Abstract: Techniques to create zone maps automatically and efficiently for database query processing are disclosed. The techniques comprise creating a sample dataset to represent an original dataset, building a query workload modeler to characterize a full workload of queries, constructing a clustering quality evaluator to evaluate query performance on a dataset with a certain clustering on the columns, finding a clustering solution by evaluating different applications of the workload to the sample dataset corresponding to different clusterings, and determining which columns of the clustering solution could benefit from zone maps.

Abstract: Systems, computer-implemented methods, and computer-readable media for facilitating resource balancing based on resource capacities and resource assignments are disclosed. Electronic communications, received via interfaces, from monitoring devices to identify resource descriptions of resources may be monitored. A resource descriptions data store may be updated to associate each entity of the entities and resource capacities of each resource type of resource types. A first electronic communication, from resource-controlling systems, may be detected. Model data from a model data store may be accessed based on the identified resource descriptions. A first model may be identified based on the model data. A resources assessment corresponding may be generated based on whether a threshold is satisfied based on the first model, a first resource capacity of a first resource type, and the first electronic communication.

Abstract: In accordance with an embodiment, a system and method for dynamic auto-scaling based on roles is provided. Within a system, a node, based upon a load demand, can be duplicated. Additionally, when duplicating a node, the role of the node can be used to determine various characteristics of the duplicated node, taking into account various factors.

Abstract: A system is disclosed that includes capabilities for generating a Machine Learning (ML) inference pipeline for deploying an ML model using artifacts received from one or more training stages in an ML training pipeline. The system receives one or more artifacts for one or more training stages in a set of training stages in a ML training pipeline associated with an ML process. The system then identifies one or more inference stages in an ML inference pipeline that correspond to the one or more training stages in the ML training pipeline. For each inference stage that corresponds to a training stage, the system associates the artifact received for the training stage with the inference stage. The system then generates the ML inference pipeline comprising the inference stages and their associated artifacts, where the artifacts include the artifacts received for the training stages in the ML training pipeline.

Abstract: Techniques for de-centralized execution of workflows are disclosed. A system executes a sequence of jobs in a workflow. Each job includes application code to perform a defined set of functions. Each job includes wrapper code at one or both ends of the application code. The system executes the sequence of jobs by iteratively spinning up a virtual machine, loading workflow data to be used by application code, executing the application code, and storing modified workflow data. The virtual machine executing one job triggers the next virtual machine to execute the next job in the workflow, without scheduling the next job by a centralized job scheduler. Upon spinning up the next virtual machine, a virtual machine executing a preceding job shuts itself down.

Abstract: Client data corresponding to a single client can be stored. A decision to proceed with a defragmentation of two or more particular core groups can be determined. A destination core group to receive client data can be identified. At least one additional core group can be initiated to manage interim data write requests from the single client. Each of the two or more particular core groups can be closed to data write requests. A defragmentation of the two or more particular core groups can be performed by merging into the destination core group. The destination core group can be availed to data read and delete requests. The two or more fragmented core groups can be reallocated for other uses. The additional core groups continue to receive read and write requests. The destination core groups further can be merged and defragmented further.

Abstract: Aspects if the disclosure are directed towards multi-step forecasting via temporal aggregation. An example embodiment includes a method the includes receiving a time series including a first time step value and a second time step value. The method can further include generating a temporally aggregated time series by summing the first time step value and the second time step value to create a third time step value. The method can further include calculating a first set of input values and a second set of input values from the temporally aggregated time series. The method can further include forecasting a fourth time step value using the first set of input values and the second set of input values, and a fifth time step using the second set of input values from the temporally aggregated time series.

Abstract: A text prediction system for providing personalized text suggestions for different customers is disclosed. For a text message being composed via an application for an intended recipient, the system receives a portion of the text message based upon a current cursor position in the text message. The system identifies a customer associated with the intended recipient and identifies a customer-specific model for the customer. The system predicts text to be suggested for type-ahead insertion at the current cursor position using the customer-specific model and based upon the portion of the text message. The suggested text may include one or more data variables that are replaceable with values determined from data stored for the customer. The data variables provide further customization to the suggested text by presenting suggestions are more personalized for the recipient of the text message. The system causes the suggested text to be output via the application.

