Abstract: A computer program product, system, and computer implemented method for application-level redirect trapping and creation of NAT mapping to work with routing infrastructure for private connectivity in cloud and customer networks. The approach disclosed herein generally comprises a method of leveraging a reverse connection endpoint and IP address mapping controller to capture redirection messages from a private cloud or network (e.g., a service consumer network or a service consumer hybrid cloud). This allows at least the IP address mapping controller to manage a cloud networking infrastructure to provide for a service provider network (e.g., a public cloud) to support applications that overcome the isolation requirements of a private cloud or network to perform useful work. For example, without saddling the private cloud or network user with a heavy pre-configuration burden, the approach disclosed herein supports redirection to dynamically determined IP addresses at the private cloud or network.

Abstract: Techniques discussed herein include providing a cloud computing environment in which applications are deployed by a deployment orchestrator using virtual-machine-based virtualization with a static pool of computing nodes (e.g., substrate nodes, overlay nodes) and container-based virtualization with a dynamic pool of computing nodes (e.g., nodes managed by a container orchestration platform). Components of a data plane may be used to deploy containers to micro-virtual machines. A container runtime interface (CRI) may receive a deployment request from the deployment orchestrator. A container networking interface of the data plane may configure network connections and allocate an IP address for the container. A container runtime of the data pane may generate and configure the container with the IP address and run the container within a micro-virtual machine that is compatible with the container orchestration platform.

Abstract: Techniques discussed herein include providing a cloud computing environment in which applications are deployed using virtual-machine-based virtualization with a static pool of computing nodes (e.g., substrate nodes, overlay nodes) and container-based virtualization with a dynamic pool of computing nodes (e.g., nodes managed by a container orchestration platform). The control plane functionality may be invoked by a deployment orchestrator (e.g., using a client of the container orchestration platform). In some embodiments, the control plane may include a set of applications that are configured to communicate with core services for certificate generation and rotation, namespace and quota management, metric monitoring and alarming, node authentication, and cluster membership management.

Abstract: Systems and methods for a VLAN switching and routing service (VSRS) are disclosed herein. A method can include generating a table for an instance of a VSRS, which VSRS couples a first virtual layer 2 network (VLAN) with a second network. The table can contain information identifying IP addresses, MAC addresses, and virtual interface identifiers for instances within the virtual layer 2 network. The method can include receiving with the VSRS a packet from a first instance designated for delivery to a second instance within the virtual layer 2 network, identifying with the VSRS the second instance within the virtual layer 2 network for delivery of the packet based on information received with the packet and information contained within the table, and delivering the packet to the identified second instance.

Abstract: A model validation system is described that is configured to automatically validate model artifacts corresponding to models. For a model artifact being validated, the model validation system is configured to dynamically determine the validation checks to be performed for the model artifact, where the validation checks include various validation checks to be performed at the model artifact level and also for individual components included in the model artifact. The checks to be performed are dynamically determined based upon the attributes of the model artifact and of the components within the model artifact. The system is configured to generate a validation report that comprises information regarding the checks performed and the results generated from performing the various validation checks. The validation report may also include information suggesting actions for passing checks that result in a failed check, or for improving the scores of certain validation checks.

Abstract: In some aspects, techniques may include monitoring a primary load of a datacenter and a reserve load of the datacenter. The primary load and reserve load can be monitored by a computing device. The primary load of the datacenter can be configured to be powered by one or more primary generator blocks having a primary capacity, and the reserve load of the datacenter can be configured to be powered by one or more reserve generator blocks having a reserve capacity. Also, the techniques may include detecting that the primary load of the datacenter exceeds the primary capacity. In addition, the techniques may include connecting the reserve generator blocks to at least one of the primary generator blocks and the primary load using a computing device switch.

Abstract: Described herein are systems and methods for a DAG based transaction processing system and method in a distributed ledger. In accordance with an embodiment, a DAG based transaction processing system and method in a distributed ledger can be introduced. The model can help achieve improved throughput performance. With additional weight mechanism, the final performance can be adjusted based on various business requirements. This is different from existing work that uses linear structure and can achieve better performance.

