Dynamic Telemetry Collection Cadence for Network Load Control
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Solution Overview
Problem
Conventional telemetry data collection in networks places a significant load on network elements and central controllers, leading to inefficiencies and resource wastage due to fixed frequency collection, which does not adapt to network needs, and complicates data management and retrieval.
Innovation Solution
Implementing a cloud-based network controller that manages telemetry data using doubly-indexed state blocks within a shared meta-schema, allowing dynamic adjustment of collection cadence and synchronization through cursors, enabling efficient data management and retrieval based on network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed frequency telemetry data collection is implemented, then data is consistently collected and sent to the controller, but the processing load on network elements and the central controller becomes significant
Solution Approach 1:
The patent implements dynamic telemetry data collection by allowing the controller to adjust the collection frequency and cadence based on network conditions and data change detection. Instead of fixed frequency collection, the system dynamically modifies collection parameters to match actual network state, reducing unnecessary data gathering during stable periods while increasing collection frequency when changes are detected.
Solution Approach 2:
The system changes the parameter of data collection frequency from a fixed value to a dynamic parameter that can be adjusted based on network conditions. The controller monitors network state and modifies collection cadence parameters in real-time, transitioning between different collection frequencies to optimize the balance between data freshness and processing load.
2Speed
If high frequency telemetry data collection is implemented, then timely network insights are obtained, but network bandwidth and processing resources are wasted when no significant changes occur
Solution Approach 1:
The patent implements a feedback mechanism where the controller monitors network data for significant changes and uses this information to adjust collection frequency. When data remains stable within threshold ranges, the system reduces collection frequency to conserve resources. When changes exceed thresholds, the system increases frequency to capture timely insights, creating a closed-loop adaptive system.
Solution Approach 2:
The system implements periodic data collection with variable periods rather than fixed intervals. The collection period dynamically adjusts based on detected network activity and change magnitude, extending intervals during stable conditions and shortening them during active changes, thereby optimizing resource usage while maintaining responsiveness.
3Ease of manufacture
If conventional fixed frequency data collection is used, then simple implementation is maintained, but the system cannot adapt to varying network conditions and capabilities
Solution Approach 1:
The patent implements self-service adaptability where the telemetry system automatically adjusts its own collection parameters based on observed network conditions without requiring manual configuration or complex external control. The system monitors its own performance and network state, then autonomously modifies collection frequency and cadence to optimize operation under varying conditions.
Solution Approach 2:
The system performs preliminary assessment of network conditions and data stability before determining collection frequency. By evaluating initial network state and predicting future trends, the system proactively adjusts collection parameters in advance, preventing unnecessary data collection while ensuring timely capture of significant events.
4Productivity
If increased processing capabilities are deployed to handle high volume telemetry data, then data analysis speed is improved, but financial costs increase
Solution Approach 1:
The patent applies partial action by collecting and processing only the necessary subset of telemetry data rather than all available data continuously. The system selectively gathers data based on change detection and network conditions, processing only relevant portions while maintaining adequate analysis speed for critical events, thereby avoiding the need for excessive processing capacity.
Data Source
AI summary
Described herein are devices, systems, methods, and processes for managing the collection and synchronization of telemetry data in a network overseen by a cloud-based network controller. This can be achieved by representing telemetry data as doubly-indexed state blocks within a shared meta-schema. Each type within the schema may be associated with a temporal list of objects of that type, providing ordered indexing by name and by time of last change. Cursors representing data witnesses may be threaded in place within these lists, enabling synchronization of telemetry data between devices without buffering. The system can dynamically adjust telemetry collection cadence in real time across devices in the fabric as users navigate the user interface. This approach can provide an effective mechanism to manage the load created by the telemetry, particularly in the context of network switches and telemetry collection.