SI-CL-SDEO algorithm for improving HDFS performance and data reliability

• Enhanced HDFS performance through dynamic replication and optimized data placement. • Integrates SSO and DE for balanced exploration and accelerated convergence. • Improved data availability, fault tolerance, and reduced network congestion. • Minimized read/write latencies for efficient distributed data storage operations. • Outperforms existing techniques across diverse load conditions and replication factors. In distributed file systems, optimizing data block storage and replication is essential for improving system reliability and system performance. Current optimization strategies often fall short in trading off critical metrics such as data availability, execution time, fault tolerance, and network utilization. To resolve these issues, we suggest that the SI-CL-SDEO (Swarm Intelligence - Chaotic Leader and Salp Differential Evolution Optimization) algorithm be particularly designed for the Hadoop Distributed File System (HDFS). The aim is to build an optimization strategy, which can greatly enhance the efficiency and stability of HDFS. The SI-CL-SDEO combines salp swarm optimization and differential evolution algorithms to focus on superior performance across multiple parameters. The performance of the proposed algorithm is evaluated through a wide-ranging set of metrics, such as data availability, execution time, fault tolerance, packet delivery ratio (PDR), network utilization, and average delay for reads and writes. Comparative performance analysis under different load conditions showed that, compared with other existing strategies SI–CL-SDEO obtained high performance. The Key performance highlights include a 40 % higher data availability at high replication factors, a 20 % reduction in execution time under low to medium load conditions, a 5–10 % improvement in fault tolerance over current methods, a 96.98 % optimization of network traffic under low load conditions, and a 160 ms average read latency and a 162 ms average write latency under medium load with higher replication factors. These outcomes confirm that the SI-CL-SDEO method is effective in enhancing HDFS's dependability and performance, giving it a robust solution to the demands of modern distributed data storage.