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.

An approach is provided for controlling computer resource usage. A new event in an integration flow in an integration platform is detected. Sender and receiver information is identified and hashed. A portion of data being sent by the sender to the receiver is selected and hashed. It is determined that the hashed sender and receiver information matches a first entry and the hashed selected portion of the data matches a second entry in a pattern repository. A recurring event in the integration flow is identified, where the recurring event uses an amount of computer resources that exceeds a threshold amount. An action is performed which reduces the amount of computer resources used by the integration flow to a new amount that does not exceed the threshold amount.

Disaster recovery resource provisioning is provided. Infrastructure resource objects are grouped into a plurality of resource pools based on resource characteristics of each respective infrastructure resource object. A set of resource capabilities is provided for seamless resource provisioning for each resource pool in the plurality of resource pools. A class of service is mapped to a resource pool corresponding to a workload spread across multiple environments considering primary workload production and secondary disaster recovery requirements. Resources are automatically provisioned from the class of service required in providing disaster recovery for the workload based on characteristics of the workload, cost, business needs, and service level agreement metrics corresponding to the disaster recovery.

Systems and techniques are provided for monitoring and managing the performance of services accessed by sites on a computing platform. When a performance issue is identified, a service is monitored to determine if calls to the service exceed a threshold completion time. If so, a resource available to call the service is adaptively throttled by the platform.

Computer agents can be throttled individually. In an example, when a computer agent completes a work item, the computer agent reports this to a central component that maintains a vote value for that agent and that increases the respective vote value based on the completed work item. When the central component determines that system performance is sufficiently diminished, central component can throttle the performance of those computer agents having respective vote values above a predetermined threshold value.

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.

[Problem] When resource reserved in a resource sharing system become unavailable, the reservation is efficiently transferred.

[Solution] In a resource sharing system 10, a plurality of users 20 (user terminals) share a plurality of resources 30. A resource reservation management device 42 includes: a reservation setting unit 402 that accepts a reservation request including a usage condition of the plurality of resources 30 from the user 20 and sets a usage reservation according to the usage condition to a first resource predetermined 30 in the resource sharing system 10; and a reservation changing unit 404 that re-sets the usage reservation to a second resource 30 being different from the first resource 30 in the resource sharing system 10 when a reserved resource 30 becomes unavailable. When a resource capacity of the second resource 30 is insufficient for the usage reservation to be re-set, the reservation changing unit 404 changes the usage condition and re-sets the usage reservation to the second resource 30.

A method and apparatus of a network device that allocates a shared memory buffer for an object is described. In an exemplary embodiment, the network device receives an allocation request for the shared memory buffer for the object. In addition, the network device allocates the shared memory buffer from shared memory of a network device, where the shared memory buffer is accessible by a writer and a plurality of readers. The network device further returns a writer pointer to the writer, where the writer pointer references a base address of the shared memory buffer. Furthermore, the network device stores the object in the shared memory buffer, wherein the writer accesses the shared memory using the writer pointer. The network device further shares the writer pointer with at least a first reader of the plurality of readers. The network device additionally translates the base address of the shared memory buffer to a reader pointer, where the reader pointer is expressed in a memory space of the first reader.

The present disclosure involves systems, software, and computer implemented methods for resource allocation and management. One example method includes receiving, in a dispatching layer, a request to run a first task for a first application, the request including a first application priority. At least one second application priority of at least one currently running application is identified. A maximum number of allowable parallel tasks per application is determined. Application priority weights are assigned to each of the first application priority and the at least one second application priority. A number of parallel tasks for the first application and the at least one currently running application are determined based on the maximum number of allowable parallel tasks per application and the assigned application priority weights. A first number of parallel tasks are assigned to the first application. The first application is executed using the assigned first number of parallel tasks.

The present application discloses a method and an apparatus for processing a development machine operation task, a device and a storage medium, which relates to the field of deep learning of artificial intelligence. The specific implementation solution is: receiving a task creating request initiated by a client; generating, according to the task creating request, the development machine operation task; allocating a target graphics processing unit (GPU) required for executing the development machine operation task for the development machine operation task; and sending a development machine operation task request to a master node in cluster nodes, where the task request is used to request executing the development machine operation task on the target GPU.