Methods and systems for managing workload performance in distributed systems is disclosed. The distributed system may include any number of data processing systems that may perform workloads. To manage workload performance, the distributed system may include a distributed control plane. The distributed control plane may include any number of data processing systems that both receive and service workload requests. When a workload request is received by one of the data processing systems of the control plane, a consensus based processing for a selecting one of the data processing systems to perform the workload may be performed. Consequently, the data processing system that received the workload request may or may not perform the workload to service the workload request depending on the outcome of the consensus based process.

Methods and systems for managing the performance of workloads in a distributed system are disclosed. The distributed system may include any number of clients, deployments, and data sources operably to one another. To service the workloads, container instances may be deployed to various deployments. When deciding where to deploy the container instances, the hardware resources of the deployments and/or resource expectations associated with the container instances may be taken into account. By doing so, container instances may be more likely to be deployed to deployments that meet their resource expectations. The resource expectations may be embedded as metadata in resources specific build files.

Application link scaling method, apparatus and system are provided. The method includes obtaining an application link, the application link being a path formed by at least two associated applications for a service scenario; determining information of target resources required by capacity scaling for all applications in the application link; allocating respective resources to the applications according to the information of the target resources; and generating instances for the applications to according the respective resources. From the perspective of services, the method performs capacity assessment for related applications on a link as a whole, and capacity scaling of the entire link, thus fully utilizing resources, and preventing the applications from being called by other applications which results in insufficient resources. This ensures the applications not to become the vulnerability of a system, ensures the stability of the system, avoids allocating excessive resources to the applications, and reduces a waste of resources.

A system and method for scaling resources of a secondary network for disaster recovery uses a disaster recovery notification from a primary resource manager of a primary network to a secondary resource manager of the secondary network to generate a scale-up recommendation for additional resources to the secondary network. The additional resources are based on latest resource demands of workloads on the primary network included in the disaster recovery notification. A scale-up operation for the additional resources is then executed based on the scale-up recommendation from the secondary resource manager to operate the secondary network with the additional resources to run the workloads on the secondary network.

Monitoring a set of virtual users during performance testing and reporting virtual user operations data to ensure an ongoing and constant transactions per second load and providing test result data with evidence of constant transactions per second (TPS) during the test. Generating and executing performance tests under constant TPS includes restarting virtual users that are terminated during the performance tests.

Methods, systems, and computer program products are provided for creating a resource management testing environment. An initial population of databases is established in a database ring, having an in initial count of databases and different types of databases that are determined based on an initial database population model. The initial population model receives ring classification information for the database ring from a ring grouping model. A sequence of database population-change events is generated based on a model, to change the population of the databases over time in the ring. An orchestration framework performs testing of resource manager operations based on the model-defined initial population of databases and the model-defined populations of databases changed over time. Model-defined resource usage metrics for each database are utilized to test the resource manager operations. Resource usage metrics and database add/drop events of a production system are used to train the models.

An apparatus for computing functions using polynomial-based approximation, comprising one or more processing circuitries configured for computing a polynomial-based approximant approximating a function by executing one or more iterations. Each iteration comprising computing the polynomial-based approximant using scaled fixed-point unit(s) according to a constructed set of coefficients, minimizing an approximation error of the computed polynomial-based approximant compared to the function while complying with one or more constraints selected from a group comprising at least: an accuracy, a compute graph size, a computation complexity, and a hardware utilization of the processing circuitry(s), adjusting one or more of the coefficients in case the approximation error is incompliant with the constraint(s) and initiating another iteration. The polynomial-based approximant and its adjusted set of coefficients for which the computed polynomial-based approximant complies with the constraint(s) may be output to one or more processing circuitries configured to approximate the function by computing the polynomial-based approximant.

One example method includes evaluating code of a quantum circuit, estimating one or more runtime statistics concerning the code, generating a recommendation based on the one or more runtime statistics, and the recommendation identifies one or more resources recommended to be used to execute the quantum circuit, checking availability of the resources for executing the quantum circuit, allocating resources, when available, sufficient to execute the quantum circuit, and using the allocated resources to execute the quantum circuit.

Inflight transactions having predictable pod failure in distributed computing environments are managed by integrating a transaction manager into pods having containers running applications in a distributed computing environment, wherein the transaction manager records a transaction log having data indicative of historical pod failure. A pod health check that is also integrated into the pods determines predictive pod failure scenarios from the data of historical pod failure in the transaction log. Pod health can be tracked using the pod health checker by matching the predictive pod failure scenarios to transaction calls. Calls may be sent to a load balancer for recovery of pod failure for transaction calling match the predictive pod failure scenarios. Pods can be configured recover for the predictive pod failure.

Technologies for managing memory on a compute device are disclosed. The compute device is configured to determine the quality of a user experience of the compute device when a certain combination of applications are running on the compute device and stores an indication of the quality of the user experience that corresponds to that combination of applications. At a later time, such as when a user selects an application to be launched, the compute device may check if the current combination of applications is expected to have an acceptable quality of a user experience. If not, the compute device may kill one or more of the current combination of applications to improve the expected quality of the user experience.