The disclosure herein describes managing multiple clusters within a container environment using a control cluster. The control cluster includes a single deployment model that manages deployment of cluster components to a plurality of clusters at the cluster level. Changes or updates made to one cluster are automatically propagated to other clusters in the same environment, reducing system update time across clusters. The control cluster aggregates and/or stores monitoring data for the plurality of clusters creating a centralized data store for metrics data, log data and other systems data. The monitoring data and/or alerts are displayed on a unified dashboard via a user interface. The unified dashboard creates a single representation of clusters and monitor data in a single location providing system health data and unified alerts notifying a user as to issues detected across multiple clusters.

An information processor includes an operation history obtaining unit configured to obtain operation histories created user operations at a terminal device; a function identifying unit configured to, based on the obtained operation histories, identify a function performed by the user operations as an operation target function; an operation extracting unit configured to, based on information about the operation target function identified by the function identifying unit, extract predetermined operation histories from the obtained operation histories; an index calculating unit configured to calculate an index which indicates a level of efficiency of the operations for the operation histories extracted by the operation extracting unit; an operation selecting unit configured to, based on the index, select the operation histories having a predetermined efficiency; and an output controller configured to output a guide information based on the operation histories selected by the operation selecting unit.

Method and apparatus for stress management in a searchable data service. The searchable data service may provide a searchable index to a backend data store, and an interface to build and query the searchable index, that enables client applications to search for and retrieve locators for stored entities in the backend data store. Embodiments of the searchable data service may implement a distributed stress management mechanism that may provide functionality including, but not limited to, the automated monitoring of critical resources, analysis of resource usage, and decisions on and performance of actions to keep resource usage within comfort zones. In one embodiment, in response to usage of a particular resource being detected as out of the comfort zone on a node, an action may be performed to transfer at least part of the resource usage for the local resource to another node that provides a similar resource.

An electronic device including an application processor and a communication processor. The communication processor including a resource memory, the communication processor configured to monitor an occupancy rate of the resource memory, determine whether the electronic device is in an idle state, forcibly release a network connection, clear the resource memory, and reconnect the network connection.

Technologies for providing shared memory for accelerator sleds includes an accelerator sled to receive, with a memory controller, a memory access request from an accelerator device to access a region of memory. The request is to identify the region of memory with a logical address. Additionally, the accelerator sled is to determine from a map of logical addresses and associated physical address, the physical address associated with the region of memory. In addition, the accelerator sled is to route the memory access request to a memory device associated with the determined physical address.

Apparatuses and methods are provided to generate customizable databases and/or analyze performance. In an example embodiment, a method of generating customizable databases is provided. The method includes receiving a calculation expression relating to one or more defined characteristics. The calculation expression may be defined by a user. The method also includes loading data into a data warehouse. The data includes at least one of the one or more defined characteristics. The method further includes generating a data cube based on the received calculation expression and the data loaded into the data warehouse. The data cube includes an accessible table. A corresponding apparatus is provided. Additional method and apparatus to analyze performance are also provided.

Embodiments of the present disclosure provide a content creation method and a device. The method comprises: displaying a creation guide panel in response to a triggering operation on a content publishing control, wherein the creation guide panel comprises at least two creation guide themes, and creation attributes corresponding to the creation guide themes are different; and in response to a triggering operation on a target creation guide theme, displaying multiple pieces of creation guide content information corresponding to the target creation guide theme, so that a user performs content creation according to the creation guide content information, wherein the target creation guide theme is any one of the at least two creation guide themes. According to the embodiments of the present disclosure, the user can use the creation guide content information for content creation, and the user can determine creatable content and also can provide a content consumer with content needed by the content consumer, thereby improving content creation efficiency and experience.

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.

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 of monitoring and optimizing computing resources usage for computing application may include predicting a first performance metric for job load capacity of a computing application for optimal job concurrency and optimal resource utilization. The system may include generating an alerting threshold based on the first performance metric. The system may further include, in response to a difference between the alerting threshold and a job load of the computing application within an interval exceeding a threshold, predicting a second performance metric for job load capacity of the computing application for optimal job concurrency and optimal resource utilization. The system may further include, in response to a difference between the first performance metric and the second performance metric exceeding a difference threshold, updating the alerting threshold with a job load capacity with the optimal resource utilization rate corresponding to the second performance metric.