Abstract: An autonomous controller for SDN, virtual, and/or physical networks can be used to optimize a network automatically and determine new optimizations as a network scales. The controller trains models that can determine in real-time the optimal path for the flow of data from node A to B in an arbitrary network. The controller processes a network topology to determine relative importance of nodes in the network. The controller reduces a search space for a machine learning model by selecting pivotal nodes based on the determined relative importance. When a demand to transfer traffic between two hosts is detected, the controller utilizes an AI model to determine one or more of the pivotal nodes to be used in routing the traffic between the two hosts. The controller determines a path between the two hosts which comprises the selected pivotal nodes and deploys a routing configuration for the path to the network.

Abstract: Provided is a process of determining a future battery level of one or more battery-powered computing devices, the process including: accessing an event record in memory describing a scheduled event in which a user of a plurality of computing devices is scheduled to participate, inferring a subset of the plurality of computing devices to be used in that time period, determining present battery levels of the computing devices, the levels being values indicative of an amount of energy stored by batteries, determining present usage rates of battery energy, inferring battery outlooks corresponding to the scheduled event, a battery outlook being an estimated amount of energy consumption attributable to the scheduled event, and predicting future battery levels of computing devices based on at least a present battery level, a present usage rate, and a battery outlook corresponding to the scheduled event.

Abstract: A multivariate clustering-based anomaly detector can generate an event for consumption by an APM manager that indicates detection of an anomaly based on multivariate clustering analysis after topology-based feature selection. The anomaly detector accumulates time-series data across a series of time instants to form a multivariate time-series data slice or multivariate data slice. The anomaly detector then performs multivariate clustering analysis with the multivariate data slice. The anomaly detector determines whether a multivariate data slice is within a cluster of multivariate data slices. If the multivariate data slice is within the cluster and the cluster is a known anomaly cluster, then the anomaly detector generates an anomaly detection event indicating detection of the known anomaly. The anomaly detector can also determine that a multivariate data slice is within an unknown cluster and generate an event indicating detection of an unknown anomaly.

Abstract: An autonomous controller for SDN, virtual, and/or physical networks can be used to optimize a network automatically and determine new optimizations as a network scales. The controller trains models that can determine in real-time the optimal path for the flow of data from node A to B in an arbitrary network. The controller processes a network topology to determine relative importance of nodes in the network. The controller reduces a search space for a machine learning model by selecting pivotal nodes based on the determined relative importance. When a demand to transfer traffic between two hosts is detected, the controller utilizes an AI model to determine one or more of the pivotal nodes to be used in routing the traffic between the two hosts. The controller determines a path between the two hosts which comprises the selected pivotal nodes and deploys a routing configuration for the path to the network.

Abstract: An autonomous controller for SDN, virtual, and/or physical networks can be used to optimize a network automatically and determine new optimizations as a network scales. The controller trains models that can determine in real-time the optimal path for the flow of data from node A to B in an arbitrary network. The controller processes a network topology to determine relative importance of nodes in the network. The controller reduces a search space for a machine learning model by selecting pivotal nodes based on the determined relative importance. When a demand to transfer traffic between two hosts is detected, the controller utilizes an AI model to determine one or more of the pivotal nodes to be used in routing the traffic between the two hosts. The controller determines a path between the two hosts which comprises the selected pivotal nodes and deploys a routing configuration for the path to the network.

Abstract: An autonomous controller for SDN, virtual, and/or physical networks can be used to optimize a network automatically and determine new optimizations as a network scales. The controller trains models that can determine in real-time the optimal path for the flow of data from node A to B in an arbitrary network. The controller processes a network topology to determine relative importance of nodes in the network. The controller reduces a search space for a machine learning model by selecting pivotal nodes based on the determined relative importance. When a demand to transfer traffic between two hosts is detected, the controller utilizes an AI model to determine one or more of the pivotal nodes to be used in routing the traffic between the two hosts. The controller determines a path between the two hosts which comprises the selected pivotal nodes and deploys a routing configuration for the path to the network.

