A request manager analyzes API calls from a client to a host application for state and performance information. If current utilization of host application processing or memory footprint resources exceed predetermined levels, then the incoming API call is not forwarded to the application. If current utilization of the host application processing and memory resources do not exceed the predetermined levels, then the request manager quantifies the processing or memory resources required to report the requested information and determines whether projected utilization of the host application processing or memory resources inclusive of the resources required to report the requested information exceed predetermined levels. If the predetermined levels are not exceeded, then the request manager forwards the API call to the application for processing.

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.

The present invention provides a deployment platform that enables solution modules to be created and deployed without writing new code. The solution modules may include existing solutions, solution components, connectors, and the like selected from a solution library. The deployment platform includes a development engine providing functionality for generating deployment information for the solution module. The deployment information may include a blueprint or other information for deploying the solution module to target infrastructure. The deployment platform also includes a deployment engine providing functionality for deploying the solution module to the target infrastructure automatically. During deployment, the deployment engine pushes components of the solution module to the target infrastructure in accordance with the deployment information. During and after deployment, information may be captured and recorded to a distributed ledger to provide end-to-end visibility into the deployed solution over the deployment lifecycle (e.g., including initial deployment, updates/upgrades, and decommissioning).

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.

Techniques related to communication between independent containers are provided. In an embodiment, a first programmatic container includes one or more first namespaces in which an application program is executing. A second programmatic container includes one or more second namespaces in which a monitoring agent is executing. The one or more first namespaces are independent of the one or more second namespaces. A monitoring agent process hosts the monitoring agent. The monitoring agent is programmed to receive an identifier of the application program. The monitoring agent is further programmed to switch the monitoring agent process from the one or more second namespaces to the one or more first namespaces. After the switch, the monitoring agent process continues to execute in the second programmatic container, but communication is enabled between the application program and the monitoring agent via the monitoring agent process.

Systems, methods, and computer-readable media are disclosed for an improved database. The systems, methods, and computer-readable media described herein may enhance the response time of databases and improve user experiences. In an example method described herein, a database monitoring system may receive instructions to perform one or more data monitoring operations comprising counting an occurrence of a first value within at least a portion of items stored in a database. The method may include determining a length of a first window of time and fetching, from a first location of a data store of the database, data indicative of a total count of the occurrence of the first value at a time associated with the beginning of the first window of time. In turn, the monitoring system may store data representing the first count in the first memory.

A monitoring and alerting system backed by a machine learning engine for anomaly detection and prediction of time series data indicative of health of an application, a system, an environment, or a person. Using any data of interest that is modeled into a time series known as times and values; comparing input data against learned previous patterns; predicting data; identifying anomalies; generating notifications or an alert identifying the deviation, and communicating the alert to users, applications, or devices, applying the action or health functions logic using the significance of the issue to modify/start/stop components of the system or application. The data is received via a metrics server and is cleaned into a unified format and passed through via streaming or push/pull mechanisms. Planned deviations are configured to prevent false positives. A variety of machine learning methods is used and the system has dual function components and disaster recovery.

Techniques for determining insight are described. An exemplary method includes receiving a request to provide insight into potential abnormal behavior; receiving one or more of anomaly information and event information associated with the potential abnormal behavior; evaluating the received one or more of the anomaly information and event information associated with the abnormal behavior to determine there is insight as to what is causing the potential abnormal behavior and to add to an insight at least two of an indication of a metric involved in the abnormal behavior, a severity for the insight indication, an indication of a relevant event involved in the abnormal behavior, and a recommendation on how to cure the potential abnormal behavior; and providing an insight indication for the generated insight.

Disclosed are various embodiments for anti-pattern detection in extraction and deployment of a microservice. A software modernization service is executed to analyze a computing application to identify various applications. When one or more of the application components are specified to be extracted as an independently deployable subunit, anti-patterns associated with deployment of the independently deployable subunit are determined prior to extraction. Anti-patterns may include increases in execution time, bandwidth, network latency, central processing unit (CPU) usage, and memory usage among other anti-patterns. The independently deployable subunit is selectively deployed separate from the computing application based on the identified anti-patterns.

The subject technology provides a portion of the functionality of an application on an electronic device on which the application is not installed. The portion of the functionality of the application is provided by a clip of the application that can be obtained, installed, and launched on the user device, at the time the functionality is desired by a user, and without authenticating information for the user. The clip of the application can provide the user with access to a purchase function, an ordering function, or any other sub-function of the application. When the application itself is installed on the device, the clip of the application can be deleted while preserving access, by the application, to data generated on the device by the clip.