Partition-based workload scheduling in living data warehouse environments

The demand for so-called living or real-time data warehouses is increasing in many application areas such as manufacturing, event monitoring and telecommunications. In these fields, users normally expect short response times for their queries and high freshness for the requested data. However, meeting these fundamental requirements is challenging due to the high loads and the continuous flow of write-only updates and read-only queries that might be in conflict with each other. Therefore, we present the concept of workload balancing by election (WINE), which allows users to express their individual demands on the quality of service and the quality of data, respectively. WINE exploits these information to balance and prioritize both types of transactions—queries and updates—according to the varying user needs. A simulation study shows that our proposed algorithm outperforms competing baseline algorithms over the entire spectrum of workloads and user requirements.