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.

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.

Various embodiments include a method for determining a collection frequency of data. The data are collected from a device for an application program to monitor the device. The method may include: determining a collection frequency requirement of the application program regarding the data of the device; determining state information of the device; and determining, based on the determined collection frequency requirement of the application program regarding the data of the device and the determined state information of the device, a collection frequency of data according to a preset rule.

Each of one or more commit cycles may be associated with a predicted number of updates. A statistics collection time for a database table can be determined by estimating a sum of predicted updates included in one or more commit cycles. Whether the estimated sum of predicted updates is greater than a first threshold may be determined. In addition, a progress point for a first one of the commit cycles can be determined. A time to collect statistics may be selected based on the progress point of the first commit cycle.

Methods, apparatus, and processor-readable storage media for server classification using machine learning techniques are provided herein. An example computer-implemented method includes obtaining, from at least one data source, data pertaining to server activity attributed to one or more servers; processing at least a portion of the obtained data using one or more rule-based analyses; selecting at least a particular machine learning classification algorithm from a set of multiple machine learning classification algorithms, based at least in part on results from the processing and one or more portions of the obtained data; classifying an activity level of at least a portion of the one or more servers by processing at least a portion of the obtained data using the selected machine learning classification algorithm; and performing at least one automated action based at least in part on results of the classifying.

A system measures, by executing a monitoring process, first metric data associated with trade data at a first time point after the trade data is output by a first process of an application and before the trade data is input to a second process of the application, identifies the trade data at a second time point after the trade data is output by the second process and before the trade data is output by the application, measures second metric data associated with the trade data identified at the second time point, and sends, in response to a latency value obtained based on the first metric data or the second metric data exceeding a latency threshold, a latency alert to a user computing device associated with the application. The monitoring process is not a process of the application and is not linked with the first process or the second process.

An apparatus comprises a processing device configured to collect system state information from host devices, to split the collected system state information into logical chunks, and to determine, based at least in part on a plurality of factors, a compression level to be applied to each of the logical chunks. The plurality of factors comprise a first factor characterizing a time at which the collected system state information is needed at a destination device and at least a second factor characterizing resources available for at least one of performing compression of the collected system state information and transmitting the collected system state information over at least one network to the destination device. The processing device is further configured to apply the determined compression level to each of the logical chunks to generate compressed logical chunks, and to transmit the compressed logical chunks to the destination device.

A first device executing an application determines data indicative of conditions associated with the first device during use of the application. Based on correspondence between this data and threshold data that indicates conditions in which frames representing a display output of the first device should be stored, the first device is caused to send data indicative of these frames to a second device. The second device generates user interface data based in part on the received frames and may send the user interface data to other devices. To reduce the amount of data sent and the computational resources used, the first device may store only changed frames of display output, and may send data to the second device at times when a communication interface of the first device is active for other purposes.

A performance manager (400, 500) and a method (200) performed thereby are provided, for managing the performance of a logical server of a data center. The data center comprises at least one memory pool in which a memory block has been allocated to the logical server. The method (200) comprises determining (230) performance characteristics associated with a first portion of the memory block, comprised in a first memory unit of the at least one memory pool; and identifying (240) a second portion of the memory block, comprised in a second memory unit of the at least one memory pool, to which data of the first portion of the memory block may be migrated to apply performance characteristics associated with the second portion. The method (200) further comprises initiating migration (250) of the data to the second portion of the memory block.

In some examples, a computing device can include a processor resource and a non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause the processor resource to: generate a model of activity for the computing device, determine a time period for performing a calculation based on the model, wherein the calculation utilizes performance metrics associated with the computing device, activate an agent at a start time of the time period to perform the calculation, send, by the agent, a result of the calculation to a remote computing device, and deactivate the agent in response to sending the result.