Self-service Control Plane for Cloud Data Storage Management
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Solution Overview
Problem
Current cloud computing environments face challenges in dynamically managing and scaling data storage repositories, requiring manual and tedious processes that are prone to errors and require expertise, lacking easy adjustment or optimization of database instances.
Innovation Solution
A separate control plane is introduced, enabling users to manage data environments through Web services, allowing self-service functionality and acting as a virtual database administrator, with APIs for users to request actions and workflows to execute tasks automatically, such as provisioning, scaling, and optimizing data storage instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual procedures are used for provisioning and scaling data storage, then database administrators can manage data repositories, but the process becomes tedious and time-consuming
Solution Approach 1:
The system enables self-service provisioning and scaling of data storage through automated workflows. Users can submit requests through web interfaces or APIs, and the system automatically executes provisioning tasks without requiring manual DBA intervention for routine operations, thereby reducing both time loss and operational complexity
Solution Approach 2:
An automated workflow engine acts as an intermediary between user requests and database provisioning systems. This mediator translates high-level user requirements into specific provisioning commands, automating the coordination between multiple systems and eliminating manual intervention steps
2Reliability
If expert database administrators are required to configure and optimize data repositories, then proper configuration is achieved, but the process requires specialized expertise and increases operational complexity
Solution Approach 1:
The system incorporates automated validation and configuration generation that performs previously expert-only functions. The workflow engine automatically validates configuration parameters against best practices and system requirements, eliminating the need for users to possess deep DBA expertise while maintaining configuration reliability
Solution Approach 2:
The system pre-configures validated parameter sets and optimization rules before they are needed. Database configuration templates and optimization strategies are prepared in advance through automated workflows, so when provisioning is requested, reliable configurations are already available without requiring real-time expert judgment
3Adaptability or versatility
If dynamic adjustment of database parameters is enabled, then flexibility and adaptability improve, but the system complexity increases
Solution Approach 1:
The workflow engine serves as an intermediary layer that manages parameter adjustment complexity. Users can request dynamic parameter changes through simple interfaces, and the workflow engine handles the complex coordination of parameter validation, configuration updates, and system restarts, thereby providing adaptability without exposing system complexity to users
Solution Approach 2:
The system implements dynamic configuration management where database parameters can be adjusted at runtime through automated workflows. The system dynamically evaluates parameter change requests, applies validated configurations, and automatically manages the transition states, enabling flexibility while maintaining system stability through automated control
Data Source
AI summary
The values of various operating and/or configuration parameters of a data environment are managed using a set of self-service Web services and interfaces of a separate control environment. A customer can submit a Web services call into an externally-facing application programming interface (API) or other such externally-facing interface of the control environment. The API receiving the call, as well as information extracted from the call, can be used to determine appropriate adjustments to be performed in the data environment. A workflow can be instantiated that includes tasks used to validate and/or apply the adjustments to the target resources, such as databases, data instances, data stores, instance classes, etc. Various real-time functions such as monitoring and auto-scaling also can be performed via the control plane.