Evaluating the performance of Microservices Architectures (MSA) is essential to ensure their proper functioning and meet end-user satisfaction. For MSA performance analysts, one of the most challenging tasks is to determine the cause of any deviation of relevant metrics from the specified range. We introduce CAR-PT (CAusal-Reasoning-driven Performance Testing), a model-based technique for workload generation designed for the performance testing of MSA. CAR-PT leverages causal reasoning to effectively explore the s pace o f operational conditions, with the goal of identifying those that lead to performance issues. Preliminary results show that CAR-PT is effective in generating configurations for discovering performance issues of an MSA.
Identifying Performance Issues in Microservice Architectures through Causal Reasoning
Antonio Guerriero;Cristian Mascia
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2024
Abstract
Evaluating the performance of Microservices Architectures (MSA) is essential to ensure their proper functioning and meet end-user satisfaction. For MSA performance analysts, one of the most challenging tasks is to determine the cause of any deviation of relevant metrics from the specified range. We introduce CAR-PT (CAusal-Reasoning-driven Performance Testing), a model-based technique for workload generation designed for the performance testing of MSA. CAR-PT leverages causal reasoning to effectively explore the s pace o f operational conditions, with the goal of identifying those that lead to performance issues. Preliminary results show that CAR-PT is effective in generating configurations for discovering performance issues of an MSA.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
