Abstract: The present embodiments relate to a secure boot partition for a cloud computing device of a cloud computing system. The computing device of the cloud computing system can transmit a first request for a pre-boot execution environment executable from a smart network interface card (SmartNIC). The computing device can receive the pre-boot environment executable from the SmartNIC and verify the pre-boot execution environment executable. The computing device can execute the pre-boot execution environment executable. Executing the pre-boot execution environment executable can include transmitting a second request secure boot metadata from the SmartNIC and receiving the secure boot metadata. Executing the pre-boot execution environment executable can further include mounting a boot partition, loading a boot loader obtained from the boot partition, verifying the boot loader based at least in part on the secure boot metadata, and executing the boot loader in response to verifying the boot loader.

Abstract: Herein, a computer generates and evaluates many preprocessor configurations for a window preprocessor that transforms a training timeseries dataset for an ML model. With each preprocessor configuration, the window preprocessor is configured. The window preprocessor then converts the training timeseries dataset into a configuration-specific point-based dataset that is based on the preprocessor configuration. The ML model is trained based on the configuration-specific point-based dataset to calculate a score for the preprocessor configuration. Based on the scores of the many preprocessor configurations, an optimal preprocessor configuration is selected for finally configuring the window preprocessor, after which, the window preprocessor can optimally transform a new timeseries dataset such as in an offline or online production environment such as for real-time processing of a live streaming timeseries.

Abstract: A computing device may receive a set of user documents. Data may be extracted from the documents to generate a first graph data structure with one or more initial graphs containing key-value pairs. A model may be trained on the first graph data structure to classify the pairs. Until a set of evaluation metrics for the model exceeds a set of deployment thresholds: generating, a set of evaluation metrics may be generated for the model. The set of evaluation metrics may be compared to the set of deployment thresholds. In response to a determination that the set of evaluation metrics are below the set of deployment thresholds: one or more new graphs may be generated from the one or more initial graphs in the first graph data structure to produce a second graph data structure. The first and second graph can be used to train the model.

Abstract: Techniques and apparatus for data networking are described. In one example, a method includes receiving a first Layer-2 Remote Direct Memory Access (RDMA) packet which includes a virtual local area network (VLAN) tag and a quality-of-service (QoS) data field; converting the first Layer-2 RDMA packet to a first Layer-3 encapsulated packet; and forwarding the first Layer-3 encapsulated packet to a switch fabric. In this method, the converting includes adding at least one header to the first Layer-2 RDMA packet, where the at least one header includes: a virtual network identifier that is based on information from the VLAN tag, and a QoS value that is based on information from the QoS data field.

Abstract: Methods for generating and utilizing a multi-modal discourse tree (MMDT) are provided herein. An extended discourse tree (EDT) may be generated (e.g., from a discourse tree (DT) or a communicative DT (CDT)) from a corpus of text. Data records (e.g., records contained numerical data) may be linked to the extended discourse tree to generate a multi-modal discourse tree. The multi-modal discourse tree may link any suitable text/records from disparate sources. For example, entities identified from elementary discourse units of the EDT may be matched to an entity of a data record. Causal links may be identified between EDTs and/or data records. Rhetorical relationships can be identified for each entity/causal link match to incorporate the data records with the EDT to generate a MMDT. The MMDT may be used to classify subsequent input, to generate answers to subsequent questions, to navigate the corpus of text and/or data records, or the like.

Abstract: Embodiments implement non-intrusive load monitoring using a novel learning scheme. A trained machine learning model configured to disaggregate device energy usage from household energy usage can be stored, where the machine learning model is trained to predict energy usage for a target device from household energy usage. Household energy usage over a period of time can be received, where the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices. Using the trained machine learning model, energy usage for the target device over the period of time can be predicted based on the received household energy usage.

Abstract: Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Abstract: Techniques for detecting and remediating anomalous intervals in time-series data of a monitored device are disclosed. A system trains a machine learning model on a combination of real data obtained from a monitoring device and false data generated by adding noise to the real data. The model predicts operating values for the device at individual intervals of a time-series data set. The system identifies anomalies in the time-series data based on differences between the predicted values and the real values. If the difference between a predicted value generate by the machine learning model and the real value exceeds a threshold, the system identifies a particular data point, such as a meter reading, as anomalous. The system ranks anomalies to perform remediation operations based on the ranking.

Abstract: A computer-implemented method to determine form factors of a tree includes building an input tree, wherein the input tree includes nodes. The method further includes implementing a first top-down pass to determine a universal number for each node in the input tree. The method further includes implementing a second top-down pass to determine form factors for each node of the input tree, wherein a form factor includes a depth and a width of the tree with a root in a corresponding node. The method further includes storing the form factors as part of node metadata or in a separate table.

Abstract: The present disclosure relates to systems and methods for automatic rule generation based on natural language input. Natural language input can be received. The natural language input can be tokenized. First tokens can be mapped to a first condition of a rule, and second tokens can be mapped to a second condition of the rule. A graph representation of the natural language input can be generated. A pre-generated, tenant-specific graph can be selected that corresponds to the graph representation of the natural language input. A rule can be generated based on the tenant-specific graph. The rule can be provided to facilitate implementation of the rule.