Abstract: Embodiments described herein are directed towards a synthetic document generation pipeline for training artificial intelligence models. One embodiment includes a method including a device that receives an instruction to generate a document to be used as a training instance for a first machine learning model, the instruction including an element configuration, a document class configuration, a format configuration, an augmentation configuration, and data bias and fairness. The device can receive an element from an interface based at least in part on the element configuration, the element can simulate a real-world image, real-world text, or real-world machine-readable visual code. The device can generate metadata describe a layout for the element on the document based on the document class configuration. The device can generate the document by arranging the element on the document based on the metadata, wherein the document is generated in a format based on the format configuration.

Abstract: Operations include determining whether to cache resources accessed by a query based on the execution time of the query. The system identifies a set of executions of a same query. The system determines a cumulative execution time for the set of executions of the same query. If the cumulative execution time exceeds a threshold value, then the system caches a resource used for execution of the query.

Abstract: Techniques are disclosed for deploying a computing resource (e.g., a service) in response to user input. A computer-implemented method can include operations of receiving (e.g., by a gateway computer of a cloud-computing environment) a request comprising an identifier for a computing component of the cloud-computing environment. The computing device receiving the request may determine whether the identifier exists in a routing table that is accessible to the computing device. If so, the request may be forwarded to the computing component. If not, the device may transmit an error code (e.g., to the user device that initiated the request) indicating the computing component is unavailable and a bootstrap request to a deployment orchestrator that is configured to deploy the requested computing component. Once deployed, the computing component may be added to a routing table such that subsequent requests can be properly routed to and processed by the computing component.

Abstract: The present embodiments relate to edge attestation of a host node to access a cloud infrastructure environment. A set of authentication data can be obtained from a console for authorization of the host node. The set of authentication data can include a first endorsement key and an authentication policy identifying characteristics of the host node. The host node can send a request for a network address to connect to the cloud infrastructure environment. The host node can generate a second endorsement key and authentication data that can be verified as corresponding to the set of authentication data received from the console. Responsive to validating the second endorsement key and the received host node authentication data, the network address can be provided to the host node that can be used to connect to the cloud infrastructure environment using the network address.

Abstract: Methods and systems are disclosed for automated semantic tagging that include detecting a particular thread executed by a processor and identifying a root process of the particular thread. An object-process link may be by linking an object that executed code that called the particular thread to the root process. A thread list of thread definitions of the object may be identified. A particular thread definition that corresponds to the particular thread can be mapped. Resource types to be consumed upon executing an instance of the thread instantiated from the particular thread definition can be identified and the corresponding values of the resource types can be determined. A process specification can be generated that encapsulates the thread definition, resource types and values so as to reproduce a state of the root process at a point in which the particular thread executed.

Abstract: Techniques are disclosed for tuning external invocations utilizing weight-based parameter resampling. In one example, a computer system determines a plurality of samples, each sample being associated with a parameter value of a plurality of potential parameter values of a particular parameter. The computer system assigns weights to each of the parameter values, and then selects a first sample for processing via a first external invocation based on a weight of the parameter value of the first sample. The computer system then determines feedback data associated with a level of performance of the first external invocation. The computer system adjusts the weights of the parameter values of the particular parameter based on the feedback data. The computer system then selects a second sample of the plurality of samples to be processed via execution of a second external invocation based on the adjustment of weights of the parameter values.

Abstract: Techniques for predictive system remediation are disclosed. Based on attributes associated with applications of one or more system-selected remedial actions to one or more problematic system behaviors in a system (e.g., a database system), the system determines a predicted effectiveness of one or more future applications of a remedial action to a particular problematic system behavior, as of one or more future times. The system determines that the predicted effectiveness of the one or more future applications of the remedial action is positive but does not satisfy a performance criterion. Responsive to determining that the predicted effectiveness is positive but does not satisfy the performance criterion, the system generates a notification corresponding to the predicted effectiveness not satisfying the performance criterion.

Abstract: Example embodiments employ a selective memory swapping system for selectively placing non-volatile memory devices of a computer system offline, e.g., for background updating, and online, for use by a computer system, whereby the background updating process includes a mechanism for performing forensics analysis and updating of offline memory devices while an alternate memory device is usable by a user of the first computer system.

Abstract: Techniques for expense report submission are disclosed. An expense report submission system receives, via a graphical user interface, a user query that corresponds to requesting whether a particular expense is allowed. The expense report submission system applies the user query to a machine learning model configured to evaluate data associated with expenses against one or more expense policy rules. The expense report submission system generates a response to the user query based at least on a result of applying the user query to the machine learning model. The response to the user query indicates whether the expense is allowed based at least on the expense reporting rule(s). The expense report submission system presents, in the graphical user interface, the response to the user query.

