Abstract: Techniques for enforcing an egress policy at a target service are described. In an example, traffic is generated for a customer, where the traffic is generated by a customer network of the customer, such as a customer tenancy or an on-premise network. The traffic can be destined to the target service. The traffic can be tagged by the customer network (e.g., by a gateway of the customer network). The customer network can be associated with the egress policy. The customer can define the egress policy at different granularity levels by using different attributes. The target service can determine the egress policy based on the information tagged to the traffic and can enforce the egress policy, based on the customer-defined attributes, on the traffic that the target service is receiving.

Abstract: In an embodiment, a method may include accessing, by a computing system, a multi-node problem. The multi-node problem may include a plurality of nodes, each respective node having one or more node features. The method may include providing, by the computing system, each respective node with each respective node feature to a machine learning model. The method may include determining, by the computing system using the machine learning model, a subset of nodes of the plurality of nodes based at least in part on the respective node features. The method may include calculating, by the computing system, one or more solutions to the multi-node problem based at least in part on the subset of nodes. The method may include storing, by the computing system, the one or more solutions to the multi-node problem in a computer memory.

Abstract: A system may display a Graphical User Interface including a source region presenting a plurality of source data-serialization elements and a destination region presenting a plurality of destination data-serialization elements. The system may receive a user input associating a first destination data-serialization element, of the plurality of destination data-serialization elements, and a first source data-serialization element of the plurality of source data-serialization elements. Responsive to receiving the user input, the system may generate and store a mapping expression that defines a mapping association between the first source data-serialization element and the first destination data-serialization element. The system may present in a mapping region of the GUI displayed concurrently with the source region and the destination region, a mapping element representing the mapping association between the first source data-serialization element and the first destination data-serialization element.

Abstract: Techniques for placing content in virtual universes at locations contextually compatible with the content are disclosed. A system trains a machine learning model to identify virtual environments compatible with content based on attributes representing contexts of the environments. Using the machine learning model, the system determines a contextual environment for a target content item. The system selects the particular contextual environment for placement of the target content item based on the compatibility score.

Abstract: A method and one or more non-transitory storage media are provided to train and implement a one-hot encoder. During a training phase, computation of an encoder state is performed by executing a set of relational statements to extract unique categories in a first training data set, associate each unique category with a unique index, and generate a one-hot encoding for each unique category. The set of relational statements are executed by a query optimization engine. Execution of the set of relational statements is postponed until a result of each relational statement is needed, and the query optimization engine implements one or more optimizations when executing the set of relational statements. During an encoding phase, a set of categorical features in a second training data set are encoded based on the encoder state to form a set of encoded categorical features.

Abstract: Techniques for improving system performance based on data characteristics are disclosed. A system may receive updates to a first data set at a first frequency. The system selects a first storage configuration, from a plurality of storage configurations, for storing the first data set based on the first frequency, and stores the first data set in accordance with the first storage configuration. The system may further receive updates to a second data set at a second frequency. The system selects a second storage configuration, from the plurality of storage configurations, for storing the second data set based on the second frequency, and stores the second data set in accordance with the second storage configuration. The second storage configuration is different than the first storage configuration.

Abstract: Techniques are described for partitioning B-tree keys of file systems into key ranges for parallel processing in delta generation during file storage replications between file systems in different cloud infrastructure regions. In certain embodiments, a delta generation processing for cross-region replication may utilize a key-range splitting mechanism involving a recursive algorithm that partitions B-tree keys of a source file system into roughly equal-size key ranges. All the partitioned key ranges may be processed in parallel and concurrently by different processing threads, one thread per key range, to improve the performance of the delta generation and achieve scalability.

Abstract: Herein are graph machine learning explainability (MLX) techniques for invalid traffic detection. In an embodiment, a computer generates a graph that contains: a) domain vertices that represent network domains that received requests and b) address vertices that respectively represent network addresses from which the requests originated. Based on the graph, domain embeddings are generated that respectively encode the domain vertices. Based on the domain embeddings, multidomain embeddings are generated that respectively encode the network addresses. The multidomain embeddings are organized into multiple clusters of multidomain embeddings. A particular cluster is detected as suspicious. In an embodiment, an unsupervised trained graph model generates the multidomain embeddings. Based on the clusters of multidomain embeddings, feature importances are unsupervised trained. Based on the feature importances, an explanation is automatically generated for why an object is or is not suspicious.

Abstract: Aspects of the present application relate to systems, methods and non-transitory computer readable media for network virtualization in a rack-based switch. The method can include sending a communication from a first virtual machine (“VM”) instantiated on a first host machine to a first network virtualization Top of Rack (“ToR”) switch. The first network virtualization ToR can include a peripheral component interconnect express (“PCIe”) switch coupled to a plurality of host-side Ethernet ports, a virtualization device communicatingly coupled to the PCIe switch, which virtualization device can include a plurality of virtualization functions, and a switching ASIC coupled to the virtualization device and to a network-side Ethernet port. The method can include forming the communication into an Internet Protocol (“IP”) packet with a first virtualization function of the virtualization device, and sending the IP packet to a second VM with the switching ASIC.

Abstract: An autonomous agent receives a user query comprising the complex question. The agent can obtain, from a corpus of unstructured texts, an answer candidate text corresponding to the user query and comprising text from which the answer is subsequently identified. The agent may generate first linguistic data corresponding to the user query and second linguistic data corresponding to the answer candidate text. Each instance of linguistic data may comprise a combination of respective syntactic data, semantic data, and discourse data generated from the user query and/or answer candidate text. Both instances of linguistic data may be provided to a machine-learning model that has been previously trained to output an answer identified from an instance of unstructured text (e.g., the answer candidate text). The model may output the answer identified from the answer candidate text, which in turn may be provided in response to the user query.