Abstract: Systems for repairing parse errors in query language statements are disclosed. In response to identifying one or more errors during parsing, a system can perform error recovery operations, including selecting a target sequence of parsable elements from candidate target sequences identified by a grammar. The operations can also include, determining a modification for the first sequence of elements including parsable elements corresponding to the target sequence. The operations can further include repairing first query by applying the modification to the first sequence. Additionally, the operations include generating error report, including information indicating the modifications that allowed parsing to succeed and suggested corrections.

Abstract: Each host machine of a plurality of host machines stores hierarchical locality information for the host machine that identifies at least a rack comprising the host machine, and a block of a plurality of blocks hosting the rack. The host machine is associated with one or more graphical processing units (GPUs), and wherein GPUs included in a first block operate at a first speed and GPUs included in a second block operate at a second speed that is different than the first speed. Responsive to receiving a request requesting execution of a workload, one or more host machines are identified as being available for executing the workload, and the hierarchical locality information and linkage information of the one or more host machines is provided in response to the request.

Abstract: Techniques in this disclosure include polling statuses of one or more assets with one or more messages signed by a device private key. The statuses can be polled by a computing device. The techniques can include receiving one or more replies from the one or more assets. A reply can be signed with an asset private key and the reply can contain an asset status. The computing device can validate the one or more replies using one or more asset public keys. The computing device can add the statuses from the validated replies to a status log.

Abstract: Described herein is a network fabric including a plurality of graphical processing unit (GPU) clusters that are communicatively coupled with one another via a plurality of switches arranged in a hierarchical structure including a first tier of switches, a second tier of switches, and a third tier of switches. One or more switches are selected from the third tier of switches to form a set of target switches, where each target switch receives address information of each GPU included in the plurality of GPU clusters. Each target switch generates, a plurality of sets of address information by filtering received address information based on a condition and transmits the plurality of sets of address information to each switch included in the first tier of switches, wherein the switch stores a subset of the plurality of sets of address information in accordance with the condition.

Abstract: Techniques are disclosed for implementing a dynamically reconfigurable network lattice within a distributed computing system. A computing device may determine a combined configuration that includes a union of at least a subset of cloud infrastructure component topologies. The computing device may then provide instructions for implementing a physical layer characterized by the combined configuration. The computing device may then determine a subset configuration corresponding to a cloud infrastructure component topology of the subset of cloud infrastructure component topologies. The computing device may then deploy the subset configuration to the physical layer of the distributed computing system.

Abstract: One or more engine instances are executed on each host to form an engine cluster. A plurality of control instances are executed on a first set of hosts to form a control cluster and comprise a control instance leader and one or more control instance followers. In response to a first host indicating a failure of a neighbor host, a pair-wise focused investigation is initiated to check peer-to-peer connections between the first host and the neighbor host. In response to one or more additional hosts indicating failures of neighbor hosts while the pair-wise focused investigation is being performed, a wide investigation is performed to check connections between the control cluster and the plurality of hosts. One or more hosts are added to an eviction list and an eviction protocol is performed to evict the one or more hosts from the engine cluster using the eviction list.

Abstract: The present disclosure relates to identifying process flows from log sources (e.g., log files), and generating visual representations (e.g., flow diagrams, Sankey diagrams, etc.) of the identified process flows. In addition, the present disclosure relates to clustering of tree structures based on the shape of the tree structure using one or more hashing algorithms. The present disclosure also relates to a user interface that presents a query builder for efficiently querying a log analytics system for tree structures that satisfy a user-defined range.

Abstract: Herein is database administration workflow automation with source annotations and intelligent scheduling techniques for applying a hierarchy of interdependent administrative tasks to distributed and/or nested databases. In an embodiment, a source language compiler analyzes annotations to identify a hierarchy of administrative tasks that administers pluggable databases in container databases. From the annotations, a runtime codebase is generated that implements and invokes the administrative task hierarchy. At runtime, a container database management system (CDBMS) autonomously identifies and instantiates the administrative tasks, including identifying a dependency of a first administrative task on a second administrative task and a lack of dependency of a third administrative task on the second administrative task.

Abstract: Log data that includes a plurality of log records is asynchronously processed to validate a configuration of each log record and data included in each log record. It is determined that one or more attributes of a particular subset of log records of the plurality of log records corresponds to one or more errors. Using the particular subset, one or more enriched log records are generated by augmenting each log record of the particular subset of log records with error information that indicates one or more categories corresponding to the one or more errors. A user interface is generated to facilitate correction of the one or more errors, the user interface comprising a plurality of interactive elements corresponding to a plurality of error metrics of different categories of errors, wherein the one or more categories of the one or more errors are included in the different categories of errors.

Abstract: Techniques are described for backup and restore of a thin-cloned data file. The process iterates through a plurality of memory portions of the thin-cloned data file and determines whether a memory portion of the thin-cloned data file is a memory portion with common data shared with the source data file. Without storing the common data of the shared memory portion into the thin-backup data file, the process stores placeholder metadata and corresponding reference to the shared memory portion for the thin-backup data file, in an embodiment. At restore, the process may replicate the derivative data, different from the common data, from the thin-backup data file into the thin-restored data file. For the common data, the process restores a reference for the thin-restored data file to the share memory portion, in an embodiment.