Empirical evaluation of multi-level buffer cache collaboration for storage systems

• Authors: Zhifeng Chen;Yan Zhang;Yuanyuan Zhou;Heidi Scott;Berni Schiefer
• Venue: SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
• Year: 2005

To bridge the increasing processor-disk performance gap, buffer caches are used in both storage clients (e.g. database systems) and storage servers to reduce the number of slow disk accesses. These buffer caches need to be managed effectively to deliver the performance commensurate to the aggregate buffer cache size. To address this problem, two paradigms have been proposed recently to collaboratively manage these buffer caches together: the hierarchy-aware caching maintains the same I/O interface and is fully transparent to the storage client software, and the aggressively-collaborative caching trades off transparency for performance and requires changes to both the interface and the storage client software. Before storage industry starts to implement collaborative caching in real systems, it is crucial to find out whether sacrificing transparency is really worthwhile, i.e., how much can we gain by using the aggressively-collaborative caching instead of the hierarchy-aware caching? To accurately answer this question, it is required to consider all possible combinations of recently proposed local replacement algorithms and optimization techniques in both collaboration paradigms.Our study provides an empirical evaluation to address the above questions. Particularly, we have compared three aggressively-collaborative approaches with two hierarchy-aware approaches for four different types of database/file I/O workloads using traces collected from real commercial systems such as IBM DB2. More importantly, we separate the effects of collaborative caching from local replacement algorithms and optimizations, and uniformly apply several recently proposed local replacement algorithms and optimizations to all five collaboration approaches.When appropriate local optimizations and replacement algorithms are uniformly applied to both hierarchy-aware and aggressively-collaborative caching, the results indicate that hierarchy-aware caching can deliver similar performance as aggressively-collaborative caching. The results show that the aggressively-collaborative caching only provides less than 2.5% performance improvement on average in simulation and 1.0% in real system experiments over the hierarchy-aware caching for most workloads and cache configurations. Our sensitivity study indicates that the performance gain of aggressively-collaborative caching is also very small for various storage networks and different cache configurations. Therefore, considering its simplicity and generality, hierarchy-aware caching is more feasible than aggressively-collaborative caching.