BSCache: A Brisk Semantic Caching Scheme for Cloud-based Performance Monitoring Timeseries Systems

Abstract

Cloud-based performance monitoring timeseries systems are emerging due to cloud’s flexibility and pay-as-you-go capabilities. For such systems, caching is particularly important considering the limited bandwidth and long access latency of the cloud storage. However, existing cache schemes, such as with external cache systems (e.g. Memcached or Redis), are not specially designed for timeseries data and thus provide suboptimal performance. In this paper, we propose BSCache, a novel lightweight semantic cache mechanism for cloud-based performance monitoring timeseries systems, which is a variant of semantic cache specially designed for timeseries workloads. BSCache supports semantic-aware, metadata-data mixed in-memory management so it can significantly improve timeseries query performance. We have implemented a fully-functional, open-source prototype of BSCache and integrated it into Cortex, a distributed performance monitoring timeseries system widely adopted in industry. BSCache is compared with Memcached which is the default caching system in Cortex. Experimental results show that BSCache can significantly improve query performance with higher cache hit ratios and less CPU overhead under the same cache sizes compared with Memcached in Cortex.