Cluster Telemetry Registration With RBAC and Dynamic Subscriptions
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Solution Overview
Problem
Existing telemetry architectures in distributed cluster networks are inflexible and unable to dynamically add new metric datasets, data producers, or consumers, lacking effective security and access controls, and inefficient in processing discrete and streaming telemetry data.
Innovation Solution
Implementing a subscription-based telemetry system with dynamic registration and role-based access control (RBAC) for telemetry data management, allowing dynamic subscription to metric datasets through preferred transports and optimizing data collection and sharing with RBAC-based cataloging and compliance checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed telemetry architecture is used, then system simplicity is maintained, but flexibility to add new metric datasets and consumers is lost
Solution Approach 1:
The telemetry architecture transitions from a static, pre-configured system to a dynamic system where metric datasets, producers, and consumers can be added and removed at runtime. The system maintains a registry of available metrics and allows dynamic subscription, enabling the architecture to adapt to changing requirements without requiring fundamental redesign.
Solution Approach 2:
The telemetry system implements a universal interface that can handle multiple types of metric datasets (discrete and streaming), multiple transport protocols (HTTP, gRPC, WebSocket), and multiple consumer types through a unified subscription mechanism. This multi-functional design allows the same core architecture to serve diverse telemetry needs.
2Loss of information
If comprehensive telemetry data collection is implemented, then complete system visibility is achieved, but security and access control requirements increase
Solution Approach 1:
The system implements Role-Based Access Control (RBAC) where access permissions are pre-configured for different roles before telemetry data is accessed. Administrators can define which roles have access to which metric datasets in advance, and the system automatically enforces these permissions during data access operations, simplifying the management of security for comprehensive telemetry collection.
3Loss of information
If both discrete and streaming telemetry data are processed, then complete data coverage is achieved, but processing efficiency decreases
Solution Approach 1:
The telemetry data processing system is segmented into separate handlers for discrete metrics and streaming metrics. Discrete metrics are processed as individual events, while streaming metrics are processed as continuous flows. This segmentation allows each data type to be optimized independently, with discrete metrics handled through event-driven processing and streaming metrics handled through buffer-based processing, thereby maintaining overall processing efficiency while achieving complete data coverage.
Data Source
AI summary
A telemetry processing system in a cluster network generates telemetry data from producers and formats it into a structured format for storage and transmission to subscribed consumers. The system implements an open telemetry system (OTEL) that is opaque regarding transport of data to the subscribers and includes access-based and security compliance controls. The system accommodates dynamic frequency requests by mapping data sets to collectors to optimize data collection and sharing. For large-scale systems, certain producer and consumer controls are provided. It also processes golden signals can be used to implement certain self-healing and debugging processes. For streaming data certain optimization features are used for storage and network bandwidth usage.