A method for adjusting the resource allocation ratio between microservices used to run an application. A microservice test sequence is defined which has an order that follows the traffic flow through the microservices. Each microservice is analyzed in order of the test sequence to classify whether or not it is acting as a bottleneck for the application. This is done by measuring whether or not decrementing the microservice's resource causes the application throughput to decrease. For each microservice classified as a bottleneck and in reverse order of the test sequence, its resource is successively incremented until the application throughput starts to increase, indicating it is no longer acting as a bottleneck. The resource allocation ratio can then be adjusted to reflect this procedure.

A method, a device and a computer program product for service allocation are proposed. In the method, a first allocation scheme for allocating a set of services to a set of containers is determined based on respective measurements of a plurality of service attributes related to each of the set of services in execution and respective importance levels of the plurality of service attributes. For a container of the set of containers, respective total measurements of the plurality of service attributes related to at least one of the set of services to be allocated to the container in the first allocation scheme are determined. In accordance with a determination that a total measurement of the respective total measurements exceeds a corresponding measurement threshold, the respective importance levels for use in determining a further allocation scheme for allocating the set of services are updated.

Methods, apparatus, systems and articles of manufacture are disclosed to map workloads. An example apparatus includes a constraint definer to define performance characteristic targets of the neural network, an action determiner to apply a first resource configuration to candidate resources corresponding to the neural network, a reward determiner to calculate a results metric based on (a) resource performance metrics and (b) the performance characteristic targets, and a layer map generator to generate a resource mapping file, the mapping file including respective resource assignments for respective corresponding layers of the neural network, the resource assignments selected based on the results metric.

This disclosure describes methods, devices, systems, and procedures in a computing system for capturing a configuration state of an operating system executing on a central processing unit (CPU), and offloading resource-related tasks, based on the configuration state, to a resource management unit such as a system-on-chip (SoC). The resource management unit identifies a status of each resource based on the captured configuration state of the operating system. The resource management unit then processes tasks associated with the status of the resources, such as modifying a clock rate of a clocked component in the computing system. This can alleviate the CPU from processing those tasks thereby improving overall computing system performance and dynamics.

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.

A system is configured for determining a technology stack in a software application to perform a work project. The system receives and evaluates the work based on its characteristics. A plurality of technology stacks is generated by implementing different combinations of technology stack components. The technology stack components include application servers and webservers. Each of the technology stacks is simulated performing the work project. Based on the simulation results of each technology stack, a performance of each technology stack is evaluated. The system identifies a first technology stack performing at a level higher than a performance threshold and at a highest performance level among the plurality of technology stacks. The system deploys the first technology stack in the software application to perform the work project.

An information processing device includes: a memory; and a processor coupled to the memory and configured to: notify a first virtual machine that is an allocation destination of an expansion card of that the expansion card is removed in a pseudo manner in a case where an operation on the expansion card mounted on an expansion slot of the information processing device is requested; switch the allocation destination of the expansion card from the first virtual machine to a second virtual machine dedicated to setting in a case where the removal of the expansion card is notified in the pseudo manner; switch the allocation destination of the expansion card from the second virtual machine to the first virtual machine in a case where the operation on the expansion card ends; and notify the first virtual machine of that the expansion card after the operation end is inserted.

Properties associated with computing resources are received. At least a portion of the received properties is used to cluster the computing resources into one or more operating groups. At least a portion of the received properties is used to determine a recommendation of an operation schedule for at least one of the one or more operating groups. The recommendation is provided. A feedback is received in response to the recommendation.

Examples described herein relate to a method and a system, for example, a workload controller, for accessing purged workloads. An alert indicative of an attempt to access a purged workload of workloads deployed in a workload environment may be received by the workload controller. The purged workload may include one or both of a deactivated workload or an archived workload. The workload controller may detect the attempt to access the purged workload based on port mirrored data traffic. Further, in some examples, the workload controller may activate the purged workload based on the alert.

A method for database management is disclosed. The method may include receiving an algorithm from a user. Based on the algorithm, a hierarchical dataflow graph (hDFG) may be generated. The method may further include generating an architecture for a chip based on the hDFG. The architecture for a chip may retrieve a data table from a database. The data table may be associated with the architecture for a chip. Finally, the algorithm may be executed against the data table, such that an action included in the algorithm is performed.