Disclosed are systems and computer implemented methods for providing quantum computing as a service. According to one embodiment the system includes a frontend computing system storing a frontend computer program, a backend computing system, and a quantum computer, the frontend computer program being a spreadsheet application configured to receive a service request from a user, the service request comprising service request parameters and input data. The frontend computing system sends the service request to the backend computing system, which is configured to encode it to a service job in a format suitable for the quantum computer to execute, and to submit the service job to the quantum computer. The quantum computer is configured to execute the service job and to provide service job results to the backend computing system, which translates them into results data and sends them to the frontend computing system.

A multi-layer serverless sizing stack may determine a compute sizing correction for a serverless function. The serverless sizing stack may analyze historical data to determine a base compute allocation and compute buffer range. The serverless sizing stack may traverse the compute buffer range in an iterative analysis to determine a compute size for the serverless function to support efficient computational-operation when the serverless function is instantiated.

Disclosed herein are system, method, and computer program product embodiments for allocating resources based on predictions of workload probability parameters. The method can include collecting a first set of historical workload data generated by operating a first set of one or more applications at a first number of past time instances; predicting probability parameters of a second set of future workload data for operating a second set of one or more applications at a second number of future time instances; and determining future resources allocated to operating the second set of one or more applications for the second number of future time instances, based on allocated current resources, a lower bound of resources to satisfy a quality of service (QoS) for operating the second set of one or more applications, an upper bound of resources to satisfy the QoS, and the predicted probability parameters.

A security system is disclosed. The security system includes a memory and a processor. The memory is configured to store several applications, in which several applications include several relationships. The processor is coupled to the memory, in which the processor is configured to manage several applications according to several relationships and at least one of a time driven method and an event driven method, in which the several relationships include a parent-child relationship, a function-group relationship, and an app-type relationship, to receive several input signals from several sources, and to display a screen picture of the several input signals according to several drawing parameters, and when several applications are running, the processor is further configured to allocate several resources of the security system to several applications according to several weighting values.

Described are systems and methods of detecting processes causing degradation of machine performance using heuristics. A device may identify a plurality of time intervals having a use of a resource on a machine above a threshold. The device may identify a percentage of the use of the resource by each of a plurality processes on the machine using the resource during each time interval of the plurality of time intervals. The device may determine a score for each process of the plurality processes based at least on a function of the percentage of the use of the resource over one or more of the plurality of time intervals in which each process used the resource. The device may provide, for display, a selection of one or more processes from the plurality of processes ranked by the score.

Embodiments of the present invention provide a system for minimizing computational processing for converting user resources to resources supported by third party entities. In particular, the system may be configured to determine that a user has scanned a code projected on an entity device via a third party application present on a user device of the user, wherein the entity device is associated with an entity, establish a first connection with the entity device, establish a second connection between the user device and the entity device based on determining that the user has scanned the code, determine that the user has inserted user resources into the entity device, via the first connection, convert the user resources to resources supported by a third party entity, and display in real-time, information associated with the resources on the third party application.

A computer-implemented method for capturing system memory dumps includes receiving, by a diagnostic data component, an instruction to capture a system memory dump associated with a computer process being executed by a computing system comprising one or more processing units, the system memory dump comprising data from a plurality of memory locations associated with the computer process. In response to determining that the system memory dump satisfies a predetermined criterion, the diagnostic data component sends a request for a computing resource boost from the computing system. Further, in response to the request for the computing resource boost being granted, the diagnostic data component uses additional computing resources from the one or more processing units to store the data from the plurality of memory locations in the system memory dump and executing the backlogged operations that were halted due to the system memory dump capture.

A method for executing a task by a scheduling device, belonging to the technical field of electronics. The method includes: acquiring a target algorithm corresponding to a target task to be executed; acquiring an execution environment condition for a target algorithm, and current execution environment information of various execution devices; in the execution devices, determining a target execution device of which the execution environment information satisfies the execution environment condition; and sending a control message for executing the target task to the target execution device.

Architectures or techniques are presented that can prioritize operating a consumption device in a manner that is efficient in terms of consumption of a resource over satisfying a specified demand assigned to the consumption device. This re-prioritizing can be performed in response to a price of the resource exceeding a threshold.

In accordance with an embodiment, described herein is a system and method for use in a distributed computing environment, for automatically scaling a cluster based on metrics being monitored. A cluster that comprises a plurality of nodes or brokers and supports one or more colocated partitions across the nodes, can be associated with an exporter process and alert manager that monitors metrics associated with the cluster. Various metrics can be associated with user-configured alerts that trigger or otherwise indicate the cluster should be scaled. When a particular alert is raised, a callback handler associated with the cluster, for example an operator, can automatically bring up one or more new nodes, that are added to the cluster, and then reassign a selection of existing colocated partitions to the new nodes/brokers, such that computational load can be distributed within the newly-scaled cluster environment.