Abstract: A system may include a nested probabilistic data structure that contains nested tables, each table having electronic records associated with different hash functions. A data analytics engine may receive information about a detected action, including action characteristics. In response to the detected action, insertions are executed into the nested probabilistic data structure at multiple levels based on the action characteristics (each insertion incrementing an entry in the data structure selected using the hash functions). The engine then continues to monitor for detected actions. Upon receipt of a query associated with the monitored action (including a query type), a key and level of nesting associated with the query is determined based on the query type. The hash functions are executed on the determined key to retrieve a value from each electronic record in an appropriate nested table, and a minimum of the retrieved values is output in response to the query.

Abstract: Embodiments may facilitate data analytics for a cloud computing environment. A plurality of system tables may store system metadata that associates a first materialized view with a first view and a first table. A transaction compute unit may process an update request relevant to the first view. A query parser may capture the update request from the transaction compute unit and detect which system tables are associated with the update request relevant to the first view. Responsive to the request, the query processor arranges for first table data to be replicated and transmit a materialized view request to a materialized view compute unit. The materialized view compute unit may be created as a Materialized View-as-a-Service (“MVaaS”) independent of the transaction compute unit. The materialized view compute unit may receive the materialized view request, refresh the first materialized view, compute the first materialized view, and store a result of the computation.

Abstract: Embodiments may facilitate data analytics for a cloud computing environment. A plurality of system tables may store system metadata that associates a first materialized view with a first view and a first table. A transaction compute unit may process an update request relevant to the first view. A query parser may capture the update request from the transaction compute unit and detect which system tables are associated with the update request relevant to the first view. Responsive to the request, the query processor arranges for first table data to be replicated and transmit a materialized view request to a materialized view compute unit. The materialized view compute unit may be created as a Materialized View-as-a-Service (“MVaaS”) independent of the transaction compute unit. The materialized view compute unit may receive the materialized view request, refresh the first materialized view, compute the first materialized view, and store a result of the computation.

Abstract: Some embodiments may be associated with a cloud-based actor framework. A dispatcher platform may determine that a first tenant actor is to be created for a first tenant in connection with a workload associated with a plurality of tenant identifiers. The first tenant may be, for example, associated with a first tenant identifier. The dispatch platform may then select a first thread for the first tenant actor from a pool of available threads and spin a first web assembly module such that execution of the first web assembly module is associated with a first web assembly browser sandbox. The dispatcher platform can then securely create the first tenant actor within the first web assembly browser sandbox to execute the workflow for the first tenant identifier. Similarly, a second web assembly browser sandbox may execute a second tenant actor for a second tenant identifier.

Abstract: According to some embodiments, a user vector generator may access information about a user (e.g., a software deployment developer or operator) in a user data store that contains electronic records each associated with different user. Each record may include, for example, a user identifier and user characteristics. Based on the user characteristics, the system may automatically generate a user vector indicating a computing environment skillset level for that user (e.g., beginner, intermediate, or expert). A machine learning privilege assignment platform may receive an indication of the user vector for the user and, based on the user vector and a machine learning algorithm, generate a privilege decision for that user (e.g., when the user attempts to update the system). An indication of the privilege decision may be output, according to some embodiments, to an SMT solver to review the privilege decision before granting the user access to the computing environment.

Abstract: A system and method are disclosed associated with a cloud platform as a service provider. A monitoring agent, associated with a database service instance running on a client database virtual machine, periodically executes a performance throttling detection engine. The performance throttling detection engine gathers database statistics based on metrics and features of the database service using a rule-based approach. When it is determined that a pre-determined condition is met, the system may transmit the gathered database statistics to an external application. For example, the external application might comprise a database tuning service and the pre-determined condition may be associated with a decision that the database service may potentially need to tune knob configuration parameters (associated with memory knobs, background writer knobs, asynchronous knobs, etc.).

Abstract: A first forwarding VM may execute in a first availability zone and have a first IP address. Similarly, a second forwarding VM may execute in a second availability zone and have a second IP address. The first and second IP addresses may be recorded with a cloud DNS web service of a cloud provider such that both receive requests from applications directed to a particular DNS name acting as a single endpoint. A service cluster may include a master VM node and a standby VM node. An IPtable in each forwarding VM may forward a request having a port value to a cluster port value associated with the master VM node. Upon a failure of the master VM node, the current standby VM node may be promoted to execute in master mode and the IPtables may be updated to now forward requests having the port value to a cluster port value associated with the newly promoted master VM node (which was previously the standby VM node).

