Simulation of In-Memory Database Workload: Markov Chains versus Relative Invocation Frequency and Equal Probability-A Trade-off between Accuracy and Time

In the last years, performance modeling approaches have been proposed to tackle new concepts for modern In-Memory Database Systems (IMDB). While these approaches model specific performance-relevant aspects, workload representation during performance modeling is considered only marginally. Furthermore, the manual integration of workload into modeling approaches comes along with high effort and requires deep domain-specific knowledge. This paper presents our experience in representing workload within performance models for IMDB. In particular, we use a Markov chain-based approach to extract and reflect probabilistic user behavior during performance modeling. An automatic model generation process is integrated to simplify and reduce the effort for transferring workload characteristics from traces to performance models. In an experimental series running analytical and transactional workloads on an IMDB, we compare this approach with two other methods which rely on less granular data to reflect database workload within performance models, namely reproducing the relative invocation frequency of queries and using the same query execution probability. The results reveal a trade-off between accuracy and speed when simulating database workload. Markov chains are the most accurate independent from workload characteristics, but the relative invocation frequency approach is appropriate for scenarios where simulation speed is important.