Abstract: The present application is directed to stranded resource recovery in a cloud computing environment. A resource utilization signal at each of a plurality of nodes that each hosts corresponding virtual machines (VMs) is measured. Based on each resource utilization signal, a set of candidate nodes is identified. Each candidate node comprises a stranded resource that is unutilized due to utilization of a bottleneck resource. The identification includes calculating an amount of the stranded resource at each candidate node. From a plurality of VMs hosted at the set of candidate nodes, a set of candidate VMs is identified for migration for stranded resource recovery. The identification includes calculating a score for each candidate VM based on a degree of imbalance between the stranded resource and the bottleneck resource at a candidate node hosting the candidate VM. Migration of at least one candidate VM in the set of candidate VMs is initiated.

Abstract: A search space for allocating a virtual machine is pruned. An allocation request for allocating a virtual machine to a plurality of clusters is received. A valid set of clusters is generated. The valid set of clusters includes clusters of the plurality of clusters that satisfy the allocation request. An attribute associated with the allocation request is identified. A truncation parameter is determined, by a trained search space classification model, based on the identified attribute. The valid set of clusters is filtered based on the truncation parameter. A server is selected from the filtered valid set of clusters. The virtual machine is allocated to the selected server. In an aspect of the disclosure, a search space pruner generates an analysis summary based on an analysis of received telemetry data. The search space pruner trains the search space classification model to determine truncation parameters based on the analysis summary.

Abstract: Techniques for performing NAT operations to send packets between networks are described. In an example, a network device receives a packet that comprises a header. The header indicates a source address of a first computing resource in a first network and a destination address of a second computing resource in a second network. The network device determines a pool of identifiers allocated for the first network and the second computing resource and identifies a packet flow based on the header. The network device also determines that no identifier from the pool of identifiers has been allocated for the packet flow and determines an identifier available to allocate for the packet flow from the pool of identifiers. The network device performs a NAT operation on the packet based on the identifier.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Abstract: The present disclosure provides methods, systems, and media for optimized customer relationship management. A method for identifying subjects for a clinical procedure may comprise (a) retrieving, from a computer database, a first set of subject records, wherein the first set of subject records corresponds to a first set of subjects that are candidates for the clinical procedure; (b) processing the first set of subject records using a trained machine learning algorithm to generate a second set of subject records; and (c) electronically outputting the second set of subject records.

Abstract: The description relates to deep learning cluster scheduler modular toolkits. One example can include generating a deep learning cluster scheduler modular toolkit that includes multiple DL scheduler abstraction modules and interactions between the multiple DL scheduler abstraction modules and allows user composition of the multiple DL scheduler abstraction modules to realize a deep learning scheduler.

Abstract: Techniques discussed herein are directed to identifying health assessment data of a set of computing instances of a distributed computing system. The health assessment data may be collected from the computing instances and stored in a first distributed cache. When a request for health assessment data for one or more computing instances is received, the health assessment data may be retrieved from the first distributed cache, provided to the requesting entity, and stored in a second distributed cache. A subsequent request may cause new health assessment data to be retrieved from the first distributed cache and compared to the stored data of the second distributed cache. Changes in the health assessment data may be identified and data indicating those changes may be provided in response to the subsequent request. One or more remedial actions may be performed in response to the health assessment data obtained.

Abstract: A method using weighted aggregated ensemble model for energy demand management of buildings includes initializing data values for integrated model to measure energy consumption, perform statistical analysis on data values to estimate accurate prediction, optimizing the data values using marine predator optimization for integrated model, analyze the output to minimize the mean square error and results show improvement in accuracy of integrated model. The data values comprise of ?, maximum number of splits, minimum leaf size, and ?. The weighted aggregated ensemble model for energy demand management of buildings shows best performance compared with other predictive models such as linear regression (LR), support vector regression (SVR), multilayer perceptron neural network (MLPNN), decision tree (DT), and generalized additive model (GAM).

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Abstract: To improve the reliability of nodes that are utilized by a cloud computing provider, information about the entire lifecycle of nodes can be collected and used to predict when nodes are likely to experience failures based at least in part on early lifecycle errors. In one aspect, a plurality of failure issues experienced by a plurality of production nodes in a cloud computing system during a pre-production phase can be identified. A subset of the plurality of failure issues can be selected based at least in part on correlation with service outages for the plurality of production nodes during a production phase. A comparison can be performed between the subset of the plurality of failure issues and a set of failure issues experienced by a pre-production node during the pre-production phase. A risk score for the pre-production node can be calculated based at least in part on the comparison.