Abstract: Resource-to-operation pairs are received at a user interface. The resource-to-operation pairs are stored in a model abstraction as a flat structure. The resource-to-operation pairs in the flat structure are converted into Representational State Transfer (REST) connectors. The REST connectors are encoded in a conventional interface description language. The REST connectors are stored in webpage code. A request is received at the webpage code from the client system for a service of a process step of the process-based software application. A REST connector in the webpage code translates the request to a message that conforms with the service. A response from the service is received at the webpage code. The response from the service is processed at the webpage code. Data retrieved by the processing of the response is accessed via a browser located at the client system.

Abstract: A first data accessor acquires a lock associated with a critical section. The first data accessor initiates a help session associated with a first operation of the critical section. In the help session, a second data accessor (which has not acquired the first lock) performs one or more sub-operations of the first operation. The first data accessor releases the lock after at least the first operation has been completed.

Abstract: Improved techniques for dialogue management are disclosed. In particular, disclosed systems facilitate improved autonomous agents that can generate a virtual social dialogue from a corpus of text. A virtual social dialogue is a dialogue between autonomous agents and user devices. For example, a virtual social dialogue can include viewpoints, concerns, or questions of various actors on a particular topic. By presenting textual content in this manner, disclosed techniques improve information comprehension and increase the practicality of autonomous agents.

Abstract: Columns of a table are stored in either row-major format or column-major format in an in-memory DBMS. For a given table, one set of columns is stored in column-major format; another set of columns for a table are stored in row-major format. This way of storing columns of a table is referred to herein as dual-major format. In addition, a row in a dual-major table is updated “in-place”, that is, updates are made directly to column-major columns without creating an interim row-major form of the column-major columns of the row. Users may submit database definition language (“DDL”) commands that declare the row-major columns and column-major columns of a table.

Abstract: The present memory restoration system enables a collection of computing systems to prepare inactive rewritable memory for reserve and future replacement of other memory while the other memory is active and available for access by a user of the computing system. The preparation of the reserved memory part is performed off-line in a manner that is isolated from the current user of the active memory part. Preparation of memory includes erasure of data, reconfiguration, etc. The memory restoration system allows for simple exchange of the reserved memory part, once the active memory part is returned. The previously active memory may be concurrently recycled for future reuse in this same manner to become a reserved memory. This enables the computing collection infrastructure to “swap” to what was previously the inactive memory part when a user vacates a server, speeding up the server wipe process.

Abstract: A method for automatic domain name system (DNS) configuration for 5G core (5GC) network functions (NFs) includes, at an NF repository function (NRF) including at least one processor, receiving a message concerning a 5GC network function. The method further includes determining a first DNS resource record parameter for the 5GC NF. The method further includes determining a second DNS resource record parameter for the 5GC NF. The method further includes automatically configuring a DNS with a mapping between the first and second DNS resource record parameters for the 5GC NF.

Abstract: A method for communicating delegated NF discovery results between SCPs and using the delegated NF discovery results for alternate SBI service request routing includes receiving, at a first SCP and from a consumer NF instance, an SBI service request. The method further includes determining that delegated NF discovery is required, and, in response, performing delegated NF discovery on behalf of the consumer NF instance. The method further includes receiving an NF discovery response including delegated NF discovery results. The method further includes sending the SBI service request including the delegated NF discovery results to a second SCP.

Abstract: A system configured to implement Comprehensive Contention-Based Thread Allocation and Placement, may generate a description of a workload from multiple profiling runs and may combine this workload description with a description of the machine's hardware to model the workload's performance over alternative thread placements. For instance, the system may generate a machine description based on executing stress applications and machine performance counters monitoring various performance indicators during execution of a synthetic workload. Such a system may also generate a workload description based on profiling sessions and the performance counters. Additionally, behavior of a workload with a proposed thread placement may be modeled based on the machine description and workload description and a prediction of the workload's resource demands and/or performance may be generated.

Abstract: A method for detecting a security vulnerability in code may include obtaining (i) a permitted information flow graph for a permitted query and (ii) a target information flow graph for a target query in the code, determining, by traversing the permitted information flow graph, a permitted information flow including permitted disclosed columns, permitted accessed columns, and a permitted predicate, determining, by traversing the target information flow graph, a target information flow including target disclosed columns, target accessed columns, and a target predicate, comparing the permitted information flow and the target information flow to obtain a comparison result, and determining, based on the comparison result, that the target query includes the security vulnerability.

