Autonomous Database Task Scheduling via Dependency Analysis
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Solution Overview
Problem
Current database administration tools are inefficient in managing complex administrative tasks across distributed and nested databases, leading to human errors, increased costs, and reduced throughput due to cumbersome manual processes and inadequate scheduling techniques.
Innovation Solution
A framework for database administration workflow automation that uses source annotations and intelligent scheduling to apply a hierarchy of interdependent administrative tasks, allowing for concurrent execution and rebalancing of tasks across multiple threads and hardware resources, ensuring efficient resource allocation and maintenance of hierarchical state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual administrative tasks are performed in database lifecycle management, then human flexibility and judgment are applied, but human errors increase and productivity decreases
Solution Approach 1:
The system implements autonomous database management systems that automatically perform administrative tasks without human intervention. The framework enables self-service automation where database operations are executed by intelligent agents that monitor system state and autonomously carry out maintenance, provisioning, and optimization tasks, eliminating human error while maintaining high productivity through continuous automated operation
Solution Approach 2:
The patent replaces manual mechanical operations with automated computational systems. The framework substitutes human administrators with software agents that execute administrative tasks through programmed logic and intelligent algorithms, transforming manual processes into automated digital operations that eliminate human error and increase throughput through parallel execution capabilities
2Reliability
If administrative tasks are executed in a serial manner, then task dependencies are maintained, but throughput decreases
Solution Approach 1:
The framework implements dynamic scheduling that adapts task execution based on real-time system state and dependency analysis. The intelligent scheduler continuously evaluates task dependencies and system resources, dynamically adjusting execution order and parallelization degree to maintain reliability while maximizing throughput through optimized concurrent execution when dependencies permit
Solution Approach 2:
The system segments administrative tasks into independent executable units with clearly defined dependencies. By breaking down complex administrative operations into smaller, modular task components, the framework enables parallel execution of independent segments while maintaining proper sequencing through dependency graphs, thereby increasing throughput without compromising task dependency integrity
3Quantity of substance
If externally-influenced long running actions are executed, then comprehensive administrative coverage is achieved, but execution units are blocked causing task starvation and priority inversion
Solution Approach 1:
The framework implements periodic execution and monitoring of administrative tasks with built-in timeout and priority preemption mechanisms. Long-running actions are executed in periodic intervals with scheduled checkpoints that allow the scheduler to intervene, pause or terminate low-priority tasks, and allocate resources to high-priority operations, ensuring comprehensive administrative coverage while preventing task starvation and priority inversion
Solution Approach 2:
The system employs feedback mechanisms where the intelligent scheduler continuously monitors task execution progress and system state. When long-running actions are detected, the scheduler receives feedback about their status and dynamically adjusts resource allocation, preempting blocked execution units for higher-priority tasks while maintaining overall administrative task coverage through adaptive resource management
4Adaptability or versatility
If databases are elastically replicated and moved across multiple computers and containers, then availability and scalability are improved, but device complexity increases
Solution Approach 1:
The framework introduces an intermediary management layer that abstracts the complexity of elastic replication and database movement. This intermediary system handles the coordination of database replicas across multiple computers and containers, managing the complex configuration and state synchronization automatically, thereby enabling high adaptability while shielding users from the underlying system complexity
Data Source
AI summary
Herein is database administration workflow automation with source annotations and intelligent scheduling techniques for applying a hierarchy of interdependent administrative tasks to distributed and/or nested databases. In an embodiment, a source language compiler analyzes annotations to identify a hierarchy of administrative tasks that administers pluggable databases in container databases. From the annotations, a runtime codebase is generated that implements and invokes the administrative task hierarchy. At runtime, a container database management system (CDBMS) autonomously identifies and instantiates the administrative tasks, including identifying a dependency of a first administrative task on a second administrative task and a lack of dependency of a third administrative task on the second administrative task. The CDBMS contains an intelligent scheduler that concurrently executes the second and third administrative tasks and defers execution of the first administrative task until after completion of the second administrative task. For example, the administrative tasks may be distributed to different databases for parallelism or instead sequenced for phased execution based on dependencies between administrative tasks or phases.