Closed Loop Instances in the ZSM Framework: A Development View
Insight into Closed Loop Instances in the ZSM Framework
Telecom networks are getting more complex by the day, and to handle that, we need a mix of automation, intelligence, and adaptability. Enter Zero-touch network and Service Management (ZSM), introduced by ETSI to facilitate end-to-end automation. A crucial part of ZSM is the closed loop (CL) instance, which allows networks and services to keep an eye on themselves, assess their performance, make decisions, and take corrective or optimization actions all on their own.
The image we shared gives a snapshot of the development view of a closed loop instance and its stages within the ZSM framework, zooming in on a single management domain. In this post, we’ll dig into each part, explain what it does, and show how closed loop automation is changing the game in telecom operations.
What is a Closed Loop Instance in ZSM?
A closed loop instance (CLI) is a self-sufficient automation tool that keeps tabs on the network (or service), checks the conditions, decides on actions, and puts them into effect without needing a human to step in.
It's made up of:
Monitoring the different entities involved.
Knowledge-driven analysis.
Smart decision-making.
Execution through orchestration and control.
This means that networks can not only self-optimize, but they can also self-heal and ensure they meet service level agreements (SLAs) in real time.
Key Stages of Closed Loop Instances
The diagram splits the closed loop into several layers and functional areas, all linked through the Domain Integration Fabric.
Let’s take a closer look at each stage:
- Governance & Coordination (Supporting Layer)
At the top, governance and coordination make sure that the closed loop instance is in sync with business policies, SLAs, and compliance requirements.
Main tasks:
Crafting automation policies.
Juggling multiple closed loops across domains.
Making sure loops don’t clash or create bottlenecks.
This stage is vital for keeping policy-driven automation front and center instead of letting things happen randomly.
- Analysis (Knowledge Layer)
This step offers domain or end-to-end (E2E) analysis capabilities.
Core activities:
Gathering and sifting through monitoring data.
Spotting trends, anomalies, and possible SLA breaches.
Employing AI/ML to foresee network issues ahead of time.
By tapping into knowledge systems, this analysis layer turns raw data into actionable insights.
- Decision (Intelligence Layer)
After insights are created, the decision function uses intelligence to figure out the best move.
What it does:
Weighing different action options.
Following business and operational policies.
Selecting actions that strike a good balance between performance and cost.
Here’s where the automation brain of the closed loop gets to work. For instance, if a cell site is congested, the decision engine might suggest load balancing through spectrum reallocation.
- Data Collection (Monitoring Layer)
At the bottom, the data collection function keeps an eye on managed entities.
Important roles:
Gathering telemetry and KPIs from networks, services, and applications.
Offering near-real-time updates to higher layers.
Laying the groundwork for data-driven automation.
Without dependable data collection, the loop can’t function properly.
- Data Services (Knowledge Layer)
This function serves as the knowledge hub for the entire system.
Tasks include:
Storing historical data for trend analysis.
Cross-referencing data from different domains.
Feeding structured input to the analysis stage.
It ensures that the loop isn’t just reactive, but also proactive, thanks to historical insights.
- Orchestration & Control (Execution Layer)
Lastly, the execution stage carries out the actions decided by the decision function.
What it does:
Orchestrating network functions (VNFs, CNFs, PNFs).
Dynamically configuring resources.
Initiating scaling, healing, or service adjustments.
This is where automation really makes a difference for the network state by enforcing corrective steps.
Domain Integration Fabric: The Nervous System
The Domain Integration Fabric ties all the components together. It ensures data interchange, interoperability, and teamwork between the layers.
Functions as the communication backbone for monitoring, analysis, and decision functions.
Offers interfaces for cross-domain orchestration.
Supports standardized APIs to maintain interoperability.
Without this integration layer, the closed loop components would end up isolated and ineffective.
Managed Entities and Actions
Closed loops work on managed entities, which may encompass:
Network functions (RAN, Core, Transport).
Services (VoIP, video streaming, IoT connectivity).
Resources (spectrum, compute, storage).
The loop constantly checks on these entities, collects data, makes decisions, and carries out actions like:
Adjusting resources up or down.
Relocating spectrum.
Changing service settings.
Isolating faulty components.
This keeps networks resilient and adaptable.
Legends: Understanding the Visual Components
The image includes a legend that clarifies the framework:
End-points: Represent management functions.
Capabilities: Represent service offerings.
Producer/Consumer roles: Indicate interactions between components.
Closed loop component boundaries: Help in identifying functional groupings.
These visual cues are crucial for mapping out how closed loop automation is rolled out in ZSM.
Why Closed Loops are Essential in ZSM
Closed loop automation isn’t just a technical feature; it signifies a shift in how we manage networks.
Benefits:
Zero-touch automation: Minimal manual input required.
Enhanced SLA compliance: Ongoing monitoring and quick adjustments.
Operational efficiency: Lower OPEX through automation.
Scalability: Handles vast IoT and 5G services.
Resilience: Automatic recovery and fault handling.
All in all, closed loops are making autonomous networks a reality.
Challenges in Closed Loop Deployment
Even with the perks, rolling out closed loops in ZSM has its hurdles:
Interoperability challenges across different vendors.
Complexities in AI/ML integration.
Data quality issues that can affect decision-making.
Policy clashes among multiple loops.
Security risks in automated control.
Tackling these challenges is key for widespread adoption.
Conclusion
The development view of closed loop instances in ZSM illustrates how networks are moving towards self-managing systems. By merging monitoring, knowledge, analysis, decision-making, and execution, closed loops pave the way for zero-touch automation and ensure networks stay agile, resilient, and smart.
For those in telecom, getting a grip on closed loop principles is essential to help steer the transition to 5G and beyond. Closed loops will not only boost operational efficiency but also lay the groundwork for the autonomous networks of the future.
As we push towards increasingly service-centric, AI-driven telecom ecosystems, the importance of closed loop automation in ZSM is only set to grow.