Abstract: In an embodiment, a computer stores source files and source clone files in a source filesystem. The source clone files are shallow copies of the source files, which initially share the same data blocks as the source files. A source file or a source clone file may eventually be modified, where some of the shared blocks are replaced by modified blocks. In a first phase, all data blocks of all source files that are not clones are replicated to a standby filesystem. A second phase compares each clone file on the source filesystem with its base file to detect a set of differed blocks. The second phase copies the differed blocks into the standby filesystem and, in the standby filesystem, applies the differed blocks to a corresponding clone file. Efficiency of keeping the standby filesystem synchronized with the source filesystem is improved by transferring, from the source filesystem to the standby filesystem, only one copy of only data blocks that are modified.

Abstract: In some aspects, a computing device of the virtual cloud network may select one or more filters from a plurality of filters for a data pipeline, the plurality of filters comprising at least one of: a malware filter; a content filter; a signature filter; a content analyzer; a machine learning filter; or an artificial intelligence filter. A sequential order for the one or more selected filters in the data pipeline can be determined. A message may be received in the data pipeline from a network interface card (NIC), the network interface card being configured as a one-way transfer device. The message in the data pipeline may be filtered by passing the message through the one or more selected filters in the determined sequential order. The computing device of the virtual cloud network may provide logs of events occurring in the data pipeline via a logging network.

Abstract: A system is provided for training an inferential model based on selected training vectors. During operation, the system receives training data comprising observations for a set of time-series signals gathered from sensors in a monitored system during normal fault-free operation. Next, the system divides the observations into N subgroups comprising non-overlapping time windows of observations. The system then selects observations with a local minimum value and a local maximum value for all signals from each subgroup to be training vectors for the inferential model. Finally, the system trains the inferential model using the selected training vectors. Note that by selecting observations with local minimum and maximum values to be training vectors, the system maximizes an operational range for the training vectors, which reduces clipping in estimates subsequently produced by the inferential model and thereby reduces false alarms.

Abstract: Disclosed are techniques for processing user profiles using data structures that are specialized for processing by a GPU. More particularly, the disclosed techniques relate to systems and methods for evaluating characteristics of user profiles to determine whether to offload certain user profiles to the GPU for processing or to process the user profiles locally by one or more central processing units (CPUs). Processing user profiles may include comparing the interest tags included in the user profiles with logic trees, for example, logic trees representing marketing campaigns, to identify user profiles that match the campaigns.

Abstract: Novel techniques are described for positive entity-aware augmentation using a two-stage augmentation to improve the stability of the model to entity value changes for intent prediction. In one particular aspect, a method is provided that includes accessing a first set of training data for an intent prediction model, the first set of training data comprising utterances and intent labels; applying one or more positive data augmentation techniques to the first set of training data, depending on the tuning requirements for hyper-parameters, to result in a second set of training data, where the positive data augmentation techniques comprise Entity-Aware (“EA”) technique and a two-stage augmentation technique; combining the first set of training data and the second set of training data to generate expanded training data; and training the intent prediction model using the expanded training data.

Abstract: Novel techniques are described for negative entity-aware augmentation using a two-stage augmentation to improve the stability of the model to entity value changes for intent prediction. In some embodiments, a method comprises accessing a first set of training data for an intent prediction model, the first set of training data comprising utterances and intent labels; applying one or more negative entity-aware data augmentation techniques to the first set of training data, depending on the tuning requirements for hyper-parameters, to result in a second set of training data, where the one or more negative entity-aware data augmentation techniques comprise Keyword Augmentation Technique (“KAT”) plus entity without context technique and KAT plus entity in random context as OOD technique; combining the first set of training data and the second set of training data to generate expanded training data; and training the intent prediction model using the expanded training data.

Abstract: Techniques for persisting user data across secure shell instances are provided. A method includes receiving a first request from a session manager service to establish a connection to a secure shell instance and restore a user block volume with corresponding backup user data. The method may include reserving an empty block volume. The method may also include transmitting a backup data identifier associated with the corresponding backup user data to a backup service and receiving the corresponding backup user data from the backup service. The method may further include providing the corresponding backup user data to the empty block volume to create a restore volume and transmitting a restore volume identifier corresponding to a data center identifier to the session manager service. The method may include receiving a second request to attach the restore volume to a reserved instance, the second request being received from the session manager service.

