| Abstrakt: |
In recent years, container-based cloud virtualization solutions have emerged to mitigate the performance gap between non-virtualized and virtualized physical resources. However, there is a noticeable absence of techniques for predicting microservice performance in current research, which impacts cloud service users' ability to determine when to provision or de-provision microservices. Predicting microservice performance poses challenges due to overheads associated with actions such as variations in processing time caused by resource contention, which potentially leads to user confusion. In this paper, we propose, develop, and validate a probabilistic architecture named Microservice Performance Diagnosis and Prediction (MPDP). MPDP considers various factors such as response time, throughput, CPU usage, and other metrics to dynamically model interactions between microservice performance indicators for diagnosis and prediction. Using experimental data from our monitoring tool, stakeholders can build various networks for probabilistic analysis of microservice performance diagnosis and prediction and estimate the best microservice resource combination for a given Quality of Service (QoS) level. We generated a dataset of microservices with 2726 records across four benchmarks including CPU, memory, response time, and throughput to demonstrate the efficacy of the proposed MPDP architecture. We validate MPDP and demonstrate its capability to predict microservice performance. We compared various Bayesian networks such as the Noisy-OR Network (NOR), Naive Bayes Network (NBN), and Complex Bayesian Network (CBN), achieving an overall accuracy rate of 89.98% when using CBN. [ABSTRACT FROM AUTHOR] |
