Dynamic Evaluation of Microservice Granularity Adaptation

Microservices have gained acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. Among the critical microservice architecture design decisions is when to adapt granularity of a by merging/decomposing microservices. No existing work investigates following question: How can we reason about trade-off between predicted benefits cost pursuing adaptation under uncertainty? To address this question, provide novel formulation decision problem pursue real options problem. We propose evaluation process dynamically evaluating uncertainty. Our based on combination concept Bayesian surprises. show our comparing it four representative industrial runtime monitoring tools, which be used retrospective decisions. comparison shows that supersede and/or complement these tools. implement application—Filmflix—using Amazon Web Service Lambda use implementation case study unique benefit compared traditional application analysis.