Abstract: Machine learning techniques are disclosed for predicting a task event such as a service completion event based on a predefined workflow. In one aspect a method includes obtaining initial data for a service request (e.g., an account application), enriching the initial data with data from one or more repositories of an enterprise executing the service request, generating a data structure comprising independent variables extracted from the enriched data, receiving a request for a prediction of a completion time for the service request (e.g., an account opening event) at a first time during processing of the service request in accordance with each workflow, in response to receiving the request for the prediction, inputting the data structure into a machine-learning regression model, predicting, using the machine-learning regression model, a completion time for the service request, and providing the completion time for the service request.

Abstract: Novel techniques are described for data augmentation using a two-stage entity-aware augmentation to improve model robustness to entity value changes for intent prediction.

Abstract: Disclosed systems relate to propagating tracing across a distributed software application. In an example, an application logs tracing data for a web page from an original server. The application determines, at the web browser, that the web page from the original server requires a request to an external server outside a domain of the original server. The application interrogates the external server to determine whether the external server allows tracing headers in requests. The application updates an allow list, at the web browser, to indicate that the external server allows tracing headers in requests from the original server. The application injects a tracing header in the request based on a result of the interrogation. The application sends, from the web browser to the external server, the request with the tracing header. The external server is configured to log tracing data based on the tracing header.

Abstract: Cyber-security techniques are described for monitoring a cloud environment and identifying potential problems, including malicious threats, to the monitored cloud environment using operational telemetry. Techniques are described for monitoring and collecting data related to reverse or recursive DNS (rDNS) traffic associated with a monitored cloud environment. The recursive DNS traffic includes recursive DNS (rDNS) requests originating from the cloud environment and responses to those requests received from DNS resolvers. This collected data is then analyzed to identify potential threats to the monitored cloud environment. The collected data may be analyzed to identify potential sources of threats and to identify one or more portions of the cloud environment that are the targets of the threats. The analysis may trigger alerts to be generated, actions to be performed (e.g., protective measures), reports to be generated, patterns to be recognized, etc.

Abstract: In accordance with an embodiment, an intelligent load plan that can automatically ensure that a system is in a runnable state and no other conflicting activity can affect the results of the data loads, such as ETLs. Such an intelligent load plan can be used in on data warehousing solutions as well as on a cloud data warehouse solution. The intelligent load plan can, in some embodiments, automatically detect situations based on which it knows it is not supposed to run the load plan.

Abstract: In one aspect, a system receives a request to cluster a set of log records. Responsive to receiving the request, the system identifies at least one dictionary that defines a set of tokens and corresponding token weights and generates, based at least in part on the set of tokens and corresponding token weights, a set of clusters such that each cluster in the set of clusters represents a unique combination of two or more tokens from the dictionary and groups a subset of log records mapped to the unique combination of two or more tokens. The system may then perform one or more automated actions based on at least one cluster in the set of clusters.

Abstract: A network interface card, such as a SmartNIC, is used to provide encryption, such as network encryption virtual function (NEVF), for a virtual machine, so that a customer can control network keys in a virtual cloud network. The NEVF includes a memory device (e.g., SRAM) and a crypto processor (e.g., a crypto core). The memory device stores a crypto key. The crypto processor uses the crypto key to encrypt data to and from a virtual machine in the virtual cloud network. A key management system can be used to securely transfer crypto keys to the NEVF. Having one NEVF per virtual machine can enable a customer to manage the crypto key for a virtual cloud network.

Abstract: Methods and systems are disclosed for automatic generation of content distribution images that include receiving user input corresponding to a content-distribution operation. The user input may be parsed to identify keywords. Image data corresponding to the keywords can be identified. Image-processing operations may be executed on the image data. Executing a generative adversarial network on the processed image data, which includes: executing a first neural network on the processed-image data to generate first images that correspond to the keywords, the first images generated based on a likelihood that each image of the first images would not be detected as having been generated by the first neural network. A user interface can display the first images with second images that include images that were previously part of content-distribution operations or images that were designated by an entity as being available for content-distribution operations.

Abstract: The present disclosure relates to managing services by a managed service provider (MSP) in a cloud based infrastructure. A control plane of the MSP is established in a first tenancy, and a first access plane of the MSP is established in a second tenancy of a cloud environment. The control plane is configured to manage a plurality of services offered by the MSP to a first host machine included in the second tenancy. A first request is transmitted from the control plane to the first access plane, where the first request is forwarded by the first access plane to the first host machine, and corresponds to a service utilized by the first host machine and managed by the control plane of the MSP. In response to the first request being validated, a first state of the first host machine is modified in the second tenancy based on the first request.

