Policy State Management Using Change Request Event Filtering
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Solution Overview
Problem
Existing event-driven architecture systems struggle with efficiently managing network assurance policies in response to change requests, leading to deferred automated actions and inefficiencies in network automation.
Innovation Solution
A system and method for change request (CR) assisted policy state management, which includes a correlation and policy engine (CPE) that processes network assurance policies, filters events based on active CRs, and manages policy states using a scheduled CR table to automate actions during defined time windows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated actions are deferred in response to change requests, then network stability is maintained during transitions, but productivity and automation efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-calculating and scheduling automated actions before change requests are processed. The action scheduler creates a timeline of actions to be executed, and the action deferrer prepares action queues in advance. This allows the system to maintain stability during transitions while ensuring automated actions are executed efficiently without unnecessary deferrals.
Solution Approach 2:
The system dynamically adjusts its behavior based on the current state of change requests and network conditions. The action deferrer and scheduler continuously monitor the environment and adapt their operations accordingly, allowing automated actions to be executed or deferred based on real-time conditions rather than following a rigid schedule, thus optimizing both stability and efficiency.
2Device complexity
If policy state management is manual, then system complexity is reduced, but productivity and operational efficiency deteriorate
Solution Approach 1:
The system implements self-service by enabling automated policy state management where the action scheduler and action deferrer automatically monitor, schedule, and execute actions based on change requests. This eliminates the need for manual intervention in policy state management, reducing operational complexity while significantly improving productivity and efficiency through automated workflows.
Solution Approach 2:
The system employs feedback mechanisms where the action scheduler continuously monitors the status of change requests and automated actions, adjusting schedules and executions based on real-time feedback. This closed-loop approach allows the system to self-regulate and optimize its operations without manual oversight, improving efficiency while maintaining manageable complexity through automated feedback-driven control.
Data Source
AI summary
A method executed by processing circuitry, includes receiving, from a user interface (UI), one or more network assurance policies configured to be used in a CPE; receiving, periodically from a change request (CR) adaptor service, one or more active change requests (CRs) configured to be implemented; storing the one or more active CRs to an active scheduled CR table included in a network assurance policy database (DB); filtering, by an ingestion service of the CPE that screens incoming event streams based on the one or more network assurance policies, further the incoming event streams based on the active CRs within the active scheduled CR table; and discarding each incoming event from the incoming event streams that matches a dependent network element (NE) or a dependent network service (NS) that is within a CR time window where the CR time window is a period where a CR is being implemented.