AI Planning Algorithm for Automated Provisioning Workflow Generation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current resource provisioning systems require manual, time-consuming, and error-prone processes to determine the correct order and parameters of provisioning operations to achieve a desired configuration state, lacking automation and efficient workflow generation.

Innovation Solution

The implementation of artificial intelligence planning techniques to identify and order provisioning operations by mapping existing operations to planning actions with defined preconditions and effects, using a planning algorithm to generate a valid workflow that transforms the system from an initial to a desired configuration state.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual provisioning workflow definition is used, then flexibility and control are maintained, but time consumption and error rate increase significantly

Engineering Contradiction:

Improveworkflow accuracyVSAvoidworkflow definition time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs self-service by automatically generating provisioning workflows through AI planning algorithms. The planner autonomously determines the sequence of provisioning operations, prerequisites, and effects without requiring manual intervention, thereby eliminating human errors and significantly reducing the time required for workflow definition while maintaining high accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of defining workflows step-by-step is replaced by an automated intelligent system. The AI planning algorithm substitutes the manual mechanical workflow definition process with automated computational reasoning that analyzes provisioning operations, identifies dependencies, and generates optimized execution sequences automatically

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If provisioning operations are automated, then efficiency increases, but complexity of determining correct operation order increases

Engineering Contradiction:

Improveprovisioning speedVSAvoidworkflow generation complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

An AI planning algorithm serves as an intermediary between the provisioning operations and the execution system. This intermediary automatically analyzes the complex relationships between operations, identifies prerequisites and effects, and generates the correct execution order, thereby managing the complexity internally while presenting a simple automated solution to the user

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system transforms the complex workflow generation problem into a structured planning problem by changing the parameters representation. It defines standardized parameters for provisioning operations including prerequisites, effects, and execution conditions, allowing the AI planner to systematically process and resolve the ordering complexity through algorithmic reasoning

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If comprehensive provisioning operations are defined, then coverage of configuration tasks increases, but difficulty of determining execution order increases

Engineering Contradiction:

Improveconfiguration coverageVSAvoidoperation ordering difficulty

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The comprehensive provisioning operations are segmented into discrete, atomic steps with clearly defined prerequisites and effects. Each operation is broken down into manageable units that the AI planner can independently analyze and sequence, making it easier to determine the correct execution order even when covering extensive configuration tasks across multiple resources

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements feedback mechanisms where the AI planning algorithm continuously evaluates the generated workflow sequence against the defined prerequisites and effects of each provisioning operation. This feedback loop ensures that the determined execution order satisfies all dependencies and constraints, automatically adjusting the sequence to resolve ordering difficulties while maintaining comprehensive configuration coverage

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS8250355B2Method, system, and product for identifying provisioning operations via planning methods

Publication Date: 2012.08.21 SERVICENOW INC

AI summary

A method, system, and computer program product are disclosed for automatically determining a valid ordering of provisioning operations, and their needed parameters, so that a provisioning system can configure a desired resource state. This is accomplished by formally describing the pre-conditions and effects of provisioning operations, the current state of managed resources and the desired final state. A planning algorithm is then used to determine the provisioning operations, a valid ordering and appropriate parameters to bring the system from the current state to the desired state.