Predictive Elastic Database Systems

On-line transaction processing (OLTP) database management systems (DBMSs) are a critical part of the operation of many large enterprises. These systems often serve time-varying workloads due to daily, weekly or seasonal fluctuations in demand, or because of rapid growth in demand due to a company’s business success. In addition, many OLTP workloads are heavily skewed to “hot” tuples or ranges of tuples. For example, the majority of NYSE volume involves only 40 stocks. To deal with such fluctuations, an OLTP DBMS needs to be elastic; that is, it must be able to expand and contract resources in response to load fluctuations and dynamically balance load as hot tuples vary over time. This project focuses on several different aspects of elasticity, including mechanisms for on-line data migration as well as algorithms for determining when to reconfigure and which data to move.