Reducing the Tail Latency of Microservices Applications via Optimal Configuration Tuning

G. Somashekar,A. Suresh,S. Tyagi, V. Dhyani, K Donkada, A. Pradhan,A. Gandhi

2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)(2022)

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The microservice architecture is an architectural style for designing applications that supports a collection of fine-grained and loosely-coupled services, called microservices, enabling independent development and deployment. An undesirable complexity that results from this style is the large state space of possibly inter-dependent configuration parameters (of the constituent microservices) which have to be tuned to improve application performance.This paper investigates optimization algorithms to address the problem of configuration tuning of microservices applications. To address the critical issue of large state space, practical dimensionality reduction strategies are developed based on available system characteristics. The evaluation of the optimization algorithms and dimensionality reduction techniques across three popular benchmarking applications highlights the importance of configuration tuning to reduce tail latency (by as much as 46%). A detailed analysis of the efficacy of different dimensionality reduction techniques in capturing the most important parameters is performed using ANOVA techniques. Results show that the right combination of optimization algorithms and dimensionality reduction can provide substantial latency improvements by identifying the right subset of parameters to tune, reducing the search space by as much as 83%.
ML for systems,microservices,configuration tuning,optimization,dimensionality reduction,tail latency
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