Systems and methods for universal auto-scaling are disclosed. In one embodiment, a method may include: (1) monitoring, by an auto-scale computer program executed by a computer processor, a utilization level at each of a plurality of data layers in a data pod, wherein each data layer comprises at least one node; (2) comparing, by the auto-scale computer program, each of the utilization levels to a threshold; (3) identifying, by the auto-scale computer program, that one of the thresholds is met or exceeded; and (4) deploying, by the auto-scale computer program, an additional node to the data layer with the met or exceeded utilization level.

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.

In an approach for generating differentiated workload telemetry data, a processor corresponds one or more services with a workload related telemetry generating an event emitter. A processor performs a correlation analysis of corresponding relationship and connection among connected resources and current traffic into and out of the one or more services. A processor labels domain context for each telemetry event. A processor communicates each telemetry event to a global event handler. A processor performs a cross-correlation in real-time of telemetry data with the global event handler. A processor updates a real-time differentiated workload report.

A computer system determines stack usage. An intercept function is executed to store a stack marker in a stack, wherein the intercept function is invoked when a program enters or exits each function of a plurality of functions of the program. A plurality of stack markers are identified in the stack and a memory address is determined for each stack marker during execution of the program to obtain a plurality of memory addresses. The plurality of memory addresses are analyzed to identify a particular memory address associated with a greatest stack depth. A stack usage of the program is determined based on the greatest stack depth. Embodiments of the present invention further include a method and program product for determining stack usage in substantially the same manner described above.

A method and system elects a master node from a plurality of nodes in a distributed system. A serverless elector function periodically outputs an election API call to a load balancer. The load balancer elects a master node from a plurality of candidate nodes each time the load balancer receives the election API call.

Managing input sensitivity of an information handling system, including performing a calibration and configuration of an input sensitivity management model, including: identifying contextual data associated with the information handling system, the contextual data including i) system characteristics of the information handling system and ii) o user characteristics of a user of the information handling system; identifying historic states of the information handling system, the historic states including for each state in time of the information handling system, inputs to the information handling system and outputs of the information handling system based on the inputs to the information handling system; training, based on the contextual data and the historic states, the input sensitivity management model for determining an input sensitivity of the inputs to the information handling system; performing, after performing the calibration and configuration of the input sensitivity management model, a steady-state monitoring of the information handling system.

One embodiment comprises a stateless container of binaries and a broker. The stateless container of binaries includes a code memory having stored thereon code for a first version of a first functional component of a content management system, the first functional component executable to provide a first version of a service. The broker may be executable to: receive a request for the service from a client application, the request associated with a user of the content management system; determine that the first version of the service is accessible with regard to the user; determine an available first server that hosts the first version of the service; provide an indication of the first version of the service to the client application; and provide an IP address and a port number associated with the available first server to the client application.

Performance of devices can be evaluated and enhanced in software-defined systems. For example, a computing device can receive, at a server of a software-defined system, a first plurality of properties describing a client system in the software-defined system. The computing device can compare, by the server, the first plurality of properties to additional properties describing at least one additional client system in the software-defined system. The computing device can determine, by the server, an adjustment for the client system based on the comparison and a similarity of the client system to each of the at least one additional client system. The computing device can output, by the server, an indication of the adjustment to the client system.

A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process includes acquiring information in a log regarding an operating state of a plurality of robots; based on the acquired information in the log, calculating a first load in each time slot related to control of the plurality of robots; when there is a first time slot in which the first load is higher than or equal to a first threshold, extracting a robot that performs a first task, from the plurality of robots, in the first time slot; and changing a time slot for operating the extracted robot.

A method and system for backup processes that includes identifying a target volume and identifying a number of available threads to back up the target volume. The elements in the target volume are distributed among the available threads based on a currently pending size of data in the threads. The elements are stored from each thread into a backup container, and merged from each of the backup containers into a backup volume.