Abstract: Disclosed herein are system, method, and computer program product embodiments for implementing AI driven application navigation recommendations based on user behavior. An embodiment operates by generating a trained machine learning model using training data obtained based on historical navigation logs corresponding to the web application. The embodiment deploys a reduced machine learning model within an instance of the web application, and the reduced machine learning model is generated by compressing the trained machine learning model. The embodiment then generates the page navigation recommendation using the reduced machine learning model based on an encoded navigation breadcrumb data corresponding to the instance of the web application.

Abstract: Disclosed herein is an AI based system and method for generating warning alerts for a location to be excavated. The method comprises obtaining, from at least one external source, at least one underground asset map of the location to be excavated. For each of the at least one underground asset map, the method comprises locating a region of interest within the underground asset map corresponding to an identified underground utility service provider and extracting at least one feature within the region of interest. The at least one extracted feature is then compared with a plurality of features stored in a repository corresponding to the identified underground utility service provider, to determine a match. In response to the determination, the extracted feature is identified as a risk feature corresponding to the identified underground utility service provider and one or more warning alerts indicative of risk assets are generated.

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.

Abstract: Systems, methods, and computer-readable media for implementing local and wide network suppression of query requests in zero-configuration networking. Disclosed are systems, methods, and computer-readable storage media for implementing suppression of query packets zero-configuration networking over local and wide networks by utilizing agents, application programming interfaces (API), and a controller. The suppression can be determined based on two time periods, a processing period and a suppression period.

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: Embodiments relate to determining whether to take a resource distribution unit (RDU) of a datacenter offline when the RDU becomes faulty. RDUs in a cloud or datacenter supply a resource such as power, network connectivity, and the like to respective sets of hosts that provide computing resources to tenant units such as virtual machines (VMs). When an RDU becomes faulty some of the hosts that it supplies may continue to function and others may become unavailable for various reasons. This can make a decision of whether to take the RDU offline for repair difficult, since in some situations countervailing requirements of the datacenter may be at odds. To decide whether to take an RDU offline, the potential impact on availability of tenant VMs, unused capacity of the datacenter, a number or ratio of unavailable hosts on the RDU, and other factors may be considered to make a balanced decision.

Abstract: An optimized and efficient method of identifying one or more points within a dataset that are close to the centers of clumps similar records in a large, multi-element dataset uses Monte Carlo techniques to compute approximate clustering costs at significantly reduced computational expense. The inaccuracy caused by the approximate methods is also estimated, and if it is too high, the method may be repeated with a larger Monte Carlo sample size to improve accuracy. Portions of the algorithm that are independent are distributed among a number of cooperating computing nodes so that the full algorithm can be completed in less time.