Example aspects include techniques for accurate and expeditious cloud service health communication to customers. These techniques may include determining that a service health incident has customer impact, the service health incident corresponding to an outage of one or more services of a cloud computing platform, identifying a plurality of customers impacted by the service health incident, and predicting, based on the service health incident and one or more other service health incidents, aggregated incident information identifying a plurality of service health incidents associated with the outage of the one or more services. In addition, the techniques may include identifying the one or more services associated with the service health incident, and transmitting, based at least in part on the aggregated incident information and the one or more services, a health notification to the plurality of customers.

In one embodiment, a method for margin determination for a computing system with a real time operating system and priority preemptive scheduling comprises: scheduling a set of tasks to be executed in one or more partitions, wherein each is assigned a priority, wherein the tasks comprise periodic and/or aperiodic tasks; executing the set of tasks on the computing system within the scheduled periodic time window; introducing an overhead task executed for an execution duration controlled either by the real time operating system or by the overhead task; controlling the overhead task to converge on a point of failure at which a length of the execution duration of the overhead task causes either: 1) a periodic task to fail to execute within a deadline, or 2) time available for the aperiodic tasks to execute to fall below a threshold; and defining a partition margin corresponding to the point of failure.

Disclosed herein are techniques and tools for verifying data for semantic correctness and/or verifying data for network correctness. In one respect, a method includes receiving an input defining at least two master nodes and at least one master link, each master node having at least one or more respective data properties populated with master node data and the master link having at least one or more master link data, the master nodes and master link defining a master semantic network, importing source data into a second semantic network, comparing the source data to the master node data and making a first determination that the source data reflects a data relationship defined by the master node data, and based on the first determination, populating the source data into the second semantic network, wherein the source data populated within the second semantic network reflects the data relationship defined by the master node data and the master link data.

In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes resource type information. Further, an API response may be obtained from the endpoint by querying the endpoint using an API call. Furthermore, the API response may be parsed and a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information correspond to a resource type in the resource type information. Further, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.

A system for container migration includes containers running instances of an application running on a cluster, an orchestrator with a controller, a memory, and a processor in communication with the memory. The processor executes to monitor a vitality metric of the application. The vitality metric indicates that the application is in either a live state or a dead state. Additionally, horizontal scaling for the application is disabled and the application is scaled-down until the vitality metric indicates that the application is in the dead state. Responsive to the vitality metric indicating that the application is in the dead state, the application is scaled-up until the vitality metric indicates that the application is in the live state. Also, responsive to the vitality metric indication transitioning from the dead state to the live state, the application is migrated to a different cluster while the horizontal scaling of the application is disabled.

Disclosed are embodiments of a installed software program that receive a model from a product management system. The model is trained to select one of a plurality of predefined states based on operational parameter values of the installation of the software program. Each of the plurality of predefined states define configuration values of the installation of the software program. The defined configuration values indicate, in some embodiments, updates to operational parameter values of the installation of the software program.

Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

A method and/or system for automated detection and automated remediation of anomalies in Robotic Process Automation (RPA) environment is disclosed. The method comprises auto discovering resources (RPA components and its dependencies) in an RPA platform. The discovered resources are monitored though observation metrics whose values are obtained by executing pre-defined scripts. The obtained values are validated against threshold values to determine if there are any anomalies, wherein the threshold values may either be static values or dynamic values. If there is a breach of threshold, a remediation plan is automatically executed causing the remediation of anomalies. The system is trained to determine the dynamic threshold values through machine learning models which are developed and trained through metrics data and by determining error patterns from the historic unstructured log data.

Techniques of automated interoperation tracking in computing systems are disclosed herein. One example technique includes tokenizing a first event log from a first software component and a second event log from the second software component by calculating frequencies of appearance corresponding to strings in the first and second event logs and selecting, as tokens, a first subset of the strings in the first event log and a second subset of the strings in the second event log individually having calculated frequencies of appearance above a preset frequency threshold. The example technique can also include generating an overall event log for a task executed by both the first and second software components by matching one of the strings in the first subset to another of the strings in the second subset.

A system may include a historical time series data store that contains electronic records associated with Software-as-a-Service (“SaaS”) applications in a multi-tenant cloud computing environment (including time series data representing execution of the SaaS applications). A monitoring platform may retrieve time series data for the monitored SaaS application from the historical time series data store and create tenant vector representations associated with the retrieved time series data. The monitoring platform may then provide the retrieved time series data and tenant vector representations together as final input vectors to an autoencoder to produce an output including at least one of a tenant-specific loss reconstruction and tenant-specific thresholds for the monitored SaaS application. The monitoring platform may utilize the output of the autoencoder to automatically detect an anomaly associated with the monitored SaaS application.