Allocating processing activities among multiple computing devices can include identifying multiple computing activities of a computer-executable process and, for each computing activity identified, estimating in real time the computing resources needed. The identifying can be in response to detecting a computer-executable instruction executed by one multiple communicatively coupled computing devices, and the computer-executable instruction can be associate with the computer-executable process. A current condition and configuration of each of the computing devices can be determined in real time. For each computing device an effect induced by executing one or more of the plurality of activities can be predicted, the predicting based each computing device's current condition and configuration and performed by a machine learning model trained using data collected from prior real-time processing of example process activities. Based on the predicting, computing activities can be allocated in real time among the computing devices.

In one embodiment, a method for margin determination for a computing system with a real time operating system and priority preemptive scheduling comprises: scheduling a set of tasks to be executed in one or more partitions, wherein each is assigned a priority, wherein the tasks comprise periodic and/or aperiodic tasks; executing the set of tasks on the computing system within the scheduled periodic time window; introducing an overhead task executed for an execution duration controlled either by the real time operating system or by the overhead task; controlling the overhead task to converge on a point of failure at which a length of the execution duration of the overhead task causes either: 1) a periodic task to fail to execute within a deadline, or 2) time available for the aperiodic tasks to execute to fall below a threshold; and defining a partition margin corresponding to the point of failure.

Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

A computer-implemented method, according to one embodiment, includes: receiving, at a computer, a request to facilitate a testing environment, where the request specifies a number and type of resources to be included in the testing environment. A database which lists available resources in systems and/or devices that are in communication with the computer is inspected and the available resources are compared to the number and type of resources specified in the request to be included in the testing environment. In response to determining that a valid combination of the available resources meets the number and type of resources specified in the request to be included in the testing environment, the database is updated to indicate that each of the resources in the valid combination are in use. Moreover, the request is satisfied by returning information about the resources in the valid combination.

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.

This disclosure describes automatically collecting, analyzing, and remediating operational issues with respect to systems executing within a network. For example, a service provider network may include a monitoring service may generate notifications related to operational issues upon detection of operational issues within a system executing within the service provider network. The monitoring service may provide one or more notifications related to an aggregation service that may aggregate the one or more notifications into a standardized format. Contextual information related to the operational issues may be automatically gathered by an analytics service, which may analyze the contextual information to determine a potential cause of the operational issues. Based on the potential cause, a remediation service may automatically remediate the operational issues.

A method performed by a computing device, of configuring a new design of a new data storage system (DSS) having initial configuration parameters is provided. The new design includes an initial plurality of storage drives. The method includes (a) collecting operational information from a plurality of remote DSSs in operation, the operational information including numbers of writes of various write sizes received by respective remote DSSs of the plurality of remote DSSs over time; (b) modeling a number of drive writes per day (DWPD) of the initial plurality of storage drives of the new DSS based on the collected operational information from the plurality of remote DSSs and the initial configuration parameters; (c) comparing the modeled number of DWPD to a threshold value; and (d) in response to the modeled number of DWPD exceeding the threshold value, reconfiguring the new DSS with an updated design.

The present disclosure provides a method for information storage and a system thereof, which adapts to a data storage system. A monitoring unit is configured to detecting and monitoring operations of a storage node in the data storage system to generate corresponding one and more monitoring data. A recording processor is configured to receiving the one or the plurality of monitoring data, and rendering one or a plurality of logs according to the difference of content of the one or the plurality of monitoring data. The adjustment mechanism is performed according to the stored logs, thereby the amount of large data generated during monitoring is effectively reduced.

An output device 10 is provided with an output unit 11 for outputting, on the basis of job feature information indicating the features of the job of a distributed processing system, estimation model application information that is information in a format suitable for an estimation model that estimates the amount of computer resources required for processing a task constituting the job. The estimation model application information may include word-containing information having binary information that indicates whether or not a character string indicated by the character string information included in the job feature information includes a prescribed word. The estimation model application information may include numerical inversion label information having, as string label information, a value derived by converting, by a prescribed function, the numeric value indicated by the numerical information included in the job feature information.

Methods and apparatus to select virtualization environments are disclosed. An example apparatus includes a logic circuit, a workload analyzer to determine characteristics of a virtualized application, a score generator to compare the characteristics of the virtualized application to a plurality of virtualization environment types to determine scores for each of the plurality of virtualization environment types, the scores based on rules that identify different scores for combinations of characteristics and virtualization environment types, and a workload deployer to deploy the virtualized application using one of the plurality of virtualization environment types based on the scores.