Systems and methods described herein support a quota policy language in a cloud infrastructure environment. A quota policy can be configured by setting a statement syntax. In order to set a quota, the quota can establish a number of factors. Each quota can be unique within a service family (e.g. compute), and as such, each quota can define a target service along with a quota name. Next, a quota can define the value to set the quota to, as well as a compartment the quota targets. There can be a set of conditions that can be included determine when the quota is applied.

Systems and methods described herein support a usage calculation process in a cloud infrastructure environment. The usage calculation process can be used to determine whether a requested transaction that targets a compartment within a tree-structure of compartments violates any compartment quota or limit within parent compartments within the tree-structure.

Systems and methods described herein for providing cross-region resource management for regional infrastructure resources in a cloud infrastructure environment. Systems and methods can provide a cross-region resource management tool that can provide searching, viewing, and management capabilities for any resource in any of the regions to which the cross-region resource management tool has access to. A user (having sufficient access levels, such as an administrator or other privileged user) can access the cross-region resource management tool to view and manage all customer resources in any region of the cloud infrastructure, no matter which region the user is located in.

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.

Systems and methods described herein for automatic limit service increase in a multi-tenant cloud infrastructure environment. The systems and methods described herein provide for automatic approval of limit service increase requests that are either automatically generated based upon a tenant's usage of resources within the cloud infrastructure environment, or are received via, e.g., a user portal. Such automatic approval can be based upon a set of maximal limits that are computed based upon the tenant's current usage, level of subscription, or hard limit values.

A network management device according to one embodiment includes a device that stores rules for mapping between a service order and a resource order; a device that stores, when a mapping is performed based on the mapping rules, a mapping history in which an identifier of a service for the service order which is a mapping source, a type of a rule used for the mapping, and an identifier of a resource for the resource order which is a mapping target are described; and a creation unit that, when a service order to change the service is created, acquires, from the mapping history and the identifier of the service to be changed, information indicating types of rules to be used for mapping of the service order to change the service and the identifier of the resource for a resource order which is a mapping target, and creates a resource order by mapping of the order to be changed based on the acquired mapping rules.

A method for processing data packets in a communication network includes establishing a path for a flow of the data packets through the communication network. At a node along the path having a plurality of aggregated ports, a port is selected from among the plurality to serve as part of the path. A label is chosen responsively to the selected port. The label is attached to the data packets in the flow at a point on the path upstream from the node. Upon receiving the data packets at the node, the data packets are switched through the selected port responsively to the label.

Implementations of the present disclosure relate to a method, apparatus and computer program product for processing a computing task. According to one example implementation of the present disclosure, there is provided a method for processing a computing task, comprising: in response to usage of multiple computing resources indicating that at least one part of computing resources among the multiple computing resources are used, determining a direction of a communication ring between the at least one part of computing resources; in response to receiving a request for processing the computing task, determining the number of computing resources associated with the request; and based on the usage and the direction of the communication ring, selecting from the multiple computing resources a sequence of computing resources which satisfy the number to process the computing task. Other example implementations include an apparatus for processing a computing task and a computer program product thereof.

In one embodiment, a device in a network receives in-situ operations administration and management (iOAM) data regarding a plurality of traffic flows in the network. The iOAM data comprises entropy values for the plurality of traffic flows. The device receives network topology information indicative of network paths available in the network. The device generates a machine learning-based entropy topology model for the network based on the received iOAM data and the received network topology information. The entropy topology model maps path selection predictions for the network paths with entropy values. The device uses the entropy topology model to cause a particular traffic flow to use a particular network path.

The present disclosure relate to a cloud resource processing method includes: determining a cloud resource; obtaining a dependent cloud resource of the cloud resource and a resource information group of the cloud resource, where the resource information group includes resource information of the dependent cloud resource; generating a resource topology template; and cloning or migrating the cloud resource based on the resource topology template. According to the cloud resource processing method and the physical node in the embodiments of the present disclosure, clone or migration processing of a plurality of cloud resources can be automatically completed, so as to improve replication efficiency.