Abstract: Techniques for storing and processing large volumes of data by increasing the number of partitions that customers can allocate and use for their streams while minimizing the physical footprint of the partitions is disclosed. In certain examples, the stream partitions are created as different logical partitions on a physical partition in a physical storage cluster of a distributed message streaming system. The disclosed system provides an improved process for reading messages stored in different logical partitions of a physical partition by identifying partition chunks in a physical partition that comprise subsets of messages associated with a logical partition and filtering out partition chunks that do not contain messages associated with the logical partition. The disclosed system reduces the amount of the physical partition that needs to be read to retrieve messages stored in different logical partitions thereby improving the overall performance of the distributed message streaming system.

Abstract: A parametric constant resolves to different values in different contexts, but a single value within a particular context. An anchor constant is a parametric constant that allows for a degree of parametricity for an API point. The context for the anchor constant is provided by a caller to the API point. The anchor constant resolves to an anchor value that records specialization decisions for the API point within the provided context. Specialization decisions may include type restrictions, memory layout, and/or memory size. The anchor value together with an unspecialized type of the API point result in a specialized type of the API point. A class object representing the specialized type is created. The class object may be accessible to the caller, but the full value of the anchor value is not accessible to the caller. The API point is executed based on the specialization decisions embodied in the anchor value.

Abstract: Embodiments forecast demand of an item by receiving historical sales data for the item for a plurality of past time periods including a plurality of features that define one or more feature sets. Embodiments use the feature sets as inputs to one or more different algorithms to generate a plurality of different models. Embodiments train each of the different models. Embodiments use each of the trained models to generate a plurality of past demand forecasts for each of some or all of the past time periods. Embodiments determine a root-mean-square error (“RMSE”) for each of the past demand forecasts and, based on the RMSE, determine a weight for each of the trained models and normalize each weight. Embodiments then generate a final demand forecast for the item for each future time period by combining a weighted value for each trained model.

Abstract: In exemplary embodiments, a computer system detects performance anomalies for user computer services. Physical databases convert physical database KPIs into logical database KPI vectors for individual user services and individual time intervals. Physical application servers convert physical application server KPIs into logical application server KPI vectors for the individual user services and time intervals. Physical web servers convert physical web server KPIs into logical web server KPI vectors for the individual user services and time intervals. A KPI server combines the logical database KPI vectors, the logical application server KPI vectors, and the logical web server KPI vectors for individual time intervals into user service KPI vectors for the individual user services during the time intervals. The KPI server detects outlier user service KPI vectors as compared to normal user service KPI vectors and indicates corresponding computer performance anomalies for the user services.

Abstract: Herein are techniques for dynamic aggregation of results of a database request, including concurrent grouping of result items in memory based on quasi-dense keys. Each of many computational threads concurrently performs as follows. A hash code is calculated that represents a particular natural grouping key (NGK) for an aggregate result of a database request. Based on the hash code, the thread detects that a set of distinct NGKs that are already stored in the aggregate result does not contain the particular NGK. A distinct dense grouping key for the particular NGK is statefully generated. The dense grouping key is bound to the particular NGK. Based on said binding, the particular NGK is added to the set of distinct NGKs in the aggregate result.

Abstract: A first set and a second set are identified as operands for a set operation of a similarity analysis task iteration. Using respective minimum hash information arrays and contributor count arrays of the two sets, a minimum hash information array and contributor count array of a derived set resulting from the set operation is generated. An entry in the contributor count array of the derived set indicates the number of child sets of the derived set that meet a criterion with respect to a corresponding entry in the minimum hash information array of the derived set. The generated minimum hash information array and the contributor count array are stored as part of input for a subsequent iteration. After a termination criterion of the task is met, output of the task is stored.

