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.

An interface receives reported information from a system on chip (SOC), where the reported information includes: (1) hardware-reported information that is reported by a hardware functional module included in the SOC and (2) firmware-reported information that is reported by a firmware functional module included in the SOC. A processor receives one or more display settings and generates visual information based at least in part on: (1) the one or more display settings, (2) the hardware-reported information, and (3) the firmware-reported information. The visual information is displayed via a display.

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.

A method by a network device for dynamically detecting emotional states of a user operating a client end station to interact with an application. The method includes receiving information regarding user inputs received by the client end station from the user while the user interacted with the application during a particular time period and determining an emotional state of the user based on analyzing the information and information regarding user inputs received by the client end station from the user while the user interacted with the application during one or more previous time periods that together with the particular time period form a time window.

The management computer stores a configuration information of a storage, a configuration information of a host computer and a VM, an information on a service level of the VM, and a performance information of a storage subsystem and a network. If an access path that the host computer uses to access a volume is changed in response to a change of storage configuration, an I/O performance of the VM operating in the host computer may be changed. If the change of state of the storage is detected, the management computer calculates a change of state of whether a service level defined for the VM is satisfied, and selects an appropriate host computer in which the VM should be operated.

The present disclosure is directed to a technique to reduce latency in feedback-based system performance determination. A system receives, from an application developer device, indications of an in-application event and a first input value for an application content delivery profile. The system receives, via an interface from an application developed by an application developer and executed by a computing device remote from the data processing system and different from the application developer device, a ping indicative of an occurrence of the in-application event on the computing device. The system merges data from the ping with internal data determined by the data processing system to generate merged data. The system determines a predicted performance for the in-application event and provides an indication of the predicted performance. The system configures, responsive to the indication of the predicted performance, the application content delivery profile with a second input value.

A plurality of completion times associated with an application request may be obtained. The plurality of completion times may include a first completion time and a second completion time. A plurality of response times associated with a first asynchronous operation triggered by the application request may be obtained. The plurality of completion times may include a first response time associated with the first completion time and a second response time associated with the second completion time. A first correlation score may be determined describing an effect of the first asynchronous operation on the application request based on the first completion time, the second completion time, the first response time, and the second response time. Visualization data may be generated representing the first correlation score.

An instrumentation analysis system processes data streams by executing instructions specified using a data stream language program. The data stream language allows users to specify a search condition using a find block for identifying the set of data streams processed by the data stream language program. The set of identified data streams may change dynamically. The data stream language allows users to group data streams into sets of data streams based on distinct values of one or more metadata attributes associated with the input data streams. The data stream language allows users to specify a threshold block for determining whether data values of input data streams are outside boundaries specified using low/high thresholds. The elements of the set of data streams input to the threshold block can dynamically change. The low/high threshold values can be specified as data streams and can dynamically change.

A method of detecting abnormality may include the following steps. A normal-value range of a parameter for a target object is determined based on historical values of the parameter in a preset time period or at a preset time point. Whether the target object is abnormal is determined based on the normal-value range and the value of the parameter for the target object in the preset time period or at the preset time point within a current time cycle. Further, another normal-value range may be determined based on historical deviation values for the target object in historical time periods or at historical time points before the preset time period or the preset time point. Whether the target object is abnormal is determined based on either of the two normal-value ranges.

A storage unit stores statistical information including an amount of resource consumption and performance information, which represents a performance, of each piece of hardware of a plurality of types that are candidates for an arrangement destination of a function, an accepting unit accepts inputs of description details of a function in a high-level language corresponding to the hardware of the plurality of types, and a performance requirement that represents a required performance, a performance predicting unit calculates a predicted performance, and a predicted amount of resource consumption, using the description details and a predetermined algorithm for each piece of hardware; and a device selecting unit selects, as an arrangement destination, hardware with the calculated predicted performance and the performance information satisfying the performance requirement and a total value of the predicted amount of resource consumption and the amount of resource consumption being equal to or smaller than a resource capacity.