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.

Examples described herein relate to dynamically adjust a manner of identifying hot pages in a remote memory pool based on adjustment of parameters of a data structure. In some examples, the parameters of the data structure include a range of number of access counts and a number of pages associated with the range.

Performance testing a data storage system includes recording operating parameters and performance data as the data storage system executes performance tests over a test period, the performance data including one or more measures of a performance characteristic (e.g., latency) across a range of I/O operation rates or I/O data rates for each of the performance tests. Subsets of recorded operating parameters and performance data are selected and applied to a machine learning model to train and use the model, and the model provides a model output indicative for each performance test of a level of validity of the corresponding performance data. Based on the model output indicating at least a predetermined level of validity for a given performance test, the performance data for the performance test are incorporated into a record of validated performance data for the data storage system, usable for benchmarking, regression analysis, hardware qualification, etc.

A system may include a historical time series data store that contains electronic records associated with Software-as-a-Service (“SaaS”) applications in a multi-tenant cloud computing environment (including time series data representing execution of the SaaS applications). A monitoring platform may retrieve time series data for the monitored SaaS application from the historical time series data store and create tenant vector representations associated with the retrieved time series data. The monitoring platform may then provide the retrieved time series data and tenant vector representations together as final input vectors to an autoencoder to produce an output including at least one of a tenant-specific loss reconstruction and tenant-specific thresholds for the monitored SaaS application. The monitoring platform may utilize the output of the autoencoder to automatically detect an anomaly associated with the monitored SaaS application.

A method, system and computer program product, the method comprising: obtaining computer code of an employed system comprising a plurality of components; obtaining data related to operating the plurality of components; based on the computer code and the data, identifying: a first component from the plurality of components, to be maintained; and a second component from the plurality of components, to be at least partly replaced by a machine learning component; and providing to a user an identification of the first component and the second component.

Disclosed herein are systems and method for gradually updating software object instances on a plurality of computer nodes. In an exemplary aspect, in response to receiving a notification from a software object instance, a system may register the software object instance at an update server. The system may store and deploy a plurality of links, wherein each deployed link uniquely corresponds to a registered software object instance. The system may then associate two or more subsets of the plurality of links with two or more update locations, in accordance with an update policy. The system may place an update to the software object instance at the two or more update locations in accordance with an update policy. In response to receiving an update request via a link from a computing node, the system may further redirect the update request to an update location associated with the link.

Requests for computing resources and other resources can be predicted and managed. For example, a system can determine a baseline prediction indicating a number of requests for an object over a future time-period. The system can then execute a first model to generate a first set of values based on seasonality in the baseline prediction, a second model to generate a second set of values based on short-term trends in the baseline prediction, and a third model to generate a third set of values based on the baseline prediction. The system can select a most accurate model from among the three models and generate an output prediction by applying the set of values output by the most accurate model to the baseline prediction. Based on the output prediction, the system can cause an adjustment to be made to a provisioning process for the object.

Operations include halting a runaway query in response to determining that a performance metric of the query exceeds a performance threshold. The runaway query halting system receives a query execution plan associated with a query and divides the received execution plan into one or more components. For each component, the system determines a predicted resource usage associated with executing the component. The system further determines a predicted resource usage associated with the query execution plan based on the predicted resource usage associated with each component. The system executes the query associated with the received query execution plan and compares the predicted resource usage associated with the query to a resource usage threshold. In response to determining that the predicted resource usage of the query execution plan exceeds the resource usage threshold, the system halts execution of the query associated with the query execution plan.

Methods, systems, and computer-readable storage media for receiving, by a current database system, a query plan file representative of a captured query plan from a source database system, receiving, by the current database system, a set of definitions including one or more definitions, each definition in the set of definitions corresponding to an object that is implicated by the query plan, the object being included in a set of objects, and determining, by the current database system, that each definition in the set of definitions is identical to a respective definition of a corresponding object within the current database system, and in response: executing the captured query plan in the current database system to provide a query result.

Embodiments provide enhanced performance diagnosis in a network computing environment. In response to an occurrence of a performance issue for a node while under operating conditions, common logs for applications on the node are analyzed. The applications are respectively registered in advance for diagnosis services. The applications each register rules in advance for the diagnosis services. At a time of the performance issue, debug programs are automatically issued to generate debug level logs respectively for the applications. Debug level logs are analyzed according to the rules to determine a root cause of the performance issue. A potential solution to the root cause of the performance issue is determined using the rules, without having to recreate the operating conditions occurring during the performance issue. The potential solution to rectify the root cause of the performance issue is executed without having to recreate the operating conditions occurring during the performance issue.