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Out-of-scope
Here’s a detailed study on transitioning from Kafka to Amazon S3 and MinIO for telecom Configuration Management (CM), referencing the provided PDFs: “OA TIC CM Distributed Configuration Management Application” and “O-RAN R1 CM Stack Study”.
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Detailed Study on Transitioning from Kafka to Amazon S3 and MinIO in Telecom Configuration Management
1. Introduction
In modern telecom networks, Configuration Management (CM) is critical for ensuring network elements (NEs) are correctly configured, updated, and maintained. Traditionally, Kafka is used in telecom CM systems for real-time streaming of CM data, but its limitations in handling large volumes of bulk data and non-real-time operations have led to the adoption of scalable object storage services like Amazon S3 and MinIO. Both S3 and MinIO offer scalable, cost-effective solutions for storing and retrieving bulk CM data asynchronously, making them ideal for handling large-scale data jobs.
This study provides a detailed analysis of:
The current use of Kafka in telecom CM systems.
The transition to Amazon S3 and MinIO as S3-compatible alternatives.
How MinIO, as an on-premise solution, can serve as an alternative to cloud-based Amazon S3.
The benefits and architectural changes involved in this transition.
The study will reference both the OA TIC CM Distributed Configuration Management Application and O-RAN R1 CM Stack Study PDFs.
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2. Overview of Kafka in Telecom Configuration Management
2.1. Kafka's Role in Telecom CM
Kafka is traditionally used to handle real-time data streaming for CM tasks, including:
Streaming data job results: When a CM data job is executed (e.g., reading or updating configurations across network elements), Kafka streams the results in real-time to consumers like rApps (Radio Applications)【47†source】.
Low-latency message delivery: Kafka provides low-latency, high-throughput message streaming, ideal for scenarios where CM data must be delivered to consumers almost instantaneously【47†source】.
Kafka is excellent for real-time applications, but it has limitations when dealing with bulk data storage:
Storage limitations: Kafka is designed for transient data rather than long-term storage of large CM datasets【47†source】.
Complexity and cost: Managing Kafka clusters for persistent data storage can be complex and costly.
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3. Understanding S3 in Telecom Configuration Management
Amazon S3 (Simple Storage Service) is a widely adopted object storage solution known for its scalability, durability, and availability. S3 is ideal for managing large volumes of bulk data that do not require real-time access.
3.1. Key Features of Amazon S3 in Telecom CM
Scalability: S3 scales automatically to store and retrieve any amount of data, making it ideal for storing configuration data from thousands of network elements【47†source】.
Durability: S3 guarantees 99.999999999% (11 9's) durability, making it suitable for storing critical CM data across multiple availability zones【47†source】.
Data Lifecycle Management: S3 allows operators to configure lifecycle policies that automatically delete old data or move it to lower-cost storage classes (e.g., S3 Glacier) based on retention needs【47†source】.
3.2. S3 in Telecom CM Systems
In telecom CM systems, S3 serves as a reliable solution for storing asynchronous data job results. As outlined in the OA TIC CM Distributed Configuration Management Application PDF, S3 can store the results of data jobs such as bulk reads or bulk configuration updates . For instance:
When a data job is executed (e.g., querying the configuration of thousands of network elements), the results can be stored in an S3 bucket for later retrieval .
CM systems can also use S3’s event notifications to trigger alerts or further processing once data is available, allowing for more efficient data workflows .
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4. Transitioning from Kafka to S3-Compatible Storage (Amazon S3 and MinIO)
4.1. Limitations of Kafka in Bulk Data Handling
While Kafka excels in real-time streaming, it is not well-suited for managing large volumes of CM data that need to be stored and accessed over time. Its limitations include:
Inadequate for bulk data: Kafka is not designed for long-term storage of bulk CM data【47†source】.
Infrastructure complexity: Maintaining Kafka clusters for large-scale, persistent storage is costly and operationally complex【47†source】.
4.2. Why Transition to S3-Compatible Storage?
Amazon S3 and MinIO offer several advantages over Kafka for bulk, asynchronous data handling:
Bulk Data Storage: S3-compatible storage solutions provide highly scalable storage, making them ideal for storing the results of large CM data jobs .
Cost Efficiency: S3’s pay-as-you-go model and MinIO’s open-source deployment allow telecom operators to reduce costs significantly compared to maintaining Kafka clusters for non-real-time data【47†source】 .
Asynchronous Data Handling: CM data jobs can be processed asynchronously, with results stored in S3 or MinIO for later retrieval by rApps .