Abstract: To improve the reliability of nodes that are utilized by a cloud computing provider, information about the entire lifecycle of nodes can be collected and used to predict when nodes are likely to experience failures based at least in part on early lifecycle errors. In one aspect, a plurality of failure issues experienced by a plurality of production nodes in a cloud computing system during a pre-production phase can be identified. A subset of the plurality of failure issues can be selected based at least in part on correlation with service outages for the plurality of production nodes during a production phase. A comparison can be performed between the subset of the plurality of failure issues and a set of failure issues experienced by a pre-production node during the pre-production phase. A risk score for the pre-production node can be calculated based at least in part on the comparison.

Abstract: A method for minimizing allocation failures in a cloud computing system without overprovisioning may include determining a predicted supply for a virtual machine series in a system unit of the cloud computing system during an upcoming time period. The predicted supply may be based on a shared available current capacity and a shared available future added capacity for the virtual machine series in the system unit. The method may also include predicting an available capacity for the virtual machine series in the system unit during the upcoming time period. The predicted available capacity may be based at least in part on a predicted demand for the virtual machine series in the system unit during the upcoming time period and the predicted supply. The method may also include taking at least one mitigation action in response to determining that the predicted demand exceeds the predicted supply during the upcoming time period.

Abstract: Techniques discussed herein are directed to identifying health assessment data of a set of computing instances of a distributed computing system. The health assessment data may be collected from the computing instances and stored in a first distributed cache. When a request for health assessment data for one or more computing instances is received, the health assessment data may be retrieved from the first distributed cache, provided to the requesting entity, and stored in a second distributed cache. A subsequent request may cause new health assessment data to be retrieved from the first distributed cache and compared to the stored data of the second distributed cache. Changes in the health assessment data may be identified and data indicating those changes may be provided in response to the subsequent request. One or more remedial actions may be performed in response to the health assessment data obtained.

Abstract: Techniques discussed herein are directed to identifying health assessment data of a set of computing instances of a distributed computing system. The health assessment data may be collected from the computing instances and stored in a first distributed cache. When a request for health assessment data for one or more computing instances is received, the health assessment data may be retrieved from the first distributed cache, provided to the requesting entity, and stored in a second distributed cache. A subsequent request may cause new health assessment data to be retrieved from the first distributed cache and compared to the stored data of the second distributed cache. Changes in the health assessment data may be identified and data indicating those changes may be provided in response to the subsequent request. One or more remedial actions may be performed in response to the health assessment data obtained.

Abstract: Techniques discussed herein are directed to identifying health assessment data of a set of computing instances of a distributed computing system. The health assessment data may be collected from the computing instances and stored in a first distributed cache. When a request for health assessment data for one or more computing instances is received, the health assessment data may be retrieved from the first distributed cache, provided to the requesting entity, and stored in a second distributed cache. A subsequent request may cause new health assessment data to be retrieved from the first distributed cache and compared to the stored data of the second distributed cache. Changes in the health assessment data may be identified and data indicating those changes may be provided in response to the subsequent request. One or more remedial actions may be performed in response to the health assessment data obtained.

Abstract: Techniques for performing NAT operations to send packets between networks are described. In an example, a network device receives a packet that comprises a header. The header indicates a source address of a first computing resource in a first network and a destination address of a second computing resource in a second network. The network device determines a pool of identifiers allocated for the first network and the second computing resource and identifies a packet flow based on the header. The network device also determines that no identifier from the pool of identifiers has been allocated for the packet flow and determines an identifier available to allocate for the packet flow from the pool of identifiers. The network device performs a NAT operation on the packet based on the identifier.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Abstract: Systems, methods, and computer-readable media for implementing roaming services utilizing zero-configuration networking over a wide area network. Disclosed are systems, methods, and computer-readable storage media for implementing zero-configuration networking over a wide area network by utilizing agents, application programming interfaces (API), and a controller. The controller can implement policies for communication between the agents and APIs, enabling zero-configuration network.

Abstract: A cohort service is configured to address the technical problem of providing recommendations to a member of an online connection network system in a manner that alleviates potentially excessive cognitive load associated with presenting recommended entities indiscriminately as a scrollable list. The cohort service is configured to visually surface recommended relevant entities already grouped as cohorts. A cohort is a grouping of entities based on one or more common attributes, such as, e.g., same school, same company, etc. The cohort service is designed to group recommendation results into cohorts at the server side, which increases the liquidity and the relevance of the recommended entities so that the already grouped recommendations can be sent to the client computer system for presentation to a viewer.