Abstract: A system and method are disclosed associated with a multi-tenant cloud computing environment. The system may receive information about a serverless function workload (e.g., a NodeJS, Java function or ABAP workload) to be launched in the cloud computing environment. A tenant associated with the serverless function workload to be launched may be identified and, based at least in part on the identified tenant, an objective function (e.g., throughput, latency, cost, etc.) for the serverless function workload to be launched may be identified. A recommendation service platform may then iteratively configure tuning parameters of the cloud computing environment using Bayesian optimization (e.g., to reach a global optimum using a Gaussian process) and the determined objective function. The system may then arrange for the serverless function workload to be executed in the cloud computing environment in accordance with the configured tuning parameters.

Abstract: Some embodiments may be associated with a cloud computing environment. A computer processor of a data analysis server may receive a stream of big data associated with the cloud computing environment. The data analysis server may then create a probabilistic data structure (e.g., a sketch) representing the received stream of big data and store the created probabilistic data structure into the probabilistic data structure repository. A request for data analytics is then received from a remote client browser. Responsive to the received request, the data analysis server may retrieve the probabilistic data structure from the probabilistic data structure repository and push a WebAssembly (“Wasm”) module and the retrieved probabilistic data structure to the client browser. According to some embodiments, the data analysis server may then receive new updates to the stream of big data and send delta changes to the client browser via a WebSocket protocol.

Abstract: A system may include a historical managed software system data store that contains electronic records associated with controllers and deployed workloads (each electronic record may include time series data representing performance metrics). An entropy calculation system, coupled to the historical managed software system data store, may calculate at least one historical entropy value based on information in the historical managed software system data store. A detection engine, coupled to a monitored system currently executing a deployed workload in the cloud computing environment, may collect time series data representing current performance metrics associated with the monitored system. The detection engine may then calculate a current monitored entropy value (based on the collected time series data representing current performance metrics) and (iii) compare the current monitored entropy value with a threshold value (based on the historical entropy value).

Abstract: A first forwarding VM may execute in a first availability zone and have a first IP address. Similarly, a second forwarding VM may execute in a second availability zone and have a second IP address. The first and second IP addresses may be recorded with a cloud DNS web service of a cloud provider such that both receive requests from applications directed to a particular DNS name acting as a single endpoint. A service cluster may include a master VM node and a standby VM node. An IPtable in each forwarding VM may forward a request having a port value to a cluster port value associated with the master VM node. Upon a failure of the master VM node, the current standby VM node may be promoted to execute in master mode and the IPtables may be updated to now forward requests having the port value to a cluster port value associated with the newly promoted master VM node (which was previously the standby VM node).

Abstract: A system may include a virtual machine, of a cloud computing environment, that executes a target application workload to be intercepted. A software defined anomaly detection engine (that is separate from the target application workload and that is also executing in the virtual machine) may intercept the target application workload. A computer processor of the software defined anomaly detection engine may intercept network traffic that is external to the virtual machine and associated with the target application workload. The software defined anomaly detection engine may then automatically execute an anomaly detection algorithm in substantially real time on the intercepted network traffic to generate an intercept result. An anomaly detection alert signal may be transmitted based on a comparison of the intercept result and an anomaly threshold value.

Abstract: Methods and systems may be associated with a cloud computing environment. A serverless function orchestrator may execute a socket activation for a VM to pre-provision a TCP socket (e.g., setting up virtual interfaces and creating socket structures) before the VM hosts any serverless function associated with the pre-provisioned TCP socket. After this socket activation, the orchestrator may receive a request for a first serverless function and, responsive to the received request, start the first serverless function on the VM using the pre-provisioned TCP socket. After the activation and prior to starting the first serverless function, the system may queue packets received in connection with the pre-provisioned TCP socket. In some embodiments, multiple TCP sockets, each associated with a VM, may activated before any serverless functions are hosted and the first serverless function is started on a VM selected based on information in a serverless function experience data store.

