Edge Filtering in Hybrid Cloud Config Updates
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Solution Overview
Problem
Current virtual computing systems face challenges in efficiently updating configuration settings for edge processing units, particularly when false positives occur, as upgrading bundled services is slow, disruptive, and resource-intensive, necessitating a strategic approach to update specific services within HCI clusters without upgrading the entire bundle.
Innovation Solution
A telemetry platform with intelligent edge processing units that dynamically pushes new configurations from a cloud server to edge processing systems in a canary manner, allowing for rollback and optimizing data processing by collecting and summarizing data locally before sending it to the cloud, thereby reducing unnecessary data transmission and enabling flexible processing on edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If configuration updates are applied to edge processing units, then system functionality and performance are improved, but false positives may occur requiring rollback
Solution Approach 1:
The patent implements canary deployments by first applying configuration updates to a subset of edge processing units before full deployment. This preliminary action allows validation of updates on a limited scale, enabling rollback if false positives occur, thus resolving the contradiction between update efficiency and system stability
Solution Approach 2:
The system continuously monitors edge processing units after configuration updates and automatically triggers rollback when false positives are detected. This feedback mechanism ensures that productivity improvements from updates do not compromise system reliability, as the system adapts based on performance data
2Stability of the object's composition
If bundled services are upgraded together, then system consistency is maintained, but update time and resource consumption increase
Solution Approach 1:
The patent segments the bundled services into individually upgradable components. Instead of upgrading all services simultaneously, the system allows selective updates of specific services within the bundle, significantly reducing update time while maintaining consistency through coordinated deployment management
Solution Approach 2:
The system applies partial updates by upgrading only the necessary services rather than the entire bundle. This partial action approach reduces resource consumption and update time while maintaining system consistency through proper configuration management and rollback capabilities
3Measurement precision
If all data is transmitted to cloud, then centralized processing accuracy is improved, but network overhead and latency increase
Solution Approach 1:
The patent segments data processing between edge and cloud by performing initial data filtering, aggregation, and preprocessing at the edge. Only processed and summarized data is transmitted to the cloud, reducing network overhead while maintaining processing accuracy through distributed intelligence
Solution Approach 2:
The edge processing units act as intermediaries between data sources and the cloud. They perform local data processing and only transmit essential information to the cloud, thereby reducing network transmission overhead and latency while maintaining centralized oversight and accuracy
4Speed
If edge processing is implemented, then data processing speed is improved, but device complexity increases
Solution Approach 1:
The patent implements universal edge processing units that can perform multiple functions including data collection, filtering, aggregation, and local processing. This multi-functionality approach increases processing speed while managing device complexity through standardized, versatile edge components
Data Source
AI summary
Various embodiments disclosed herein are related to a non-transitory computer readable storage medium. In some embodiments, the medium includes instructions stored thereon that, when executed by a processor, cause the processor to receive, at a node of a cluster on an edge network, an indication that the cluster received a configuration update, compare a first parameter of a configuration state of the node to a second parameter of the configuration update, determine if the first parameter matches the second parameter, in response to determining that the first parameter matches the second parameter, apply the configuration update, and collect data in accordance with the configuration update.