Optimization of Hadoop Cloud Backup System ( HD ‐ CDBS ) for Power Minute Freeze Data Based on ABC Algorithm

Efficient backup of power minute‐level frozen data are of utmost importance for the stable operation of the power grid. However, existing backup methods suffer from long time delays due to network transmission and encryption processes. This paper proposes an optimized Hadoop Cloud Backup System (HD‐CDBS) for efficient power‐minute‐level frozen data backup, addressing delays, high‐energy consumption, and poor load balancing in existing methods. Leveraging the Artificial Bee Colony (ABC) algorithm, the system employs a three‐tier architecture: edge nodes collect real‐time data, the server side optimizes task scheduling using a multi‐objective model balancing computation delay, energy consumption, and node performance, while the client side ensures dynamic load balancing. The ABC algorithm enhances global search capabilities, improving task allocation efficiency. Experimental results demonstrate that the optimized HD‐CDBS achieves rapid backup with minimal energy usage, effectively overcoming the ‘delay‐energy consumption’ trade‐off in large‐scale power data backup. The study offers a scalable solution for smart grid real‐time data management and significantly improves power grid fault recovery efficiency.