Abstract: A method for reducing inter-PLMN forwarding of messages relating to Nnrf service operations includes receiving, at a first NRF, a first request for an Nnrf service operation, determining that the first request identifies a PLMN different from a PLMN of the first NRF, determining that the first request cannot be processed locally at the first NRF, and, in response, forwarding the first request to a second NRF in the PLMN identified in the first request. The method further includes receiving, from the second NRF, a response to the first request, caching information from the response, receiving a second request for an Nnrf service operation, determining that the second request identifies a PLMN different from the PLMN of the first NRF, determining that the second request can be processed locally at the first NRF, and processing the second request locally at the first NRF using the cached information from the first response.

Abstract: A degree of fragmentation is determined based on a number of holes present in a storage system layout or a portion of a layout. Edges between the holes and used portions of the storage system are tabulated by scanning a storage space. The occurrences of a pattern of used/available allocation units and/or the occurrences of another pattern available/used allocation units are recognized. A fragmentation value is calculated based on occurrences of the patterns in view of the total storage space. The present fragmentation measurement system utilizes the number of occurrences of the holes in assessing fragmentation.

Abstract: A concurrency-restricting lock may divide a set of threads waiting to acquire the lock into an active circulating set (ACS) that contends for the lock, and a passive set (PS) that awaits an opportunity to contend for the lock. The lock, which may include multiple constituent lock types, lists, or queues, may be unfair over the short term, but improve throughput of the underlying multithreaded application. Culling and long-term fairness policies may be applied to the lock to move excess threads from the ACS to the PS or promote threads from the PS to the ACS. These policies may constraint the size or distribution of threads in the ACS (which may be NUMA-aware). A waiting policy may avoid aggressive promotion from the PS to the ACS, and a short-term fairness policy may move a thread from the tail of a list or queue to its head.

Abstract: Embodiments detect stops by an entity on a pre-planned trip that includes a plurality of stops. For each stop of the plurality of stops, embodiments determine a distance to a nearest stop and generate a geofence for each of the stops, each geofence having a radius having a size based on the distance. Embodiments detect the entity entering one of the geofences and, based on the detecting the entity entering one of the geofences, determine that the entity has stopped at a corresponding stop.

Abstract: Using a computer system, an instruction is received to define or modify a permission constraint corresponding to one or more files. A permission-instruction data set representing the permission constraint is stored in a data store. Subsequent to storing the permission-instruction data, a user request to access a particular file is intercepted. The data store is queried to determine whether any pending permission-instruction data set corresponds to the particular file. In response to the query, it is determined that the permission-instruction data set corresponds to the particular file. A permission constraint of the particular file is added or modified based on the permission-instruction data set. Based on the modified or added permission constraint, it is determined whether and/or an extent to which the user request is authorized. A response to the user request based on the determination as to whether and/or an extent to which the user request is authorized.

Abstract: A blockchain object stores multiple user blockchains, each blockchain comprising an ordered set of records in the blockchain object. The records of a user blockchain have the same blockchain key value. Users can create multiple blockchains by establishing respective blockchain key values for the blockchains. Like blocks in a blockchain, the records in a user blockchain are ordered by a sequence of numbers that is specific to the user blockchain; each user blockchain has its own sequence of numbers. Each record in a user blockchain holds a sequence number in a field of the blockchain object. An efficient mechanism maintains and assigns a sequence number to a record when appended to a user blockchain.

Abstract: A model-based approach to determining an optimal configuration for a data center may use an environmental chamber to characterize the performance of various data center configurations at different combinations of temperature and altitude. Telemetry data may be recorded from different configurations as they execute a stress workload at each temperature/altitude combination, and the telemetry data may be used to train a corresponding library of models. When a new data center is being configured, the temperature/altitude of the new data center may be used to select a pre-trained model from a similar temperature/altitude. Performance of the current configuration can be compared to the performance of the model, and if the model performs better, a new configuration based on the model may be used as an optimal configuration for the data center.

Abstract: Techniques for smoothing a signal are disclosed. The system partitions the portion of the data sequence into a stable subsequence and an unstable subsequence of data points. The system applies a rate of change exhibited by the stable subsequence to the unstable subsequence to create a smoothed, more stable subsequence.

Abstract: A training request including an identifier that is indicative of a type of a machine learning (ML) model that is to be trained is received. A plurality of workers are maintained in a training pool, and a plurality of jobs are maintained in a queue of training jobs. Each worker is configured to train a particular type of ML model. Upon the training request being validated, a training job is created for the request and submitted to the queue of training jobs. For each type of ML model, a first metric and a second metric is obtained. A target metric is computed based on the first and the second metrics. The number of workers included in the training pool is modified based on the target metric.

Abstract: Techniques for restricting plug-in application recipes (“PIARs”) are disclosed. A PIAR management application receives a PIAR restriction via an authenticated user of the PIAR management application. The PIAR restriction is applicable to certain users of the PIAR management application. The PIAR restriction identifies functionality of the PIAR management application that would otherwise be available to a user of the PIAR management application if the user had an active account for a particular plug-in application that, once linked to that user in the PIAR management application and associated with a particular PIAR, would be capable of supplying at least part of the functionality. The PIAR management application stores the PIAR restriction, including information to block the restricted functionality. Based on the PIAR restriction, the PIAR management application blocks the restricted functionality. Many different types of functionality may be restricted, in various ways.