Optimizing Computational Throughput in Metagraphy Via Integrated Dna and Kamla Frameworks

Abstract

The increasing demand for secure data transmission has driven innovations in metagraphic systems, where computational efficiency remains a critical bottleneck. This research addresses throughput optimization in metagraphic information processing by integrating DNA-based encoding with KAMLA (Key-based Advanced Multiple Layer Algorithm) frameworks. Traditional metagraphic systems suffer from computational overhead that limits real-time applications, particularly when processing large data volumes or operating on resource-constrained devices. The proposed integrated framework leverages DNA sequencing's parallel processing capabilities alongside KAMLA's optimized key generation to achieve significant throughput improvements. Through systematic experimentation across varying data sizes and complexity levels, the study demonstrates that the DNA-KAMLA integration reduces processing time by approximately 58% compared to conventional metagraphic methods while maintaining security standards. Performance benchmarking reveals that the optimized system achieves throughput rates of 2.47 Mbps for encoding operations and 3.12 Mbps for decoding operations on standard hardware configurations. The framework's scalability testing across multiple platforms confirms consistent performance gains ranging from 52% to 64% depending on system architecture. These findings contribute practical solutions for high-throughput secure communication systems where speed and security must coexist without compromise.