynamically updates the cache contents to ensure that the most relevant and frequently accessed data is readily available for computation.Furthermore, we introduce adaptive eviction policies to manage the cache size dynamically, ensuring that the system adapts to varying workloads and resource constraints. The proposed solution is implemented and evaluated on a real-world Big Data processing environment, demonstrating significant improvements in performance compared to traditional approaches. In summary, our Efficient Distributed Cache Management in Big Data Processing with MapReduce presents a scalable and adaptable solution to enhance the efficiency of large-scale data processing tasks. The experimental results showcase the effectiveness of our approach in reducing computation time and data transfer overhead, making it a valuable contribution to the field of Big Data analytics.

Copyright © 2024 K Ramesh Babu. This is an open access article distributed under the Creative Commons Attribution License.