An information processing system capable of controlling bursting more appropriately is provided. The information processing system includes: a credit management unit that manages a credit amount for controlling allocation of a resource amount of resources via the bursting with respect to each of a plurality of operation targets; and a bursting management unit that calculates the resource amount to be added via the bursting with respect to each of the plurality of operation targets according to the credit amount managed by the credit management unit and issues an instruction to a storage apparatus to allocate each calculated resource amount to each of the plurality of operation targets.

A device may receive information associated with a network resource request. The network resource request may be a request, by a user device, for access to a network resource associated with a network. The device may determine a subscriber class associated with the user device based on receiving the information associated with the network resource request. The device may determine a service type associated with the network resource request based on receiving the information associated with the network resource request. The device may determine an allocation and retention priority (ARP) level based on the determined subscriber class and the determined service type. The device may assign the ARP level to the network resource.

Methods, apparatus, systems and articles of manufacture to provide a custom installable open virtualization application file for on-premise installation via the cloud are disclosed. Example instructions cause one or more processors to determine a sizing factor based on a response from a user device; determine customized resource information for an agent in a private cloud network using an inventory count algorithm based on the sizing factor; modify an open virtualization appliance (OVA) file to include the customized resource information, the OVA file being deployed in a public cloud network; and transmit an indication to a location of the modified OVA file to the user device, the location of the modified OVA file being the same location as the OVA file.

Systems and methods of dynamic resource allocation. The system may include a processor and a memory coupled to the processor. The memory stores processor-executable instructions that, when executed, configure the processor to: receive a signal representing a resource allocation request; determine a projected resource availability based on a resource model and a second data set including at least one data record unrepresented in batched historical data sets, the batched historical data sets including data records representing at least one of recurring or non-recurring resource allocations, and wherein the resource model is prior-trained based on the batched historical data sets; and generate an output signal for displaying the projected resource availability corresponding with the resource allocation request.

Effectively allocating computing resources to end-users is provided. A system can identify mapping groups comprising a first mapping group that maps first users to corresponding first machines having a first hardware configuration, and a second mapping group that maps second users to corresponding second machines having a second hardware configuration. The system can determine a first metric indicating computing resource utilization of a first machine of the first machines when executing one or more sessions of a first user in the first mapping group, and a second metric indicating computing capacity of the second machines. The system can re-map the first user from the first mapping group to the second mapping group to cause sessions of the first user to execute on a second machine of the second machines.

Examples herein include systems and methods for providing capacity forecasting for high-usage periods of a computing infrastructure. An example method can include segmenting a first portion of a data stream and generating a first core set for a forecasting model that predicts future usage of computing resources. The example method can further include segmenting a second portion of the data stream, generating a second core set, and using both core sets to forecast usage. The first core set can then be phased out after a predetermined time period has elapsed such that forecasting is based only on the second core set. The example method can further include defining at least two clusters of data and performing predictive analysis on that specific cluster. Cluster-specific results can be displayed on a GUI, which can also provide a user with options for increase or decrease computing resources based on the predictions.

A method of assigning network resource between users of a network is disclosed. The method comprises the steps of monitoring a measure of user experience of users accessing a service within the network (step 120), determining a current level of user satisfaction with the service accessed for users within the network (step 130), and distributing network resource between users to minimise the number of users exhibiting a level of user satisfaction that is below a first threshold level (step 140). Also disclosed is a computer program product for carrying out a method of assigning network resource between users of a network and a system (200) configured to assign network resource between users of a network.

The present invention discloses a cloud computing infrastructure having a rights management device, which is designed to manage operating rights for at least one application which can be executed in the cloud computing infrastructure for the purpose of controlling the operation of the at least one application in the cloud computing infrastructure, to evaluate the managed operating rights with respect to execution of the at least one application and to output an execution enable on the basis of the evaluation of the operating rights, and having an execution device which is designed to execute the at least one application in the cloud computing infrastructure on the basis of the output execution enable. The present invention also discloses a method for operating at least one application in a cloud computing infrastructure and an application for execution in a cloud computing infrastructure.

Methods, systems, and devices for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, the managed systems may need to operate in a predetermined manner conducive to, for example, execution of applications that provide the computer implemented services. Similarly, the managed system may need access to certain hardware resources (e.g., and also software resources such as drivers, firmware, etc.) to provide the desired computer implemented services. To improve the likelihood of the computer implemented services being provided, the managed systems may be managed using a subscription based model. The subscription model may utilize a highly accessible service to obtain information regarding desired capabilities (e.g., a subscription) of a managed system, and use the acquired information to automatically configure and manage the features and capabilities of the managed systems by powering and depowering select components as well as deploying dynamically composed management entities.

A system and method remotely allocate bandwidth among content consumers on a computing network based on optimizing an aggregate objective pertaining to a plurality of flows of content. The system and method create a profile for each flow of the plurality of flows from a content provider to a content consumer on the computing network. Information is stored in each profile based on at least a metric associated with the corresponding flow. A target bandwidth for each profile is computed remotely, based on optimizing an aggregate objective pertaining to the plurality of flows of content. The optimizing is also based on the information stored in their respective profiles. The system and method distribute the bandwidth to each flow of the plurality of flows based on the target bandwidth remotely computed for each profile.