A method and a device for analyzing a consumption of resources in a computing infrastructure to predict a resource consumption anomaly on a computing device. The method includes determining a plurality of resource consumption modeling functions; determining a correlation between the resource consumption modeling functions; measuring a resource consumption by a measurement of a consumption value of a first resource; and predicting the resource consumption of the computing infrastructure. The predicting includes a calculation of a value of future consumption of a resource to be predicted from the consumption value of the first resource and from a previously calculated correlation between modeling functions.

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.

Techniques are disclosed for utilizing configurable delays in an instruction stream. A set of instructions to be executed on a set of engines are generated. The set of engines are distributed between a set of hardware elements. A set of configurable delays are inserted into the set of instructions. Each of the set of configurable delays includes an adjustable delay amount that delays an execution of the set of instructions on the set of engines. The adjustable delay amount is adjustable by a runtime application that facilitates the execution of the set of instructions on the set of engines. The runtime application is configured to determine a runtime condition associated with the execution of the set of instructions on the set of engines and to adjust the set of configurable delays based on the runtime condition.

An information handling system includes a first memory and a baseboard management controller. The first memory stores a first firmware partition and a second firmware partition. The baseboard management controller includes a second memory. The baseboard management controller begins execution of a DM-Verity daemon, and performs periodic patrol reads of the first firmware partition. The baseboard management controller detects one or more block failures in the first firmware partition, and stores information associated with the one or more block failures in a message box of the second memory. In response to the entire first firmware partition being scanned, the baseboard management controller switches a boot partition from the first firmware partition to the second firmware partition, and initiates a reboot of the information handling system.

Embodiments are disclosed for providing workout recommendations based on monitored user inputs with a digital design system. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving a series of inputs performed by a user with an application, categorizing each input in the series of inputs into a user interaction type, where each of the plurality of user interaction types is associated with a counter maintaining a detected user input count, determining that a first counter associated with a first user interaction type has exceeded a threshold amount, identifying a first action associated with the first user interaction type, and providing a notification message including information associated with the first action.

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.

Computing systems, for example, multi-tenant systems deploy software artifacts in data centers created in a cloud platform using a cloud platform infrastructure language that is cloud platform independent. The system receives an artifact version map that identifies versions of software artifacts for data center entities of the data center and a cloud platform independent master pipeline that includes instructions for performing operations related to services on the data center, for example, deploying software artifacts, provisioning computing resources, and so on. The system receives a deployment manifest that provides declarative specification of deployment strategies for deploying software artifacts in data centers. The system implements a deployment operator that executes on a cluster of computing systems of the cloud platform to implement the deployment strategies.

Systems and methods are disclosed for detecting shingled overwrite errors. When a read error is encountered when reading from shingled recording tracks, a processor may determine whether the read error is an error caused by shingled overwriting. The processor may determine whether the read error is caused by shingled overwriting by determining read signal quality of one or more sectors preceding the read error, such as based on a bit error count or bit error ratio (BER), and comparing the read signal quality to a threshold value. The processor may determine that the read error is caused by shingled overwriting when the read signal quality value is lower than the threshold.

Techniques for determining insight are described. An exemplary method includes receiving a request to provide insight into potential abnormal behavior; receiving one or more of anomaly information and event information associated with the potential abnormal behavior; evaluating the received one or more of the anomaly information and event information associated with the abnormal behavior to determine there is insight as to what is causing the potential abnormal behavior and to add to an insight at least two of an indication of a metric involved in the abnormal behavior, a severity for the insight indication, an indication of a relevant event involved in the abnormal behavior, and a recommendation on how to cure the potential abnormal behavior; and providing an insight indication for the generated insight.

The subject technology selects a most recently created file from a set of files stored in a source table. The subject technology iterates, in the source table, starting from the most recently created file up to an age threshold to select a first set of files for performing a first defragmentation process. The subject technology sets an indication corresponding to a particular file that is a last file, from the first set of files, that meets the age threshold. The subject technology performs the first defragmentation process on the selected first set of files. The subject technology determines that the first defragmentation process was successful.