Systems and methods for detecting and remedying software anomalies

A method executed by a computing system, the method comprising:

• obtaining a set of observed data vectors indicating operation of a topology of nodes that represents a given software application while the given software application is running on a first computing platform that is not controlled by a provider of the given software application, wherein each observed data vector in the set comprises data values captured for a given set of operating variables at a particular point in time;
• applying an anomaly detection model to the obtained set of observed data vectors, wherein the anomaly detection model is:
• (i) defined by applying an unsupervised learning technique to a set of training data vectors indicating operation of the topology of nodes that represents the given software application while the given software application is running on a second computing platform that is controlled by the provider of the given software application, and (ii) configured to evaluate whether a deviation between observed and predicted values for each operating variable of the given set of operating variables is indicative of an anomaly;
• based on the anomaly detection model, identifying an anomaly in at least one operating variable in the given set of operating variables;
• evaluating whether any identified anomaly is indicative of a problem related to the given software application and thereby determining that at least one identified anomaly is indicative of a problem related to the given software application;
• and
• based on determining that the at least one identified anomaly is indicative of a problem related to the given software application, causing a client station to present a notification indicating the at least one identified anomaly.

A non-transitory computer-readable medium having instructions stored thereon that are executable by at least one processor to cause a computing system to:

• obtain a set of observed data vectors indicating operation of a topology of nodes that represents a given software application while the given software application is running on a first computing platform that is not controlled by a provider of the given software application, wherein each observed data vector in the set comprises data values captured for a given set of operating variables at a particular point in time;
• apply an anomaly detection model to the obtained set of observed data vectors, wherein the anomaly detection model is:
• (i) defined by applying an unsupervised learning technique to a set of training data vectors indicating operation of the topology of nodes that represents the given software application while the given software application is running on a second computing platform that is controlled by the provider of the given software application, and (ii) configured to evaluate whether a deviation between observed and predicted values for each operating variable of the given set of operating variables is indicative of an anomaly;
• based on the anomaly detection model, identify an anomaly in at least one operating variable in the given set of operating variables;
• evaluate whether any identified anomaly is indicative of a problem related to the given software application and thereby determine that at least one identified anomaly is indicative of a problem related to the given software application;
• and
• based on the determination that the at least one identified anomaly is indicative of a problem related to the given software application, cause a client station to present a notification indicating the at least one identified anomaly.