Backward Chaining Rule Engine for Container Cluster Configuration
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Solution Overview
Problem
In containerized computing clusters, manually encoding business logic using procedural programming languages is tedious, error-prone, and inefficient for collaboration, especially when encoding rules for managing stateful applications.
Innovation Solution
Implementing a rule engine with backward chaining functionality to infer missing configuration information and trigger corrective actions automatically, utilizing a rule engine that evaluates rules against facts to determine criteria for cluster configurations and perform necessary actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual encoding of business logic using procedural programming languages is used, then implementation is straightforward, but it is tedious, error-prone, and inefficient for collaboration
Solution Approach 1:
The patent introduces a rule engine as an intermediary layer between the container orchestration system and business logic requirements. This rule engine uses Domain Specific Language (DSL) to encode rules, serving as a mediator that translates high-level business requirements into executable logic, thereby improving collaboration efficiency while maintaining ease of implementation through standardized rule templates
Solution Approach 2:
The system changes the parameter of rule encoding from procedural programming languages to Domain Specific Language (DSL). This parameter change enables more efficient collaboration by allowing rules to be defined in a domain-specific syntax that is more intuitive and easier to collaborate on, while the rule engine handles the complexity of interpretation and execution
2Device complexity
If traditional rule evaluation methods are used, then implementation is simple, but they require significant computational resources and time for forward chaining through all rules
Solution Approach 1:
The patent inverts the traditional forward chaining approach by implementing backward chaining. Instead of starting from rules and evaluating them against facts to find conclusions, the system starts from a goal state and works backward to determine what facts and rules are needed to achieve it. This inversion dramatically reduces computational resources by only evaluating relevant rules rather than all rules in the system
Solution Approach 2:
The system extracts only the necessary rules and facts required to achieve the goal state, rather than processing the entire rule set. By taking out only the relevant subset of rules that contribute to the goal, the backward chaining approach minimizes computational resource usage while maintaining implementation simplicity
3Measurement precision
If comprehensive rule evaluation is performed to ensure accuracy, then inference precision is high, but processing time increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing rules and facts into structures that facilitate efficient backward chaining. Rules are pre-compiled and indexed, and the system pre-identifies relevant rule subsets based on goal characteristics. This preliminary preparation enables accurate inference without the time penalty of comprehensive real-time evaluation
Data Source
AI summary
A method includes determining, by a processing device, a criterion associated with a configuration of a containerized computing cluster, wherein the containerized computing cluster comprises a plurality of virtualized computing environments running on one or more host computer systems; evaluating a rule against a fact, wherein the rule specifies a condition including the criterion and an action to perform if the condition of the rule is satisfied; and responsive to determining that the condition specified by the rule matches the asserted fact, performing the action specified by the rule, wherein the action comprises a notification regarding the configuration of the containerized computing cluster.