Cold Hot Seperation in 5G RAN
Hot Data
Hot data refers to frequently accessed and low-latency-needed real-time data, typically involving quick read and write operations. It must be stored on high-performance storage devices (like SSDs or memory) to ensure rapid response and high throughput. In the context of 5G RAN, hot data is crucial because real-time performance directly impacts network quality.
Examples of Hot Data in 5G RAN:
- Real-time network monitoring and alarm data: This includes metrics for monitoring 5G network performance, such as traffic, latency, signal strength, and device connection status. This type of data requires frequent access so operators can make timely adjustments or trigger alarms.
- Real-time RAN configuration parameters: For example, configuration parameters in 5G base stations (gNB) may need real-time updates and reads to adapt to changing network traffic or user needs.
- Real-time performance indicators (KPIs): Indicators such as network throughput and packet loss rate require frequent querying to enable real-time analysis, optimization, or decision-making.
Storage Characteristics of Hot Data in 5G RAN:
- Stored on high-performance devices like SSDs or DRAM.
- Short data lifecycle with frequent updates.
- High query performance requirements, highly latency-sensitive.
Cold Data
Cold data refers to historical data, typically accessed infrequently or only in specific cases (e.g., audits, historical analysis, trend prediction). Unlike hot data, cold data does not require real-time processing and can be stored on lower-cost, slower storage devices.
Examples of Cold Data in 5G RAN:
- Historical network monitoring data: Data on network status from previous days, weeks, or months, used for trend analysis, troubleshooting, and historical comparisons. Although this data doesn’t require frequent access, it is valuable for debugging and network optimization.
- Historical performance logs: Historical records of traffic and connection counts. Operators may retain this data for long-term trend analysis or to meet compliance and audit requirements.
- User behavior statistics: Statistical data on 5G user behavior, such as usage patterns and traffic usage, useful for business decisions and analysis but does not require frequent reads.
Storage Characteristics of Cold Data in 5G RAN:
- Can be stored on low-cost storage devices like HDDs or object storage (e.g., MinIO, S3).
- Long data lifecycle with low access frequency.
- Lower query performance requirements, higher latency is acceptable.
Criteria for Hot and Cold Data Segmentation
In the 5G RAN context, hot and cold data are segmented mainly based on access frequency and real-time requirements:
- Hot data is typically associated with real-time network monitoring, optimization, and scheduling, requiring quick response.
- Cold data is generally historical or infrequently accessed data, mainly used for historical analysis, compliance audits, and long-term trend forecasting.
Benefits of Hot and Cold Data Separation
- Cost Optimization: Hot data is stored on high-performance, high-cost devices, while cold data is stored on lower-cost storage, greatly reducing storage expenses.
- Performance Improvement: Storing hot data on high-performance storage ensures fast response times for data with real-time requirements.
- Higher Storage Efficiency: Cold data is stored in larger-capacity but slower storage, saving space and reducing the burden on hot data storage.
Application in 5G RAN Scenarios
By segmenting logs, monitoring data, and historical performance data in a 5G RAN system, it is possible to achieve:
- Real-time monitoring and response: Quickly processing high real-time requirement data ensures network stability.
- Long-term data retention and analysis: Storing historical data in object storage facilitates long-term analysis or compliance while not impacting system performance.
Hot and cold data separation is an effective strategy in 5G RAN operation and monitoring, optimizing costs while ensuring real-time capabilities and long-term maintainability.