A system may include a historical managed software system data store that contains electronic records associated with controllers and deployed workloads (each electronic record may include time series data representing performance metrics). An entropy calculation system, coupled to the historical managed software system data store, may calculate at least one historical entropy value based on information in the historical managed software system data store. A detection engine, coupled to a monitored system currently executing a deployed workload in the cloud computing environment, may collect time series data representing current performance metrics associated with the monitored system. The detection engine may then calculate a current monitored entropy value (based on the collected time series data representing current performance metrics) and (iii) compare the current monitored entropy value with a threshold value (based on the historical entropy value). Based on the comparison, a corrective action for the monitored system may be triggered.

Methods and systems for monitoring performance in a distributed storage system described. One example method includes identifying requests sent by clients to the distributed storage system, each request including request parameter values for request parameters; generating probe requests based on the identified requests, the probe requests including probe request parameter values for probe request parameter values, representing a statistical sample of the request parameters included in the identified requests; sending the generated probe requests to the distributed storage system over a network, wherein the distributed storage system is configured to perform preparations for servicing each probe request in response to receiving the probe request; receiving responses to the probe requests from the distributed storage system, and outputting at least one performance metric value measuring a current performance state of the distributed storage system based on the received responses.

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.

Implementations include providing, by a computer-executed migration advisor executing within a run-time of a source database system, a query data set including queries processed by the source database system during production use of the source database system, providing, by the migration advisor, an object data set including data representative of database objects stored within a database of the source database system, generating, by the migration advisor, a list of query-level features and a list of object-level features, each feature in the list of query-level features and each feature in the list of object-level features including a feature that is deprecated in a target database system, resolving one or more issues represented by features of one or more of the list of query-level features and the list of object-level features, and executing migration of the database of the source database system to the database of the target database system.

A computer-implemented method includes scanning a clip of messages that includes message requests and message responses arranged in a sequence. The scanning is performed based on one or more search parameters and produces a list of one or more name/value pairs. The clip is utilized to perform a load test on a target website. Each name/value pair has a corresponding value. For each name/value pair in the list a message request in the clip is identified where the corresponding value is first used. Then, looking backwards in the sequence from the message request where the corresponding value is first used, prior message responses are located where the corresponding value is found. An extraction point is specified in the clip for the corresponding value as a latest message response in the sequence where the corresponding value was returned from the target website. The corresponding value is then stored as a property.

A disclosed load estimation method includes: collecting run information of a processor being executing a predetermined program; specifying execution status of the processor based on the collected run information; and estimating a load of the predetermined program based on a result of comparison between the execution status of the processor and execution characteristics of the processor. Each of the execution characteristics is stored in association with a load level of the predetermined program.

Provided is a method for testing a microservice application with at least one microservice with at least one application programming interface, including: reading characteristic data of the application programming interface of the microservice of the microservice application and ascertaining at least one endpoint of the application programming interface; automatically generating an execution script on the basis of the characteristic data of the application programming interface; automatically generating a test infrastructure, wherein the test infrastructure includes at least one client entity; executing the execution script and transmitting the data query of the execution script by the client entity to the application programming interface of the microservice and receiving corresponding response data of the microservice by the client entity; and ascertaining the transfer characteristic by the client entity.

A method, system and computer readable medium for running a legacy application on a non-legacy device. Operating parameters of the non-legacy device when running the legacy application are set based on one or more pre-determined heuristics for adjustment of operating parameters of the newer system when running the legacy application on the non-legacy device from one or more performance metrics and other performance information.

Methods, systems, and computer-readable storage media for receiving a capture file, the capture file holding data representative of a workload executed in a source database system, processing the capture file to provide a replay file, the replay file being in a format that is executable by a replayer to replay the workload in a target database system, the workload including a set of requests represented within the replay file, providing a set of tags associated with the replay file, the set of tags including one or more tags, each tag associated with a request in the set of requests, and during replay of the workload in the target database system: pausing replay of the workload in response to a tag, executing a request associated with the tag, providing replay results specific to the request, and selectively resuming replay of the workload in the target database system.

Techniques described herein relate to a method for deploying workflows. The method may include receiving, by a global orchestrator of a device ecosystem, a request to execute a workflow; decomposing, by the global orchestrator, the workflow into a plurality of workflow portions; executing, by the global orchestrator, a metaheuristic algorithm to generate a result comprising a plurality of domains of the device ecosystem in which to execute the plurality of workflow portions; and providing, by the global orchestrator, the plurality of workflow portions to respective local orchestrators of the plurality of domains based on the result of executing the metaheuristic algorithm.