A hybrid filtering approach for storage optimization in main-memory cloud database

Abstract

Enterprises and cloud service providers face dramatic increase in the amount of data stored in private and public clouds. Thus, data storage costs are growing hastily because they use only one single high-performance storage tier for storing all cloud data. There's considerable potential to reduce cloud costs by classifying data into active (hot) and inactive (cold). In the main-memory databases research, recent works focus on approaches to identify hot/cold data. Most of these approaches track tuple accesses to identify hot/cold tuples. In contrast, we introduce a novel Hybrid Filtering Approach (HFA) that tracks both tuples and columns accesses in main-memory databases. Our objective is to enhance the performance in terms of three dimensions: storage space, query elapsed time and CPU time. In order to validate the effectiveness of our approach, we realized its concrete implementation on Hekaton, a SQL's server memory-optimized engine using the well-known TPC-H benchmark. Experimental results show that the proposed HFA outperforms Hekaton approach in respect of all performance dimensions. In specific, HFA reduces the storage space by average of 44�96%, reduces the query elapsed time by average of 25�93% and reduces the CPU time by average of 31�97% compared to the traditional database approach. � 2015