An expert system for redesigning software for cloud applications
نویسندگان
چکیده
Cloud-based software has many advantages. When services are divided into independent components, they easier to update. Also, during peak demand, it is scale cloud (just hire more CPUs). Hence, organizations partitioning their monolithic enterprise applications cloud-based microservices. Recently there been much work using machine learning simplify this task. Despite research, no single method can be recommended as generally useful. More specifically, those prior solutions “brittle”; i.e. if well for one kind of goal in dataset, then sub-optimal applied datasets and multiple goals. This extends proposes DEEPLY fix the brittleness problem. Specifically, we use (a) hyper-parameter optimization sample from Pareto frontier configurations (b) a weighted loss choose optimally (c) 1cycle rate policy avoid local minima with Adam (d) spectral clustering over k-means. Our shows that outperforms other algorithms space across different metrics. Moreover, our ablation study reveals changes, most important, followed by (contrary belief). To enable reuse available on-line at .
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ژورنال
عنوان ژورنال: Expert Systems With Applications
سال: 2023
ISSN: ['1873-6793', '0957-4174']
DOI: https://doi.org/10.1016/j.eswa.2023.119673