Wireless Telemetry Gossiping for Resource-Constrained Networks
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Solution Overview
Problem
Resource-constrained devices in wireless networks face challenges with excessive telemetry data generation, leading to resource exhaustion and inefficient data management due to the inherent mobility of wireless clients and complex system interactions, which existing observability tools exacerbate.
Innovation Solution
Implement gossiping techniques among network devices to reduce telemetry data transmission and introduce closed-loop telemetry strategies that adapt sampling rates and storage based on device resources and geographical constraints, using a dominating set to efficiently collect data from a subset of devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all network devices send telemetry data to the controller, then complete network visibility is achieved, but resource consumption and data volume increase excessively
Solution Approach 1:
The network devices are segmented into a dominating set and non-dominating set. Only devices in the dominating set send telemetry data to the controller, while devices in the non-dominating set rely on their dominating set neighbors for data collection. This segmentation reduces the number of devices transmitting data while maintaining comprehensive network coverage.
Solution Approach 2:
Devices in the dominating set act as intermediaries between the controller and devices in the non-dominating set. The intermediary devices collect and forward telemetry information, enabling indirect monitoring of non-dominating devices without requiring direct communication from those devices to the controller.
2Measurement precision
If telemetry sampling rate is increased, then event detection accuracy improves, but device resource exhaustion occurs
Solution Approach 1:
Different sampling strategies are applied to different devices based on their roles. Devices in the dominating set, which have higher resource requirements, use adaptive sampling rates. Devices in the non-dominating set use lower sampling rates since their data is collected indirectly. This local differentiation optimizes overall network resource usage while maintaining event detection accuracy.
Solution Approach 2:
The telemetry sampling rate is made dynamic rather than static. The controller adjusts sampling rates adaptively based on network conditions, device resource status, and event importance. This allows the system to increase sampling during critical events while reducing it during normal operation to conserve resources.
Data Source
AI summary
Techniques for improving telemetry in resource-constrained device environments. In some examples, the techniques include gossiping telemetry information between peer devices of a wireless network to, among other things, reduce telemetry cost and/or an amount of telemetry data streamed to a telemetry collector. In some examples, the techniques may also include intelligently exporting telemetry data from resource-constrained devices towards backend systems without exhausting the resource-constrained devices and/or the backend systems. In examples, the telemetry data may be contextual information associated with an endpoint, an application, a network-device resource (e.g., CPU, battery, memory, storage, etc.), geographical constraints, and/or the like.