Abstract: A method for supporting configurable producer network function (NF) Internet protocol (IP) address mappings includes, at an NF repository function (NRF), receiving, from a requesting node, a request message for network address and/or service information of a producer NF. The method further includes determining, from the request message, at least one consumer NF parameter. The method further includes using the at least one consumer NF parameter, a producer NF IP address mapping rule. The method further includes, in response to locating the producer NF IP address mapping rule, determining, using the producer NF IP address mapping rule, an IP address to return to the requesting node. The method further includes generating a response message including the IP address and transmitting the response message to the requesting node.

Abstract: A content string can be identified from a resource file that defines one or more webpages of a web site. The content string can include particular content to be displayed in a webpage. The resource file can indicate a location associated with the particular content relative to other content to be displayed in the webpage. The webpage can be of the one or more webpages and associated with the content string. Key information for the content string can be identified. The key information can be unique relative to other key information associated with other content strings in the one or more resource files. A modified content string can be generated that includes the content, identifier of the resource file, and key information. The one or more resource files can be updated to include the modified content string. The one or more resource files can be transmitted to a receiving device.

Abstract: In some aspects, a computing device of the disconnected network may generate an application programming interface (API) configured to present a set of filter types. A selection of one or more filters types from the set of filter types may be received via an API. A sequential order for the selected filter types may be received via an API. A data pipeline with the selection of filters in the sequential order may be generated. A message received at a one-way transfer device may be analyzed by passing the message through the selected filters in the sequential order. A log of events occurring in the data pipeline may be received via a logging network. The log of events may be presented via the application programing interface. The data pipeline may be terminated upon receiving a termination command via the application programming interface.

Abstract: Techniques describes herein include using software tools and feature vector comparisons to analyze and recommend images, text content, and other relevant media content from a content repository. A digital content recommendation tool may communicate with a number of back-end services and content repositories to analyze text and/or visual input, extract keywords or topics from the input, classify and tag the input content, and store the classified/tagged content in one or more content repositories. Input text and/or input images may be converted into vectors within a multi-dimensional vector space, and compared to a plurality of feature vectors within a vector space to identify relevant content items within a content repository. Such comparisons may include exhaustive deep searches and/or efficient tag-based filtered searches. Relevant content items (e.g., images, audio and/or video clips, links to related articles, etc.

Abstract: Novel techniques are described for performing hierarchical key management involving an end-to-end file storage replication between different cloud infrastructure regions. The novel hierarchical key management comprises three different keys, a first security key for the source region, a session key, valid only for a session, for the transfer of data between two different regions, and a second security key for the target region. Novel techniques are also described for using different file keys for different files of a file system in each region.

Abstract: A method, apparatus, and product to provide a parser for property graph queries with precise error reporting and auto-completion based on information from property graph schemas. The approach generally comprises analysis of graph queries prior to their execution to identify issues prior to execution. In some embodiments, the approach includes any of: use of a property graph schema to determine whether names in a received property graph query exist within a corresponding property graph; determining whether the property graph query includes a comparison of mismatched data types; providing an autocomplete suggestion feature for assistance in resolving errors or corresponding to a cursor position within a query string; or evaluation of a property graph query to determine whether it would return an empty result. In some embodiments, property graph query analysis is performed using a context aware approach.

Abstract: Plugins that are independently written are executed in a collaborative manner to analysis a log. A plugin executing with respect to a particular node of a hierarchical data structure determines values for a set of keys based on information of the particular node and/or any ancestor nodes, and information stored in a shared repository. The plugin stores the values for the keys as additional information of the particular hierarchical node and/or into the shared repository. The plugin does not access information of non-ancestor nodes when executing with respect to the particular hierarchical node. Each plugin writes into and retrieves from the shared repository using the shared naming convention, thereby sharing information. The sequence of execution of the plugins is not dependent on dependencies amongst the plugins. If a dependent plugin requiring an output from a requisite plugin is first executed, the dependent plugin is flagged as pending and subsequently re-executed.

Abstract: Techniques are described for efficient replication and maintaining snapshot data consistency during file storage replication between file systems in different cloud infrastructure regions. In certain embodiments, snapshot creation and deletion requests that occur during cross-region replications may be temporarily withheld until appropriate times to execute such requests safely, depending on the timing relationship between such requests and cross-region replication cycles.