Distributed Time-Series Server for Network State Detection
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Solution Overview
Problem
Existing remote network management platforms face challenges in efficiently detecting and managing computing device states and state transitions, particularly in large networks where devices may become overloaded or misconfigured, leading to unavailable features and capabilities due to processing and storage burdens on computing devices with limited resources.
Innovation Solution
A distributed architectural arrangement is introduced where computing devices take measurements and transmit them to a time-series server device for storage and processing, allowing the server to apply pre-defined trigger configurations to detect states and transitions, reducing the burden on devices and enabling efficient storage and response to observed states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If computing devices store time-series measurement data locally, then post-hoc analysis capability is improved, but storage utilization on devices increases
Solution Approach 1:
The patent extracts the time-series data storage function from computing devices and places it in a dedicated time-series server. Computing devices only retain trigger configurations and receive transition notifications, while the server stores complete measurement data streams indefinitely, enabling post-hoc analysis without burdening device storage resources.
Solution Approach 2:
The time-series server acts as an intermediary between computing devices and analysis requirements. It receives measurements from devices, applies trigger configurations to detect transitions, stores the complete time-series data, and provides notification services, thereby decoupling storage requirements from computing devices.
2Speed
If computing devices process and detect states in real-time, then detection speed is improved, but processing burden on devices increases
Solution Approach 1:
The system segments the state detection functionality into two parts: computing devices generate measurements and transmit them to the server, while the time-series server applies trigger configurations to detect transitions. This segmentation reduces processing burden on devices while maintaining real-time detection capability through the server's dedicated trigger engine.
3Reliability
If all computing devices have full monitoring functionality, then detection capability is improved, but device resource requirements increase
Solution Approach 1:
The time-series server provides universal state detection services to multiple computing devices simultaneously. Instead of each device having its own monitoring stack, the server handles trigger configuration application and transition detection for all devices, reducing resource requirements on individual devices while maintaining comprehensive detection capability across the network.
Data Source
AI summary
A computing system may involve a time-series server device and computing devices. The time-series server device may be configured to: receive and store pre-defined trigger configurations; receive and store time-series data, wherein the pre-defined trigger configurations define states and/or state transitions for the received time-series data; apply, by way of a trigger engine, the pre-defined trigger configurations to the received time-series data to determine observed states and/or state transitions in the time-series data; and store, in transition storage, representations of the observed states and/or state transitions. One or more applications operating on computing devices may be configured to: transmit the pre-defined trigger configurations to the time-series server; transmit a stream of the time-series data to the time-series server; and repeatedly poll and receive, by way of a plurality of worker threads, the representations of the observed states and/or state transitions from the transition storage.