Abstract: Systems and methods for application development include predicting a probable set of risks (e.g., security risks, financial risks, legal risks etc.) and risk mitigations for software development or deployment risk management. The system records user activity with respect to assigning risks and risk mitigations to application components. The system utilizes user inputs and characteristics of the modelled application as well as the user inputs and characteristics associated with past development and deployment of similar applications in order to predict a probable set of risks and/or risk mitigation actions.

Abstract: A multivariate path-based anomaly detection and prediction service (“anomaly detector”) can generate a prediction event for consumption by the APM manager that indicates a likelihood of an anomaly occurring based on path analysis of multivariate values after topology-based feature selection. To predict that a set of metrics will travel to a cluster that represents anomalous application behavior, the anomaly detector analyzes a set of multivariate date slices that are not within a cluster to determine whether dimensionally reduced representations of the set of multivariate data slices fit a path as described by a function.

Abstract: To facilitate distinguishing between topics which belong to the same or similar semantic fields, previously-known domain information is modeled with a bipartite graph. The bipartite graph created for the software security domain indicates a set of risks and a set of mitigation actions. A topic categorization system utilizes the bipartite graph to identify which risks and mitigation actions were discussed in a conversation by first using existing NLP techniques to extract relevant topics from conversation text and subsequently mapping the topics to the bipartite graph. A security assessment report identifying potential security threats and corresponding mitigation actions is generated based on the resulting mappings. Conversation fragments which were extracted and mapped are included in the assessment report.

Abstract: An embodiment includes identifying a first location in memory containing first data rows copied from a second location in the memory containing second data rows retrieved from one or more objects in a data repository, selecting a portion of the first data rows to be scanned. The portion of the first data rows correspond to a first object of the one or more objects. The embodiment further includes performing a scan of the portion of the first data rows, calculating a probability that the first object contains sensitive data based, at least in part, on one or more instances of sensitive data identified during the scan, and marking the first object in the data repository with a sensitive data indicator. The sensitive data indicator is based, at least in part, on the probability that the first object contains sensitive data.

Abstract: A browser resource controller combines code metric values with a complexity analysis of rendered content to determine whether resource metric values are appropriate for a web application. The browser resource controller analyzes rendered content of a web application to generate the complexity metric values that represent the complexity of the web application. The browser resource controller also compares executable elements from the web application with exploitative code components from code repositories to determine an exploitative code risk. The browser resource controller determines a resource consumption limit for a web application based on both the exploitative code risk and the complexity metric values and compares the resource consumption limit to a detected resource consumption value.

Abstract: A digital currency driven channel assignment technique is disclosed. Each AP in a distributed network uses a channel selection manager and a distributed ledger to select channels according to a channel assignment and a digital currency associated with the distributed ledger. The digital currency incentivizes APs to make sacrifices in their channel selection for the benefit of the overall network while punishing APs that select channels selfishly and cause bandwidth interference.

Abstract: Provided is a computer system that includes a processor and a memory coupled to the processor, the memory including computer readable program code embodied therein that, when executed by the processor, causes the processor to generate a catalog that identifies a plurality of tasks that a plurality of network resources are available to perform, the network resources including Internet-of-things devices and human network resources and to generate, in response to receiving a request to perform a complex project, a solution path that includes an ordered list corresponding to selected ones of the plurality of tasks that are capable of aggregately performing the complex project, wherein the selected ones of the plurality of tasks define the solution path in an edge graph that include the plurality of tasks represented as edges therein.

Abstract: A network device associated with a database management system receives information associated with a customer support ticket. Based on information in the database management system, a direct relationship between the received customer support ticket and a customer support ticket in the database may be determined. A graph including nodes representing customer support tickets is generated based on information in the database. Edge prediction is performed on the graph to derive relationships among the nodes in the graph. A predictive relationship between customer support tickets is derived. A relationship data set based on the direct relationship between the customer support tickets and based on the predictive relationship between the customer support tickets is generated. The relationship data set associated with the customer support ticket is communicated to the user device.

