Utilization-Driven Performance Enhancement in Storage Area Networks

Efficient resource utilization and low response times are critical challenges in storage area network (SAN) systems, especially as data-intensive applications like those driven by the Internet of Things and Artificial Intelligence place increasing demands on reliable, high-performance data storage solutions. Addressing these challenges, this paper contributes by proposing a proactive, utilization-driven traffic redistribution strategy to achieve balanced load distribution across switches, thereby improving the overall SAN performance and alleviating the risk of overload-incurred cascading failures. The proposed approach incorporates a Jackson Queueing Network-based method to evaluate both utilization and response time of individual switches, as well as the overall system response time. Based on a comprehensive case study of a mesh SAN system, two key parameters—the transition probability adjustment step size and the node selection window size—are analyzed for their impact on the effectiveness of the proposed strategy, revealing several valuable insights into fine-tuning traffic redistribution parameters.