According to one aspect of the present disclosure, a method and technique for hybrid virtual machine configuration management is disclosed. The method includes assigning to a first set of virtual resources associated with a virtual machine a first priority and assigning to a second set of virtual resources associated with the virtual machine a second priority lower than the first priority. An operating system of the virtual machine is provided with the first and second priorities assigned to the respective first and second sets of virtual resources. The operating system dispatches to process a workload the virtual resources from the first set before dispatching the virtual resources from the second set.

Some embodiments may be associated with a cloud-based computing environment. A multi-tenant master process platform, associated with a RDBMS, may create a logical database for a tenant on a physical instance of the cloud-based computing environment. A connection to the logical database may be received from a client user associated with the tenant, and a process for the connection may be created. A process identification number created for the process may then be captured along with the database identifier for the tenant using an in-kernel virtual machine program. The system may send the process identification number and the database identifier to a user space program. The user space program creates a control group with the name of the database identifier and places the process identification number into the control group. The control group can then be limited with respect to a maximum amount of resources (memory, CPU etc.).

Techniques are disclosed for dynamically adjusting a throttling threshold in a multi-tenant virtualized computing environment. System health parameters are collected during a predetermined time interval. A system health status of the multi-tenant virtualized computing environment is determined. Based on the system health status, a throttling threshold for service requests for the multi-tenant virtualized computing environment is determined. The throttling threshold is applied for further service requests. During a subsequent time interval, an updated system health status of the multi-tenant virtualized computing environment is determined based on system health parameters received during the subsequent time interval. The throttling threshold is updated based on the updated system health status. The updated throttling threshold is applied for further service requests.

A method and an apparatus for processing a resource request are provided. The method includes: classifying, by a computing device, access virtual objects into a plurality of density grades according to a density of interaction virtual objects in a current interactive range of each access virtual object; allocating a resource request quota to each density grade. The method also includes: when a resource request sent by an access virtual object in a first density grade is received within the first preset duration, processing the resource request if the resource request quota corresponding to the density grade is greater than the preset quota threshold, and subtracting a preset value from the resource request quota corresponding to the density grade; and rejecting the resource request if the resource request quota corresponding to the density grade is not greater than the preset quota threshold.

Embodiments for allocating shared resources are disclosed. In an embodiment, an apparatus includes a core and a hardware rate selector. The hardware rate selector is to, in response to a first indication that demand for memory bandwidth from the core has reached a threshold value, determine a delay value to be used to limit allocation of memory bandwidth to the core. The hardware rate selector includes a controller having a first counter to count a second indication of demand for memory bandwidth from the first core and a second counter to count expirations of time windows. The first indication is based on a difference between the first counter value and the second counter value.

According to some embodiments, methods and systems may include a data storage device that contains user identifiers and associated entitlement values for a plurality of tenants of a cloud computing environment. A provisioning application platform processor may receive a user request for an integration service and access the data storage device. The provisioning application platform processor may then transmit at least one entitlement value to a platform resource manager processor to facilitate creation of a plurality of microservices resulting in implementation of the integration service for the user.

The technology includes methods, processes, and systems for virtualizing graphics processing unit (GPU) memory. Example embodiments of the technology include managing an amount of GPU memory used by one or more processes, such as Application Programming Interfaces (APIs), that directly or indirectly impact one or more other processes running on the same GPU. Managing and/or virtualizing the amount of GPU memory may ensure that an end user does not receive a GPU out-of-memory error because the API request is impacted by the processing of other API requests. A virtual machine with access to a GPU may be organized with one or more job slots that are configured to specify the number of processes that are able to run concurrently on a specific virtual machine. A process may be configured on each virtual machine running a software program or API and is used to schedule work based on GPU memory requirements.

A cloud compute resource provider implements a method for automatically adjusting a quota of compute resources allocated to an individual customer subscription. The method includes determining a current usage metric for the individual customer subscription for a recent time interval; determining whether a subscription-based historical usage model has been trained on historical usage data of the individual customer subscription; and responsive to determining that the subscription-based historical usage model has been trained, executing the subscription-based historical usage model to generate a future resource usage metric predicting a usage of the customer subscription over a future time interval; and outputting a recommended adjusted resource quota for the individual subscription, the predicted future resource usage metric satisfying a target utilization of the recommended adjusted resource quota.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model to perform a machine learning task. In one aspect, a method performed by one or more computer is described. The method includes: obtaining data defining a compute budget that characterizes an amount of computing resources allocated for training a machine learning model to perform a machine learning task; processing the data defining the compute budget using an allocation mapping, in accordance with a set of allocation mapping parameters, to generate an allocation tuple defining: (i) a target model size for the machine learning model, and (ii) a target amount of training data for training the machine learning model; instantiating the machine learning model, where the machine learning model has the target model size; and obtaining the target amount of training data for training the machine learning model.

Techniques are disclosed for dynamically adjusting a throttling threshold in a multi-tenant virtualized computing environment. System health parameters are collected during a predetermined time interval. A system health status of the multi-tenant virtualized computing environment is determined. Based on the system health status, a throttling threshold for service requests for the multi-tenant virtualized computing environment is determined. The throttling threshold is applied for further service requests. During a subsequent time interval, an updated system health status of the multi-tenant virtualized computing environment is determined based on system health parameters received during the subsequent time interval. The throttling threshold is updated based on the updated system health status. The updated throttling threshold is applied for further service requests.