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Generation of microservices from a monolithic application based on runtime traces

專利號(hào)
US11176027B1
公開日期
2021-11-16
申請(qǐng)人
International Business Machines Corporation(US NY Armonk)
發(fā)明人
Jin Xiao; Anup Kalia; Chen Lin; Raghav Batta; Saurabh Sinha; John Rofrano; Maja Vukovic
IPC分類
G06F11/36; G06F11/32
技術(shù)領(lǐng)域
monolithic,or,runtime,can,model,cluster,causal,traces,generation,classes
地域: NY NY Armonk

摘要

Systems, computer-implemented methods, and computer program products to facilitate generation of microservices from a monolithic application based on runtime traces are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a model component that learns cluster assignments of classes in a monolithic application based on runtime traces of executed test cases. The computer executable components can further comprise a cluster component that employs the model component to generate clusters of the classes based on the cluster assignments to identify one or more microservices of the monolithic application.

說明書

In some embodiments, microservice generation system 102 can employ any suitable machine learning based techniques, statistical-based techniques, and/or probabilistic-based techniques to train model component 108 to learn the cluster assignments and/or graph embeddings described above using the causal sequences of the one or more causal graphs described above. For example, microservice generation system 102 can employ an expert system, fuzzy logic, support vector machine (SVM), Hidden Markov Models (HMMs), greedy search algorithms, rule-based systems, Bayesian models (e.g., Bayesian networks), neural networks, other non-linear training techniques, data fusion, utility-based analytical systems, systems employing Bayesian models, and/or another model. In some embodiments, microservice generation system 102 can perform a set of machine learning computations associated with training model component 108 to learn the cluster assignments and/or graph embeddings described above using the causal sequences of the one or more causal graphs described above. For example, microservice generation system 102 can perform a set of clustering machine learning computations, a set of logistic regression machine learning computations, a set of decision tree machine learning computations, a set of random forest machine learning computations, a set of regression tree machine learning computations, a set of least square machine learning computations, a set of instance-based machine learning computations, a set of regression machine learning computations, a set of support vector regression machine learning computations, a set of k-means machine learning computations, a set of spectral clustering machine learning computations, a set of rule learning machine learning computations, a set of Bayesian machine learning computations, a set of deep Boltzmann machine computations, a set of deep belief network computations, and/or a set of different machine learning computations to train model component 108 to learn the cluster assignments and/or graph embeddings described above using the causal sequences of the one or more causal graphs described above.

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