Data Retention and Lifecycle Management: S3’s lifecycle management tools automatically archive or delete old CM data, ensuring efficient data management without manual intervention .
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5. MinIO: A S3-Compatible On-Premise Solution
MinIO is a high-performance, S3-compatible object storage solution that can be deployed on-premises. It allows telecom operators to retain full control over their data while benefiting from the simplicity and scalability of S3.
5.1. MinIO in Telecom CM Systems
MinIO is especially useful for telecom operators that prefer to keep sensitive CM data within their own infrastructure. It offers:
Full control over data: MinIO allows operators to control where and how their data is stored, ensuring compliance with local regulations .
Performance and Scalability: MinIO’s distributed architecture supports horizontal scaling, allowing it to handle the large data volumes typically generated by telecom CM operations .
5.2. Deployment Scenarios for MinIO
MinIO can be deployed as part of:
Hybrid cloud architectures: Telecom operators can use MinIO for on-premise data storage while integrating with cloud services as needed.
Edge computing environments: MinIO’s lightweight architecture makes it ideal for edge computing, enabling telecom operators to store and process CM data close to the network’s edge for lower latency and faster access .
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6. Architectural Diagrams: Transition from Kafka to S3-Compatible Storage
6.1. Current System with Kafka (Before Transition)
In the current system, Kafka is used for real-time streaming of CM data. Data jobs (such as retrieving configuration data from network elements) are streamed through Kafka topics for immediate consumption by rApps.
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Figure 1: Kafka-Based Configuration Management
Kafka handles real-time streaming of data job results from the NCMP (Network Configuration Management Proxy) to rApps【47†source】.
Network elements communicate with the NCMP, which publishes the results through Kafka topics for consumption by rApps.
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6.2. Transition to S3-Compatible Storage (After Transition)
In the new architecture, Amazon S3 or MinIO replaces Kafka for bulk data storage. Asynchronous data job results are stored in S3 or MinIO buckets, where they can be accessed by rApps as needed.
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Figure 2: Transition to S3-Compatible Storage (Amazon S3 / MinIO)
NCMP processes data jobs and stores the results in S3 or MinIO buckets.
rApps access the stored results asynchronously, without the need for real-time streaming .
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7. Use Cases and Benefits of Transitioning to S3-Compatible Storage
7.1. Bulk Configuration Data Jobs
In telecom CM systems, bulk read and write operations (e.g., updating the configuration of thousands of network elements) generate large volumes of data that need to be stored for future reference. With S3-compatible storage:
Async data job results can be stored in S3 or MinIO buckets, allowing rApps to retrieve the results at their convenience .
This approach simplifies the management of large-scale CM operations, making it easier to handle large data volumes without the need for immediate processing .
7.2. Cost Efficiency and Scalability
Amazon S3: Operators only pay for the storage they use, making it cost-effective for storing large amounts of CM data. It also scales automatically to handle increasing data volumes【47†source】.
MinIO: Being an open-source solution, MinIO can be deployed on existing infrastructure, allowing operators to maintain full control over their data and reduce costs associated with cloud services【47†source】.
7.3. Data Lifecycle and Retention Policies
S3 lifecycle policies allow operators to automatically delete or archive old CM data after a set retention period. This helps reduce storage costs and ensures that only relevant data is kept in active storage【52:0†source】【52:4†source】【52:5†source】.
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8. Conclusion
Transitioning from Kafka to Amazon S3 or MinIO for telecom Configuration Management provides several key benefits for managing bulk data. While Kafka remains a powerful tool for real-time streaming, S3-compatible storage solutions offer superior scalability, cost efficiency, and flexibility for handling asynchronous data workflows and bulk data operations.
Amazon S3 is ideal for cloud-based deployments, offering automatic scaling, integrated lifecycle management, and seamless integration with other AWS services. On the other hand, MinIO provides a cost-effective, on-premise alternative for telecom operators that require full control over their data storage and infrastructure.
References
OA TIC CM Distributed Configuration Management Application PDF
O-RAN R1 CM Stack Study PDF
Amazon S3 Documentation: https://docs.aws.amazon.com/s3
MinIO Documentation: https://docs.min.io/
Kafka Documentation: https://kafka.apache.org/documentation/
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This detailed study provides telecom operators with a comprehensive view of how to transition from Kafka to S3-compatible storage solutions like Amazon S3 and MinIO, while also referencing the provided PDFs for further technical details