Adaptive Cache Optimization Integrating Spatiotemporal Analysis and Sliding Modules

Cache-enabled networks present challenges in managing rapidly changing information demand and accommodating diverse user preferences. This paper proposes a cache placement strategy named SMAC. SMAC is specifically tailored for video scenes and comprehensively considers the spatiotemporal characteristics of contents. By deeply analyzing the characteristics of data across three dimensions: platform, style, and theme of videos, SMAC can accurately capture and predict demand patterns. Additionally, SMAC introduces a cache threshold adaptive adjustment mechanism based on a sliding module. The mechanism dynamically adjusts the content placement level of caching according to changes in user preferences over time. Experimental results indicate that, compared to some common strategies, under various experimental conditions, SMAC can increase the hit ratio by 3-8%, reduce server load by 5-30%, and decrease total delay by 3-22%. The demonstration of these network performance validates the effectiveness of the SMAC.