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.

Systems and methods for connection virtualization in data storage device arrays are described. A host connection identifier may be determined for a storage connection request. A target storage device and corresponding completion connection identifier may be determined for a storage command including the host connection identifier. A command tracker may be stored that associates the storage command with the host connection identifier and the completion connection identifier and the storage command may be sent to the processing queue associated with the completion connection identifier.

Methods, systems, and computer-readable media for resource usage restrictions in a time-series database are disclosed. Elements of a plurality of time series are stored into one or more storage tiers of a time-series database. The time series are associated with a plurality of clients of the time-series database. Execution of tasks is initiated using one or more resources of one or more hosts. The time-series elements represent inputs to the tasks. The tasks comprise a first task and a second task. A usage of the one or more resources by the first task is determined to violate one or more resource usage restrictions. Based at least in part on the usage, one or more actions are performed to modify the execution of the first task. The one or more actions increase an amount of the one or more resources available to the second task.

Systems and methods are provided for offloading a task from a central processor in a radio access network (RAN) server to one or more heterogeneous accelerators. For example, a task associated with one or more operational partitions (or a service application) associated with processing data traffic in the RAN is dynamically allocated for offloading from the central processor based on workload status information. One or more accelerators are dynamically allocated for executing the task, where the accelerators may be heterogeneous and may not comprise pre-programming for executing the task. The disclosed technology further enables generating specific application programs for execution on the respective heterogeneous accelerators based on a single set of program instructions. The methods automatically generate the specific application programs by identifying common functional blocks for processing data traffic and mapping the functional blocks to the single set of program instructions to generate code native to the respective accelerators.

Various aspects are disclosed for unified resource management of containers and virtual machines. A podVM resource configuration for a pod virtual machine (podVM) is determined using container configurations. The podVM comprising a virtual machine (VM) that provides resource isolation for a pod based on the podVM resource configuration. A host selection for the podVM is received from a VM scheduler. The host selection identifies hardware resources for the podVM. A container scheduler is limited to bind the podVM to a node corresponding to the hardware resources of the host selection from the VM scheduler. The podVM is created in a host corresponding to the host selection. Containers are started within the podVM. The containers correspond to the container configurations.

A resource-usage notification framework can be implemented for distributed computing environments. For example, a system can determine the resource usage of a software application in a distributed computing environment. The system can determine if the resource usage is within a predefined range of a predefined resource-consumption limit. If so, the system can generate an event notification and transmit the event notification to the software application. The software application can receive the event notification and perform a mitigation operation in response. The mitigation operation can be configured to prevent the resource usage from exceeding the predefined resource-consumption limit or to mitigate an impact of the resource usage exceeding the predefined resource-consumption limit.

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 disclosure relates to a method and apparatus including setting a memory swap size limit, the limit being lower than a memory swap size defining a maximum size of a part of said memory resources used for swap, obtaining a score for at least one running program, a high score corresponding to a low priority level, obtaining monitoring information representative of a monitored activity of the program during a time period and of a learnt user's habit of use of the program, including a number of times the program gained the focus within the time period. The disclosure also includes deriving a score delta from information with a decrement value to the score delta at each focus gained by the program, adjusting the score by adding the delta, and terminating execution when memory swap size limit is reached and the adjusted score reaches a memory swap size limit threshold.

An overall access rating for each user in a plurality of users for accessing a computing resource of a set of computing resources is generated. Reduced performance of the computing resource is identified. Access metrics associated with each user in the plurality of users who are accessing the computing resource during the reduced performance of the computing resource are determined. The generated overall access ratings based on the determined access metrics are modified. Access to the computing resource is granted based on a ranking of the modified overall access ratings.

Resource allocation limitations include resource limits and resource guarantees. A consumer is vulnerable to interruption by other consumers if using more resources than guaranteed. Resources are designated and/or assigned to consumers based on resource limits and resource guarantees. A constraint programming (CP) solver determines resource limits and resource guarantees that minimize vulnerability and/or vulnerability cost based on resource usage data. A CP data model includes limit elements, guarantee elements, and vulnerability elements. The CP data model further includes guarantee-vulnerability constraints, which relies on exceedance distributions generated from resource usage data for the consumers. The CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.