Abstract: A system may include a nested probabilistic data structure that contains nested tables, each table having electronic records associated with different hash functions. A data analytics engine may receive information about a detected action, including action characteristics. In response to the detected action, insertions are executed into the nested probabilistic data structure at multiple levels based on the action characteristics (each insertion incrementing an entry in the data structure selected using the hash functions). The engine then continues to monitor for detected actions. Upon receipt of a query associated with the monitored action (including a query type), a key and level of nesting associated with the query is determined based on the query type. The hash functions are executed on the determined key to retrieve a value from each electronic record in an appropriate nested table, and a minimum of the retrieved values is output in response to the query.

Abstract: A system may include a historical managed software system data store that contains electronic records associated with controllers and deployed workloads (each electronic record may include time series data representing performance metrics). An entropy calculation system, coupled to the historical managed software system data store, may calculate at least one historical entropy value based on information in the historical managed software system data store. A detection engine, coupled to a monitored system currently executing a deployed workload in the cloud computing environment, may collect time series data representing current performance metrics associated with the monitored system. The detection engine may then calculate a current monitored entropy value (based on the collected time series data representing current performance metrics) and (iii) compare the current monitored entropy value with a threshold value (based on the historical entropy value).

Abstract: A system may include a historical time series data store that contains electronic records associated with Software-as-a-Service (“SaaS”) applications in a multi-tenant cloud computing environment (including time series data representing execution of the SaaS applications). A monitoring platform may retrieve time series data for the monitored SaaS application from the historical time series data store and create tenant vector representations associated with the retrieved time series data. The monitoring platform may then provide the retrieved time series data and tenant vector representations together as final input vectors to an autoencoder to produce an output including at least one of a tenant-specific loss reconstruction and tenant-specific thresholds for the monitored SaaS application. The monitoring platform may utilize the output of the autoencoder to automatically detect an anomaly associated with the monitored SaaS application.

Abstract: A system and method are disclosed associated with a cloud computing environment. The system includes a tracing tool, coupled to a controller in the cloud computing environment, that captures sequences of events associated with the controller and a deployed workload. A detection engine may detect important event patterns in the sequences captured by the tracing tool using a PrefixSpan algorithm in connection with a specific controller action associated with the deployed workload. A neural network, trained with the detected important event patterns, may predict which important event patterns caused the controller to perform the specific action associated with the deployed workload.

Abstract: A Relational Database Management System (“RDBMS”) as a service cluster may including a master RDBMS Virtual Machine (“VM”) node associated with an Internet Protocol (“IP”) address and a standby RDBMS VM node associated with an IP address. The RDBMS as a service (e.g., PostgreSQL as a service) may also include n controller VM nodes each associated with an IP address. An internal load balancer may receive requests from cloud applications and include a frontend IP address different than the RDBMS IP as a service addresses and a backend pool including indications of the master RDBMS VM node and the standby RDBMS VM node. A Hyper-Text Transfer Protocol (“HTTP”) custom probe may transmit requests for the health of the master RDBMS VM node and the standby RDBMS VM node via the associated IP addresses, and responses to the requests may be used in connection with a failover operation.

Abstract: Some embodiments may be associated with a cloud computing environment. A serverless runtime workload may execute an eBPF program via a kprobe which gets invoked when function code is executed as a Linux process. The system may determine, by the kprobe function associated with an identifier, that an orchestrator is evicting the serverless runtime workload. Responsive to the determination, a userspace program may be invoked via the eBPF in tandem with the kprobe acting as an interception mechanism. The system may then capture the current workload process state data associated with the serverless runtime workload based on the identifier. A clustered memory-based storage component may store the captured current workload process state data in association with the identifier. A subsequent serverless runtime workload may determine that the orchestrator is restoring the serverless runtime workload.

Abstract: According to some embodiments, a user vector generator may access information about a user (e.g., a software deployment developer or operator) in a user data store that contains electronic records each associated with different user. Each record may include, for example, a user identifier and user characteristics. Based on the user characteristics, the system may automatically generate a user vector indicating a computing environment skillset level for that user (e.g., beginner, intermediate, or expert). A machine learning privilege assignment platform may receive an indication of the user vector for the user and, based on the user vector and a machine learning algorithm, generate a privilege decision for that user (e.g., when the user attempts to update the system). An indication of the privilege decision may be output, according to some embodiments, to an SMT solver to review the privilege decision before granting the user access to the computing environment.