Enabling High Performance and Resource Utilization in Clustered Cache via Hotness Identification, Data Copying, and Instance Merging

In-memory cache systems such as Redis provide low-latency and high-performance data access for modern internet services. However, in large-scale Redis systems, the workloads show strong skewness and varied locality, which degrades system performance and incurs low CPU utilization. Though there are many approaches toward load imbalance, the two-layered architecture of Redis makes its workload skewness show special characteristics. Redis first maps data into data groups, which is called Group Mapping. Then the data groups are distributed to instances by Instance Mapping. Under Redis's layered architecture, it gives rise to a small number of hot-spot instances with very limited hot data groups, as well as a large number of remaining cold instances. To improve Redis's performance and CPU utilization, it entails the accurate identification of instance and data group hotness, and handling hot data groups and cold instances. We propose HPUCache+ to address the hot-spot problem via hotness identification, hot data copying, and cold instance merging. HPUCache+ accurately and dynamically detects instance and data group hotness based on multiple resources and workload characteristics at low cost. It enables access to multiple data copies by dynamically updating the cached mapping in Redis client, achieving high user access performance with Redis client compatibility, while providing highly self-definable service level agreement. It also proposes an asynchronous instance merging strategy based on disk snapshots and temporal caches, which separates the massive data movement from the critical user access path to achieve high-performance instance merging. We implement HPUCache+ into Redis. Experiments show that, compared to the native Redis design, HPUCache+ achieves up to 2.3$\times$ and 3.5$\times$ throughput gains, 11.3$\times$ and 14.3$\times$ CPU utilization gains, respectively. It also achieves up to 50% less CPU and 75% less memory consumption compared to the state-of-the-art approach Anna.