Horae: causal consistency model based on hot data governance

Causal consistency has attracted considerable attention in distributed systems because it meets the high availability and high-performance requirements in the presence of network partitions. Existing causal consistency models seldom pay attention to hot data governance and run the data stabilization process periodically and thus fail to meet the user requirements of real-time data and high concurrency. In response to this problem, this study proposes a model based on thermal data governance, the Horae model, which simplifies causal sequence verification by sorting, accelerates data stability, and optimizes model update visibility and read response latency. Furthermore, the Horae model stores hot data, reduces the number of partition loads, increases operation parallelism, and ultimately improves throughput. Theoretical analysis and simulation experiments show that the proposed model outperforms existing models in terms of throughput, read response time, and update visibility.