Abstract
ContextThe microservices architectural style is gaining momentum in the IT industry. This style does not guarantee that a target system can continuously meet acceptable performance levels. The ability to study the violations of performance requirements and eventually predict them would help practitioners to tune techniques like dynamic load balancing or horizontal scaling to achieve the resilience property.ObjectiveThe goal of this work is to study the violations of performance requirements of microservices through time series analysis and provide practical instruments that can detect resilient and non-resilient microservices and possibly predict their performance behavior.MethodWe introduce a new method based on growth theory to model the occurrences of violations of performance requirements as a stochastic process. We applied our method to an in-vitro e-commerce benchmark and an in-production real-world telecommunication system. We interpreted the resulting growth models to characterize the microservices in terms of their transient performance behavior.ResultsOur empirical evaluation shows that, in most of the cases, the non-linear S-shaped growth models capture the occurrences of performance violations of resilient microservices with high accuracy. The bounded nature associated with this models tell that the performance degradation is limited and thus the microservice is able to come back to an acceptable performance level even under changes in the nominal number of concurrent users. We also detect cases where linear models represent a better description. These microservices are not resilient and exhibit constant growth and unbounded performance violations over time. The application of our methodology to a real in-production system identified additional resilience profiles that were not observed in the in-vitro experiments. These profiles show the ability of services to react differently to the same solicitation. We found that when a service is resilient it can either decrease the rate of the violations occurrences in a continuous manner or with repeated attempts (periodical or not).ConclusionsWe showed that growth theory can be successfully applied to study the occurences of performance violations of in-vitro and in-production real-world systems. Furthermore, the cost of our model calibration heuristics, based on the mathematical expression of the selected non-linear growth models, is limited. We discussed how the resulting models can shed some light on the trend of performance violations and help engineers to spot problematic microservice operations that exhibit performance issues. Thus, meaningful insights from the application of growth theory have been derived to characterize the behavior of (non) resilient microservices operations.
Highlights
Over the last few years, microservices emerged as the architectural style of choice in a variety of application domains in the IT industry
We showed that growth theory can be successfully applied to study the occurences of performance violations of in-vitro and in-production real-world systems
We introduce a novel approach to model the occurrences of performance violations of microservices systems based on stochastic models of growth theory, which have been for long successfully used in the context of reliability analysis (e.g., Virene (1968), AbdelGhaly et al (1986), Musa et al (1987), Succi et al (2003a), Taber and Port (2014), Rossi
Summary
Over the last few years, microservices emerged as the architectural style of choice in a variety of application domains in the IT industry. The microservices paradigm is an approach for developing a single application as a suite of small black-box services, each running in its own process and adopting lightweight messaging protocols like HTTP/REST (Lewis and Fowler 2014). These services are built around business capabilities and they are independently deployable by automated procedures inside “continuous development/delivery” pipelines. A performance requirement is typically defined according to usability engineering practices (Nielsen 1994) or explicit contracts with the customer (e.g., service level agreement). We propose to adopt the method introduced by (Avritzer et al 2018) to automatically derive the performance requirement by empirically estimating the threshold value which defines the acceptable behavior of each microservice
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