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; and 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.

A sample ratio mismatch (SRM) analyzer receives data from an online controlled experiment (OCE) and provides information to help determine a root cause of an SRM. The SRM analyzer may identify one or more segments in the data that include an SRM and may determine whether a triggered scorecard of the OCE includes an SRM. The data may include one or more scorecards. The SRM analyzer may determine whether each scorecard has an SRM. The SRM analyzer may test a difference in proportion of users assigned to treatment between a last scorecard without an SRM and a first scorecard with an SRM. If the difference in proportion is statistically meaningful, the SRM analyzer may determine that the SRM arose after the last scorecard. If the difference in proportions is not statistically meaningful, the SRM analyzer may determine that the SRM existed from a beginning of the OCE.

Systems, computer program products, and methods are described herein for implementing federated containerization platform using LiFi (Light Fidelity). The present invention is configured to electronically receive, from a container orchestration engine, a request to allocate one or more resources to one or more applications to execute a first task; retrieve one or more resource requirements associated with the one or more applications; determine, using a machine learning model, one or more resources to be allocated to the one or more applications; retrieve, from a resource repository, the one or more resources to be allocated to the one or more applications; and allocate the one or more retrieved resources to the one or more applications.

Systems and methods directed to generating a predicted quality metric are provided. Telemetry data may be received from a from a first group of devices executing first software. A quality metric for the first software may be generated based on the first telemetry data. Telemetry data from a second group of devices may be received, where the second group of devices is different from the first group of devices. Covariates impacting the quality metric based on features included in the first telemetry data and the second telemetry data may be identified, and a coarsened exact matching process may be performed utilizing the identified covariates to generate a predicted quality metric for the first software based on the second group of devices.

A system, method and program product for handling potentially problematic events in an enterprise computing platform. A method is disclosed that includes receiving a request to process an event from a client, wherein the event specifies a feature to be performed on an enterprise resource within the enterprise platform. The method further includes retrieving a processing threshold for the feature from a set of stored configuration settings and obtaining metadata associated with the enterprise resource, wherein the metadata indicates an attribute of the enterprise resource. The method then determines whether the attribute of the enterprise resource exceeds the processing threshold, and if so, does not process the event.

Techniques described herein relate to systems and methods of data storage, and more particularly to providing layering of file system functionality on an object interface. In certain embodiments, file system functionality may be layered on cloud object interfaces to provide cloud-based storage while allowing for functionality expected from a legacy applications. For instance, POSIX interfaces and semantics may be layered on cloud-based storage, while providing access to data in a manner consistent with file-based access with data organization in name hierarchies. Various embodiments also may provide for memory mapping of data so that memory map changes are reflected in persistent storage while ensuring consistency between memory map changes and writes. For example, by transforming a ZFS file system disk-based storage into ZFS cloud-based storage, the ZFS file system gains the elastic nature of cloud storage.

Methods and systems for evaluating metrics (e.g., quality of service metrics) corresponding to a monitored computer, detecting metric anomalies, and issuing alerts, are disclosed. A metrics collecting agent, operating on a monitored computer, collects metrics corresponding to the monitored computer and/or one or more monitored services. These metrics are transmitted to a monitoring server that dynamically determines metric thresholds corresponding to normal metrics and anomalous metrics. Using these metric thresholds, along with a machine learning model, the monitoring server can determine whether one or more metrics are anomalous, automatically issue alerts to security and operations teams, and/or transmit a control instruction to the monitored computer in order to fix the issue causing the anomalous metrics.

A method and system for communicating with IoT devices to gather information related to device failure or error(s) is disclosed. The system makes a copy of at least a portion of the device's non-volatile memory and/or receives IoT device data (e.g., sensor data and/or log files etc.) from an IoT device that recently failed. The system determines which log files and/or sensor data, for example, the IoT device created before and/or after a failure. After gathering this information, the system stores the information in a database, sends it to the IoT device manufacturer, for further analysis and diagnostics to troubleshoot the failure and send a fix or software update to the IoT device.

In one aspect, a first device may download at least one disk image and then provide the disk image to second and third devices through a fourth device that controls connections to the second and third devices. The first device may then run computer diagnostics concurrently on the second and third devices through the fourth device and using the image provided to each of the second and third devices. In some examples, communication between the devices may occur using USB ports.

In one aspect, a device may include at least one processor and storage accessible to the at least one processor. The storage may include instructions executable by the at least one processor to allocate, in memory, a read-once memory container to store data from a first computer program. The instructions may also be executable to write the data from the first computer program to the read-once memory container and to permit a second computer program to use the data as stored in the read-once memory container. The data, upon being accessed from the read-once memory container, may not be readable again from the read-once memory container without being written again.