Conflict resolution in nested transactions using ConvLTSM and Crow search algorithm in distributed optical data center databases with fiber delay lines and load balancing

Abstract This paper addresses the issue of conflict resolution in nested transactions within distributed databases of optical data centers, where efficient data processing and management are crucial. We propose a novel approach using conflict resolution in nested transactions via ConvLTSM (Convolutional long short-term memory) along with the Crow Search algorithm (CSA) for optimizing database performance. A key component of our approach is the integration of fiber delay lines (FDLs), which play a critical role in enhancing system efficiency by minimizing overall delays and ensuring optimal resource utilization. The use of FDLs helps in controlling the timing of data transmission, ensuring that transactions are processed in a timely manner, and reducing bottlenecks within the system. The incorporation of FDLs along with load balancing techniques significantly improves the throughput of nested transactions, reduces latency, and ensures consistency across the distributed system. By optimizing the timing and distribution of transaction data, FDLs facilitate better resource management, leading to more efficient and reliable database operations. Our experimental results, with a load of 0.6 and a buffering of 10, show that the transaction loss probability is 7.27 × 10 −5 and the average delay is 1.54 slots. These results demonstrate the effectiveness of the proposed approach in mitigating conflicts, improving data integrity, and optimizing the performance of optical data centers.