Sap Se

Mayank Tiwary

Pritish Mishra

Shashank Mohan Jain

Sabyasachi Dhal

Meenakshi Sundaram P

Kirti Sinha

PankajKumar Agrawal

Nishii Bharill

Sunil Kumar

Saurav Mondal

Talwinder Singh

Various examples are directed to systems and methods for managing network traffic between a user query machine and a database query machine. A data center network controller may generate a plurality of query type scores describing database query type types directable from the user query machine to the database query machine. The data center network controller may generate a first database element score for a first database element. The first database element score may be based at least in part on a set of the query type scores associated with database queries to be executed at the first database element and on a first schema weight of a first schema associated with the first database element. The data center network controller may assign a first database element traffic flow to a first network appliance queue based at least in part on the first database element score.

Network virtualization for web application traffic flows

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.).

Performance throttling identification service for autonomous databases as a service

Embodiments allow automated provisioning of a plan upgrade for databases hosted in storage environments. A database is hosted in a shared storage environment according an existing plan, based upon consumption of available system resources (e.g., processing, I/O, memory, disk). An agent periodically issues requests for information relevant to database behavior (e.g., performance metrics, query logs, and/or knob settings). The agent collects the received information (e.g., via a domain socket), performing analysis thereon to predict whether future database activity is expected remain within the existing plan. Such analysis can include but is not limited to compiling statistics, and calculating values such as entropy, information divergence, and/or adjusted settings for database knobs. Based upon this analysis, the agent communicates a recommendation including a plan update and supporting statistics. Embodiments can reduce the effort/cost of the database administrator in having to manually predict future estimated database resource consumption and generate a plan update.

Automated plan upgrade system for backing services

Various examples are directed to systems and methods for tuning a database service in a cloud platform. A tuning service may access a neural network model trained to classify workload points to one of classes. The tuning service may execute the neural network model with a first source workload point as input to return a first class as output, where the first source workload describing a source database. The tuning service may select a target workload for the first source workload point from a plurality of reference workloads. Selecting the target workload may be based at least in part on the first class returned by the neural network model. The tuning service may generate a recommended knob configuration for the source database using the target workload.

Database instance tuning in a cloud platform

A host delegates Just-In-Time (JIT) bytecode compilation to a serverless Web Assembly (WASM) runtime. The WASM runtime receives the bytecode, together with any additional arguments (e.g.: offsets of dependent functions, vtable metadata, virtual machine state). The host may include a parser to provide the additional arguments. In response to receiving the bytecode and arguments, the WASM runtime triggers a thread and loads appropriate WASM modules to compile the bytecode. The resulting assembly instructions are sent back to the host for execution in connection with the (frequently requested) method. Only the bytecode of frequently-accessed methods (as determined at the host) may be delegated for compilation. Delegation of bytecodes for compilation according to embodiments, may conserve a significant percentage of CPU cycles at the host, which can then be used for executing code instead. Based upon analysis of historical compilation activities, particular embodiments may pro-actively implement speculative loading of additional WASM modules.

Delegating bytecode runtime compilation to serverless environment

A computer-implemented method includes constructing a tenancy knowledge graph having a plurality of tenant nodes representing respective tenants in a multitenant computing environment, a plurality of property nodes representing respective properties of the tenants, and a plurality of edges connecting the plurality of tenant nodes and the plurality of property nodes, transforming the plurality of property nodes to corresponding property vectors, performing random walks starting from the plurality of tenant nodes of the tenancy knowledge graph, feeding sequences of nodes traversed by the random walks into a neural network to generate a plurality of tenant vectors corresponding to the plurality of tenant nodes, and clustering the plurality of tenant nodes into one or more tenant clusters based on similarity of the plurality of tenant vectors.

Clustering tenants based on tenancy knowledge graph

Some embodiments may be associated with a cloud-based computing environment. A WASM runtime may execute as serverless functions on an entity (VM or container) dynamically selected based on a data store location (associated with data locality and/or gravity). The WASM runtime may include one or more sandboxes each running a WASM module. A database service may access the data store, and the database service may execute on the same entity as the WASM runtime. In some embodiments, an orchestration layer selects the entity based on a default policy or user-defined custom rules in accordance with exposed attributes (CPU load, memory load, read/write mixture, etc.). According to some embodiments, the serverless functions execute in a multi-tenant fashion. Moreover, the WASM runtime process may use instruction set secure enclaves to secure an access host such that, even if a root is compromised, an attacker cannot access a sandbox memory heap.

