An on-line hot data identification for flash-based storage using sampling mechanism

Efficient hot and cold data identification in computer systems has been a fundamental issue. However, it has been least investigated. In this paper, we propose a novel on-line hot data identification scheme for flash-based storage named HotDataTrap. The main idea is to maintain a working set of potential hot data items in a cache based on a sampling mechanism. This sampling-based scheme enables HotDataTrap to early discard some of the cold items so that it can reduce runtime overheads as well as a waste of memory spaces. Moreover, our two-level hash indexing scheme helps HotDataTrap directly look up a requested item in the cache and save a memory space further by exploiting spatial localities. Both our sampling approach and hierarchical hash indexing scheme empower HotDataTrap to precisely and efficiently identify hot data with a even less memory. Our extensive experiments with various realistic workloads demonstrate that our HotDataTrap outperforms the state-of-the-art scheme by an average of 335% and our two-level hash indexing scheme considerably improves further HotDataTrap performance up to 50.8%.