Systems, methods and media are directed to automatically generating a recommendation. Data describing a configuration of a production infrastructure is received, the production infrastructure running the system operating in the production environment. One or more metrics data values indicative of a performance of the system operating in the production environment is retrieved. Expected performance values of the system are received. An augmented decisioning engine compares the metrics data values with the expected performance values. The augmented decisioning engine is trained to provide a recommended configuration of the production infrastructure. Based on the comparing, the augmented decisioning engine is trained to improve subsequent recommendations of configuration of the production infrastructure through a feedback process. The augmented decisioning engine is adjusted based on an indication of whether the configuration of production infrastructure satisfies a threshold metric data value in response to the production infrastructure running the system operating in a production environment.

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.

Various embodiments comprise systems and methods to maintain data consistency in a data pipeline. In some examples, a computing system comprises data monitoring circuitry that monitors the operations of the data pipeline. The data pipeline receives input data, processes the input data, and generates output data. The data monitoring circuitry receives and processes the output data sets to identify changes between the output data sets. The data monitoring circuitry generates a consistency score based on the changes that indicates a similarity level between the output data sets. The data monitoring circuitry determines when the consistency score exceeds a threshold value. When the consistency score exceeds the threshold value, the data monitoring circuitry generates and transfers an alert that indicates ones of the output data sets that exceeded the threshold value.

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.

A method for exchange of equipment performance data includes the steps of: obtaining performance data of a communicatively-insulated device; converting the performance data into a scannable code; capturing an image of the scannable code; decoding the scannable code using a communicatively-enabled device to extract an address string encoded in the scannable code, the address string comprising an address of a remote server and the performance data; initiating, by the communicatively-enabled device, a communications link with the remote server using the address string thereby to provide the performance data to the remote server; performing, by the remote server, analytics on the performance data; and sending historic device performance data and/or analytical results to a remote computing device and/or sending a link to the historic device performance data and/or analytical results to the remote computing device; wherein the communicatively-insulated device is packaging equipment and wherein obtaining the performance data comprises: running a calibration phantom through the packaging equipment; scanning the calibration phantom with a calibration unit; and using the calibration unit to generate a system status report identifying one or more operational parameters of the packaging equipment.

Techniques for improving system performance based on data characteristics are disclosed. A system may receive updates to a first data set at a first frequency. The system selects a first storage configuration, from a plurality of storage configurations, for storing the first data set based on the first frequency, and stores the first data set in accordance with the first storage configuration. The system may further receive updates to a second data set at a second frequency. The system selects a second storage configuration, from the plurality of storage configurations, for storing the second data set based on the second frequency, and stores the second data set in accordance with the second storage configuration. The second storage configuration is different than the first storage configuration.

Methods, systems, and computer-readable media for role-based failure response training for distributed systems are disclosed. A failure response training system determines a failure mode associated with an architecture for a distributed system comprising a plurality of components. The training system generates a scenario based at least in part on the failure mode. The scenario comprises an initial state of the distributed system which is associated with one or more metrics indicative of a failure. The training system provides, to a plurality of users, data describing the initial state. The training system solicits user input representing modification of a configuration of the components. The training system determines a modified state of the distributed system based at least in part on the input. The performance of the distributed system in the modified state is indicated by one or more modified metrics differing from the one or more initial metrics.

In general, in one aspect, the invention relates to a method for managing performances of services, the method comprising: generating subsystem groups, wherein each subsystem group of the subsystem groups comprises a plurality of subsystems, wherein each subsystem group is associated with one a plurality of services, wherein the subsystem groups are generated using per-service subsystem requirements; and performing at least one of the plurality of services using a subsystem group of the subsystem groups.

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.