Sap Se

Pritish Mishra

Mayank Tiwary

Shashank Mohan Jain

Meenakshi Sundaram P

Kirti Sinha

Saurav Mondal

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

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

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

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

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

A system and method is disclosed for recovering from any form of disaster-based deletion or loss of resource for a provisioner in a PaaS offering deployed on a cloud. Major architectural changes in the typical provisioner framework are provided along with algorithms to recover from any kind of disaster. Gap-points in existing recovery processes for a provisioner are solved with the algorithms described herein. In addition, disaster recovery for an entire tenant in a provisioner framework may be provided in accordance with some embodiments. In other words, a solution is described in which a user/customer is able to recover a previous state with zero-data loss and zero-re-configuration cost in any scenario of resource deletion.

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Bengaluru, India

Pritish Mishra