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MACHINE LEARNING-BASED TECHNIQUES FOR PROVIDING FOCUS TO PROBLEMATIC COMPUTE RESOURCES REPRESENTED VIA A DEPENDENCY GRAPH
MACHINE LEARNING-BASED TECHNIQUES FOR PROVIDING FOCUS TO PROBLEMATIC COMPUTE RESOURCES REPRESENTED VIA A DEPENDENCY GRAPH
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机译:基于机器学习的技术,用于提供焦点以通过依赖图表示的问题计算资源
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摘要
Methods, systems, apparatuses, and computer-readable storage mediums are described for machine learning-based techniques for reducing the visual complexity of a dependency graph that is representative of an application or service. For example, the dependency graph is generated that comprises a plurality of nodes and edges. Each node represents a compute resource (e.g., a microservice) of the application or service. Each edge represents a dependency between nodes coupled thereto. A machine learning-based classification model analyzes each of the nodes to determine a likelihood that each of the nodes is a problematic compute resource. For instance, the classification model may output a score indicative of the likelihood that a particular compute resource is problematic. The nodes and/or edges having a score that exceed a predetermined threshold are provided focus via the dependency graph.
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