A data processing method includes determining whether or not it is possible to perform a new processing task within the new processing task's time limit if execution of one or more current processing tasks is continued based on the one or more current processing tasks' configurations. In case the new task's performing is determined not to be possible within the new task's time limit, resources are released from the current tasks so as to still enable performing the current tasks within their respective time limits.

Systems and methods described herein support compartment quotas in a cloud infrastructure environment. Cloud administrators do not generally have the ability to restrict resource usage in existing clouds. Granting a user permission to create resources allows them to create any number of resources up to a predefined account limit. Compartment quotas allow admins to restrict a user's resource usage to the appropriate level allowing fine-tuned cost control.

A system including a distributed network of one or more virtual machines, having a first portion of committed virtual machines dedicated to a user and a second portion of on-demand virtual machines. The system may also include a workload scheduler configured to receive workloads associated with the user. The scheduler may determine whether to schedule a given workload to be performed by a combination of virtual machines in the first and second portions, or by virtual machines included in only the first portion. The given workload may be scheduled to be performed by virtual machines in only the first portion at a first time if a sum of an anticipated resource consumption level of the given workload and a first consumption level of the first portion of virtual machines at the first time is less than or equals a total amount of resources included in the first portion.

As part of a container initialization procedure, a maximum number of hardware threads per processor core in a set of cores of a computer system are enabled, the container initialization procedure configuring an operating system executing on the computer system for container execution and configuring a first container for execution on the operating system. From a set of available cores in the set of cores, an execution core is selected. In the selected execution core, a number of threads per core to be used during execution of the first container is configured, the number of threads per core specified for the container initialization procedure by a first simultaneous multithreading (SMT) parameter. Using the configured execution core, the first container is executed, the executing virtualizing the operating system.

Computational resources are used for storing, processing, and transmitting logs, metrics, and other tracking data. Usage of such resources may be reduced by reducing the retention of identified tracking data subsets, based on the subsets' respective utilization levels, after monitoring activities that review a subset or at least request a subset for review. Efficient usage of tracking data subsets may be increased, as measured by signal-to-noise fractions, wherein signal strength corresponds to subset utilization in monitored review activities. Tracking data resource usage efficiency may be gained whether the amount of resources used is increased, maintained, or reduced. Resource usage control functionality may be integrated with security orchestration and automation, security information and event management, workload management, laws, entity policies, operational requirements, usage goals, or application dependencies, even in legacy systems.

Systems and methods for allocating resources are disclosed. Resources as processing time, writes or reads are allocated. Credits are issued to the clients in a manner that ensure the system is operating in a safe allocation state. The credits can be used not only to allocate resources but also to throttle clients where necessary. Credits can be granted fully, partially, and in a number greater than requested. Zero or negative credits can also be issued to throttle clients. Segment credits are associated with identifying unique fingerprints or segments and may be allocated by determining how many credits a CPU/cores can support. This maximum number may be divided amongst clients connected with the server.

Various approaches are described to manage the execution of operations. Such operations may be performed without human intervention and may help maintain functionality of a cloud platform or client instances. In one aspect of the present approach, the number and/or type of automations starting in a given time frame may be limited to maintain an even or consistent distribution of resource usage. In a further aspect, the number and/or type of concurrent automations may be limited to a defined threshold to maintain an even or consistent distribution of resource usage.

Systems and methods are described for allocating resources on an on-demand code execution system under conditions of scarcity, when demand for resources exceeds threshold limits. Under such conditions, a single high-demand resource consumer—such as a function or an account on the system—might monopolize available resources, denying access to the system to other resource consumers. Embodiments of the present disclosure prevent that monopolization by implementing constrained equal awards allocation, whereby resource consumers with relatively low-demand are allocated their requested resources, and remaining resources are divided substantially equally among remaining consumers of relatively high demand. The allocation techniques described herein may be implemented even under varying demand levels, without requiring each consumer to positively state their desired portion prior to allocation.

There is provided a method of computing resource allocation. The method comprises allocating a first bounded amount of computing resources forming a first set of computing resources; exclusively assigning the first set of computing resources to a first process of a computer program; receiving a request from the first process for additional computing resources; in response to the request from the first process, allocating a second bounded amount of computing resources forming a second set of computing resources; and spawning a second process from the first process and exclusively assigning the second set of computing resources to the second process; wherein this method may be repeated indefinitely by the first process, second process, or any other process created according to this method. By following this method, a process does not control the amount of computing resources allocated to that process (i.e., itself), but instead controls the amount of computing resources allocated to its child processes.

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.