Abstract: A server system can receive an input identifying a problem to generate a solution using a machine-learning application. The method selects a machine-learning model template from a plurality of templates based at least in part on the input. The method analyzes one or more formats of the customer data to generate a customer data schema based at least in part a data ontology that applies to the identified problem. The method determines whether the customer data schema is misaligned with one or more key features of the selected machine-learning model template. Based on this determination, the method analyzes the metadata for the selected machine-learning model template to determine what additional information is required to re-align the customer data with the data expectations. The method can include gathering the addition information required to re-align the customer data with the data expectations of the selected machine-learning model template.

Abstract: Techniques are disclosed for cryptographically secure shuffling processes for generating and utilizing secrets in an infrastructure-as-a-service (IaaS) environment. In an embodiment, a method comprises generating a source list and a destination list, the source list and destination list association with a sequential format and the source list comprising a plurality of elements in the sequential format; generating a first random number and a second random number; determining a first element in the source list, the first element corresponding to a position in the sequential format of the source list based on the first random number; determining a first destination position in the destination list, the first destination position corresponding to a position in the sequential format of the destination list based on the second random number; and updating the destination list to include the first element in the source list at the first destination position.

Abstract: The disclosed systems, methods and computer readable media relate to managing Non-Volatile Memory Express (NVMe) over Transmission Control Protocol (TCP) (NVMeOTCP) connections between a smart network interface card (smartNIC) and a block storage data plane (BSDP) of a cloud computing environment. A software agent (“agent”) executing at the smartNIC may manage a number of network paths (active and, in some cases, passive network paths). The agent may monitor the network traffic (e.g., input/output operations (IOPS)) through the paths (e.g., using established NVMeOTCP connections corresponding to the paths). If a condition is met relating to a performance threshold associated with the monitored paths, the agent may increase or decrease the number established NVMeOTCP connections to match real time network conditions.

Abstract: The disclosed embodiments relate to a system that characterizes susceptibility of an inferential model to follow signal degradation. During operation, the system receives a set of time-series signals associated with sensors in a monitored system during normal fault-free operation. Next, the system trains the inferential model using the set of time-series signals. The system then characterizes susceptibility of the inferential model to follow signal degradation. During this process, the system adds degradation to a signal in the set of time-series signals to produce a degraded signal. Next, the system uses the inferential model to perform prognostic-surveillance operations on the set of time-series signals with the degraded signal. Finally, the system characterizes susceptibility of the inferential model to follow degradation in the signal based on results of the prognostic-surveillance operations.

Abstract: Techniques described herein allow a user of an RDBMS to specify a graph algorithm function (GAF) declaration, which defines a graph algorithm that takes a graph object as input and returns a logical graph object as output. A database dictionary stores the GAF declaration, which allows addition of GAFs without changing the RDBMS kernel. GAFs are used within graph queries to compute output properties of property graph objects. Output properties are accessible in the enclosing graph pattern matching query, and are live for the duration of the query cursor execution. According to various embodiments, the declaration of a GAF includes a DESCRIBE function, used for semantic analysis of the GAF, and an EXECUTE function, which defines the operations performed by the GAF. Furthermore, composition of GAFs in a graph query is done by supplying, as the input graph argument of an outer GAF, the result of an inner GAF.

Abstract: Techniques for a database management system to predict when in the future a materialized view will have a quiet period during which the materialized view will not be stale. This is a followed by an approach that uses the quiet period prediction to determine an optimized schedule for refreshing the materialized view. The approach combines the quiet period prediction with a query rewrite pattern prediction for the materialized view and an estimated refresh duration for the materialized view to determine the optimized refresh schedule for the materialized view.

Abstract: In accordance with an embodiment, described herein is a system and method for dependency analysis for a calculation script in a multidimensional database computing environment. A multidimensional database cube aggregation can be represented as a lattice of blocks or cube, arranged according to a database outline (e.g., intra-dimensional or member hierarchy). When the multidimensional database system performs computations in parallel for a given calculation script, portions of the cube that can be computed concurrently are identified.