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pro vyhledávání: '"Ha Huong"'
Microservice architecture has become a popular architecture adopted by many cloud applications. However, identifying the root cause of a failure in microservice systems is still a challenging and time-consuming task. In recent years, researchers have
Externí odkaz:
http://arxiv.org/abs/2408.13729
Detecting failures and identifying their root causes promptly and accurately is crucial for ensuring the availability of microservice systems. A typical failure troubleshooting pipeline for microservices consists of two phases: anomaly detection and
Externí odkaz:
http://arxiv.org/abs/2405.09330
Publikováno v:
Transactions on Machine Learning Research 2024
Bayesian Optimization (BO) is an effective method for finding the global optimum of expensive black-box functions. However, it is well known that applying BO to high-dimensional optimization problems is challenging. To address this issue, a promising
Externí odkaz:
http://arxiv.org/abs/2402.03104
Gaussian process (GP) based Bayesian optimization (BO) is a powerful method for optimizing black-box functions efficiently. The practical performance and theoretical guarantees of this approach depend on having the correct GP hyperparameter values, w
Externí odkaz:
http://arxiv.org/abs/2306.06844
Publikováno v:
Journal of Asian Business and Economic Studies, 2024, Vol. 31, Issue 2, pp. 124-136.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JABES-08-2023-0289
Configurable software systems are employed in many important application domains. Understanding the performance of the systems under all configurations is critical to prevent potential performance issues caused by misconfiguration. However, as the nu
Externí odkaz:
http://arxiv.org/abs/2212.13359
Autor:
Ha, Huong
Monitoring machine learning systems post deployment is critical to ensure the reliability of the systems. Particularly importance is the problem of monitoring the performance of machine learning systems across all the data subgroups (subpopulations).
Externí odkaz:
http://arxiv.org/abs/2212.08312