Function execution based on data locality and securing integration flows

Technologies are described for the automated determination of materials. For example, material can be automatically identified (e.g., unique material numbers can be determined) based on sensor data and using machine learning models. In some implementations, as part of a first phase, a first set of sensor information describing the material is obtained. Using the first set of sensor information, a material class of the material is determined. As part of a second phase, a second set of sensor information describing the material is obtained. Using the second set of sensor information, the specific material is identified (e.g., a unique material identifier is determined for the material).

Automated determination of material identifiers for materials using machine learning models

Some embodiments may be provided for an enterprise associated with on-premise systems and cloud-based computing applications. A plurality of on-premise collector agents may each execute between an on-premise system and a collector system server. A plurality of cloud collector agents may each execute between a cloud-based computing application and the collector system server. A collector and optimization framework, executing on the collector system sever, may collect connectivity interfaces data from the on-premise and cloud collector agents and store the collected connectivity interfaces data in a local data store. The stored connectivity interfaces data may then be automatically analyzed to generate an optimization recommendation for an enterprise landscape. The collector and optimizer framework may then transmit information about the optimization recommendation to a user via a User Interface ('UI') application.

Connectivity interface optimization recommendation engine for enterprise system

A system and method are disclosed to facilitate a database tuning as a service offered by a cloud platform as a service provider. A throttling detection engine, associated with a database service instance, may periodically determine if an automated database tuning process should be performed. When it is determined that the automated database tuning process should be performed, the throttling detection engine may transmit database performance metrics. A database tuner as a service, coupled to the throttling detection engine, may access aggregated database performance metrics of the database service instance and determine a set of tunable parameters associated with the database service instance. The database tuner as a service may then execute the automated database tuning process to recommend, using an intelligent algorithm, a new set of configurations for the set of tunable parameters to be applied to the database service instance.

Generic autonomous database tuning as a service for managing backing services in cloud

Various examples are directed to systems and methods for network virtualization for web applications. For example, a WAN controller may receive from a backend query processor, a first tenant user query table that describes a first set of query types for a first user. The first set of query types may comprises a first query type having a first computational weight and a second query type having a second computational weight less than the first computational weight. The WAN controller may determine that a first network path to a web application data center is less congested that a second network path to the web application data center. The WAN controller may send a first flow entry to a first network appliance on the first network path instructing the first network appliance on the first network path to send packets associated with queries of the first set of query types to a second network appliance on the first network path.

Network virtualization for web application queries

Some embodiments are associated with systems and methods to provide effective cloud-resource scaling (e.g., for virtual machines and containers). A data center computing system may collect programmable-network real-time traffic statistics associated with enterprise resource planning users. A network and enterprise resource planning load for the enterprise resource planning users may then be predicted (e.g., using a mathematical regression approach) based on an analysis of past programmable-network real-time traffic statistics and current enterprise resource planning user activity. According to some embodiments, the analysis includes time-domain network traffic clustering based on a pre-processing query level categorization. The data center computing system may then dynamically allocate network resources for the enterprise resource planning users based on the predicted network and enterprise resource planning load.

Cloud resource scaling using programmable-network traffic statistics

Some embodiments are associated with multi-tenant software defined data center network traffic management. A data center computing system may calculate a first value for a first traffic flow, associated with a first user, using a dynamic, distributed, and substantially real-time model. The system may calculate a second value for to a second traffic flow, associated with a second user, using the dynamic, distributed, and substantially real-time model. The system may then dynamically allocate network resources to the first and second traffic flows based on the first and second priorities. Some embodiments may establish a plurality of network device queues and perform queue selection for optimization. According to some embodiments, the first user may be categorized as a premium user while the second user is categorized as an enterprise user.

Distributed multi-tenant network real-time model for cloud based enterprise resource planning solutions

A method includes monitoring a user login to a computer network, receiving a media access control (MAC) address associated with a logged-in user, comparing the MAC address to a table that associates the MAC address with one or more queries to which the user has access, wherein the table comprises information associated with other users who are currently logged-in to the computer network, determining whether the user and at least one of the other currently logged-in users have common access to one or more queries, identifying the user and the at least one of the other currently logged-in users determined to have common access to one or more queries as conflicting users, and restricting write or modify queries through the network for the conflicting users.

Maintaining consistency within a federated cloud environment

Some embodiments are associated with multi-tenant software defined data center network traffic management. A data center computing system may assign a first priority to a first traffic flow, associated with a first customer, using a regression technique based on a first response time of a first query associated with the first traffic flow. The system may assign a second priority to a second traffic flow, associated with a second customer, using the regression technique based on a second response time of a second query associated with the second traffic flow. The system may then dynamically allocating network resources to the first and second traffic flows based on the first and second priorities. Some embodiments may establish a plurality of network device queues and calculate demand-supply scores for the first and second traffic flows. The first and second traffic flows may then be sent to queues based on the calculated demand-supply scores.

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Rourkela, India

Mayank Tiwary