TriCache: Providing three-tier caching for time series data in serverless healthcare services

Healthcare services and IoT, as highlighted by Hu et al. [9], generate enormous volumes of time series data. Using caching in serverless functions can significantly reduce latency and improve performance when storing frequently accessed data in memory. Although several approaches offer improvements, such as the use of in-memory caching, data prediction, and distributed systems, none of them fully addresses the need for a robust and efficient system for time series in healthcare, leaving a gap in necessary data availability and optimization. The TriCache model proposes a three-tier caching system to optimize storage and access to time series data in healthcare serverless functions, using a combination of memory in the serverless function, in-memory cache, and disk storage, in addition to predictive intelligence. The main contribution of the model is the significant reduction in latency and the improvement in the hit rate by efficiently predicting and allocating data across different cache layers. Experiments demonstrated a notable reduction in response time, with a 110 millisecond decrease in the 99th percentile. Additionally, the model performed significantly, achieving a 93% hit rate, compared to the 78% observed in the traditional model.