Abstract: Provided is a process of determining a future battery level of one or more battery-powered computing devices, the process including: accessing an event record in memory describing a scheduled event in which a user of a plurality of computing devices is scheduled to participate, inferring a subset of the plurality of computing devices to be used in that time period, determining present battery levels of the computing devices, the levels being values indicative of an amount of energy stored by batteries, determining present usage rates of battery energy, inferring battery outlooks corresponding to the scheduled event, a battery outlook being an estimated amount of energy consumption attributable to the scheduled event, and predicting future battery levels of computing devices based on at least a present battery level, a present usage rate, and a battery outlook corresponding to the scheduled event.

Abstract: A multivariate path-based anomaly detection and prediction service (“anomaly detector”) can generate a prediction event for consumption by the APM manager that indicates a likelihood of an anomaly occurring based on path analysis of multivariate values after topology-based feature selection. To predict that a set of metrics will travel to a cluster that represents anomalous application behavior, the anomaly detector analyzes a set of multivariate date slices that are not within a cluster to determine whether dimensionally reduced representations of the set of multivariate data slices fit a path as described by a function.

Abstract: A multivariate clustering-based anomaly detector can generate an event for consumption by an APM manager that indicates detection of an anomaly based on multivariate clustering analysis after topology-based feature selection. The anomaly detector accumulates time-series data across a series of time instants to form a multivariate time-series data slice or multivariate data slice. The anomaly detector then performs multivariate clustering analysis with the multivariate data slice. The anomaly detector determines whether a multivariate data slice is within a cluster of multivariate data slices. If the multivariate data slice is within the cluster and the cluster is a known anomaly cluster, then the anomaly detector generates an anomaly detection event indicating detection of the known anomaly. The anomaly detector can also determine that a multivariate data slice is within an unknown cluster and generate an event indicating detection of an unknown anomaly.

Abstract: In a datacenter setting, a summary of differences and similarities between two or more states of the same or similar systems are predicted. Initially, a Long Short-Term Memory (LSTM) neural network is trained with to predict a summary describing the state change between at least two states of the datacenter. Given a set of training data (at least two datacenter states that are annotated with a state change description), the LSTM neural network learns which similarities and differences between the datacenter states correspond to the annotations. Accordingly, given a set of test data comprising at least two states of a datacenter represented by context graphs that indicate a plurality of relationships among a plurality of nodes corresponding to components of a datacenter, the LSTM neural network is able to determine a state change description that summarizes the differences and similarities between the at least two states of the datacenter.

Abstract: To aid in the root cause analysis of current system errors or anomalies, a graph-based root cause analysis software determines whether a graph representing an anomalous region of a system, referred to as a pattern, is similar to a previously stored pattern in a pattern library. The analysis software extracts a sub-graph or pattern representing components currently experiencing an anomaly from an overall system graph. The analysis software calculates a similarity score based on the comparison of the extracted pattern to patterns in the pattern library. The patterns in the pattern library represent previously encountered anomalies and include attributes, event data, expert/system administrator notes, etc., that can aid in diagnosing the current system anomaly.

Abstract: Determining a similarity between a pair of graphs or patterns can be a computationally expensive and time-consuming process. To reduce the similarity calculation costs, patterns can be simplified based on equivalent classes of components. A similarity score can be calculated between nodes of a pattern. The nodes which represent a same component type and have similar attributes will likely have a high similarity score and can be combined into a single node representing the entire class of the components. The decision to combine nodes also considers a node's topological features such as relationships and connections to other nodes. By combining equivalent nodes, the search space for mapping and determining similarity between two graphs can be reduced. Reducing the search space, exponentially reduces the number of iterations required for determining an optimal similarity score and improves the performance and scalability of the overall root cause analysis framework.