A system and method for scaling resources of a secondary network for disaster recovery uses a disaster recovery notification from a primary resource manager of a primary network to a secondary resource manager of the secondary network to generate a scale-up recommendation for additional resources to the secondary network. The additional resources are based on latest resource demands of workloads on the primary network included in the disaster recovery notification. A scale-up operation for the additional resources is then executed based on the scale-up recommendation from the secondary resource manager to operate the secondary network with the additional resources to run the workloads on the secondary network.

An apparatus includes a processor and a memory that stores a deep Q reinforcement learning (DQN) algorithm configured to generate an action, based on a state. Each action includes a recommendation associated with a computational resource. Each state identifies at least a role within an enterprise. The processor receives information associated with a first user, including an identification of a first role assigned to the user and computational resource information associated with the user. The processor applies the DQN algorithm to a first state, which includes an identification of the first role, to generate a first action, which includes a recommendation associated with a first computational resource. In response to applying the DQN algorithm, the processor generates a reward value based on the alignment between the first recommendation and the computational resource information associated with the first user. The processor uses the reward value to update the DQN algorithm.

Technologies for deploying virtual machines (VMs) in a virtual network function (VNF) infrastructure include a compute device configured to collect a plurality of performance metrics based on a set of key performance indicators, determine a key performance indicator value for each of the set of key performance indicators based on the collected plurality of performance metrics, and determine a service quality index for a virtual machine (VM) instance of a plurality of VM instances managed by the compute as a function each key performance indicator value. Additionally, the compute device is configured to determine whether the determined service quality index is acceptable and perform, in response to a determination that the determined service quality index is not acceptable, an optimization action to ensure the VM instance is deployed on an acceptable host of the compute device. Other embodiments are described herein.

A method, system and computer program product for forecasting a storage requirement of a database management system (DBMS). The storage-related operations (e.g., create, delete, update) of the applications connected to the DBMS are monitored. The impact on the storage usage of the DBMS based on these storage-related operations performed by the applications is monitored. Furthermore, the applications are categorized into groups of applications based on the monitored storage-related operations. A mathematical model is then built to forecast the storage requirement of the DBMS based on the monitored impact on the storage usage of the DBMS by the monitored storage-related operations of the applications and the categorization of the applications. The storage requirement of the DBMS is then forecasted based on the built mathematical model. In this manner, the storage requirements of the DBMS may be accurately predicted to ensure that there is available storage thereby preventing performance degradation.

Techniques for capacity management in computing systems are disclosed herein. In one embodiment, a method includes analyzing data representing a number of enabled users or a number of provisioned users to determine whether the analyzed data represents an anomaly based on historical data. The method can also include upon determining that the data represents an anomaly, determining a conversion rate between a change in the number of enabled users or the number of provisioned users and a change in a number of active users of the computing service and deriving a future value of the number of active users of the computing service based on both the detected anomaly and the determined conversion rate. The method can further include allocating and provisioning an amount of the computing resource in the distributed computing system in accordance with the determined future value of the active users of the computing resource.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support configuration of cloud-based functionality. A configuration device is provided and includes a data processing module, a modelling module, and a loading module. The data processing module provides functionality for compiling information for use in configuring the cloud-based functionality in a requirements compliant manner. The modelling module may include various machine learning modules configured to evaluate configuration workbooks for compliance with requirements specified by a user. The modelling module may output recommendations for improving compliance of the configuration workbooks and appropriate changes may be made. The loading module may be configured to obtain templates applicable to the cloud-based functionality being configured and to extract appropriate data from the (updated) configuration workbooks. The extracted data may then be loaded into the obtained templates for use in configuring the cloud-based functionality in a regulatory compliant manner.

An apparatus comprises a processing device configured to receive system state information corresponding to one or more devices, to predict a usage frequency of the system state information using one or more machine learning models, and to determine, based at least in part on the usage frequency, a compression level for storage of the system state information. The compression level is applied to the system state information to generate at least one compressed file for transmission to a database.

The present disclosure is directed to methods and apparatus for evaluating resources that would be used by machine learning model(s) for purposes of implementing the machine learning model(s) on resource-constrained devices. For example, in one aspect, a plurality of layers in a machine learning model may be identified. A plurality of respective output sizes corresponding to the plurality of layers may be calculated. Based on the plurality of output sizes, a maximum amount of volatile memory used for application of the machine learning model may be estimated and compared to a volatile memory constraint of a resource-constrained computing device. Output indicative of a result of the comparing may be provided at one or more output components.

Disclosed is a system for tracking user interactions with an application to recommend creation of a microapp. The system determines a recommendation score for creating a microapp corresponding to a functionality of an application based on at least one of the amount of time users spend interacting with the application, the number of interface elements of the application that the user changes, and the input values provided by the users. The system uses interactions corresponding to multiple different users to determine the recommendation score. The system may also recommend an interface element to be included in the microapp. The recommendation score is provided to an administrator, who may use the information to create a microapp.

One example method includes evaluating code of a quantum circuit, estimating one or more runtime statistics concerning the code, generating a recommendation based on the one or more runtime statistics, and the recommendation identifies one or more resources recommended to be used to execute the quantum circuit, checking availability of the resources for executing the quantum circuit, allocating resources, when available, sufficient to execute the quantum circuit, and using the allocated resources to execute the quantum circuit.