Performance Modeling of Distributed Data Processing in Microservice Applications

Microservice applications are increasingly adopted in distributed data processing systems, such as in mobile edge computing and data mesh architectures. However, existing performance models of such systems fall short in providing comprehensive insights into the intricate interplay between data placement and data processing. To address these issues, this paper proposes a novel class of performance models that enables joint analysis of data storage access workflows, caching, and queueing contention. Our proposed models introduce a notion of access path for data items to model hierarchical data locality constraints. We develop analytical solutions to efficiently approximate the performance metrics of these models under different data caching policies, finding in particular conditions under which the underlying Markov chain admits a product-form solution. Extensive trace-driven simulations based on real-world datasets indicate that service and data placement policies based on our proposed models can respectively improve by up to 35% and 37% the average response time in edge and data mesh case studies compared to baseline resource allocation heuristics.