Edge Event Reporting With In-Memory Aggregation and Fallback
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Solution Overview
Problem
Existing computer systems face inefficiencies, inaccuracies, and inflexibilities in monitoring and reporting metrics from edge devices, leading to high latency, resource wastage, and inability to operate when disconnected from cloud computing systems.
Innovation Solution
A real-time event data reporting system that embeds services within edge devices to collect, organize, and publish event data in real time using in-memory storage, processes data hierarchically, and utilizes fallback mechanisms for fault tolerance, enabling efficient, accurate, and flexible reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time monitoring and reporting is implemented in edge devices, then processing latency is reduced and monitoring accuracy is improved, but device complexity and resource consumption increase
Solution Approach 1:
The system segments the monitoring architecture into multiple components: event data collectors embedded within services, event maps for organized storage, publishing services for data distribution, and monitoring agents for data consumption. This segmentation allows real-time processing capabilities to be distributed across multiple specialized components rather than concentrated in a single complex system, reducing overall device complexity while maintaining real-time performance.
Solution Approach 2:
The patent introduces intermediary components such as event maps that act as buffers between data collection and data consumption processes. These intermediaries decouple the timing of data generation from data processing, allowing real-time monitoring without requiring all components to operate simultaneously at full complexity, thus reducing peak device complexity requirements.
2Measurement precision
If real-time monitoring with in-memory storage is implemented, then monitoring accuracy and speed are improved, but reliability and fault tolerance deteriorate due to data loss risks
Solution Approach 1:
The system implements beforehand cushioning through event maps that serve as in-memory buffers with spill-to-disk capabilities. These buffers are prepared in advance to hold event data temporarily, providing a cushion against data loss in case of failures. The event maps can spill data to persistent storage before memory is full, ensuring that critical monitoring data is preserved even when in-memory storage is at risk.
Solution Approach 2:
The patent changes the state parameter of data storage from purely volatile (in-memory) to a hybrid state that includes both in-memory and persistent storage layers. By adjusting the persistence parameter of event maps, the system can dynamically balance between speed (in-memory access) and reliability (persistent storage), achieving both real-time monitoring accuracy and fault tolerance.
3Ease of operation
If centralized cloud-based monitoring is used, then system management is simplified, but processing latency increases and connectivity dependence worsens
Solution Approach 1:
The monitoring system is segmented into distributed edge components that can operate independently. Event data collectors, publishing services, and monitoring agents are distributed across multiple edge devices rather than centralized in the cloud. This segmentation enables local real-time processing at each edge device while maintaining simplified centralized management through standard protocols and interfaces.
Solution Approach 2:
The patent adds a spatial dimension to the monitoring architecture by distributing monitoring capabilities across multiple edge devices in different locations. This dimensional change from centralized cloud-based monitoring to distributed edge monitoring reduces processing latency by enabling local real-time analysis while maintaining ease of operation through standardized management interfaces.
4Loss of information
If comprehensive event data collection is implemented, then monitoring coverage is improved, but resource wastage and processing overhead increase
Solution Approach 1:
The system applies local quality by organizing event data into event maps with specific structures optimized for different types of monitoring needs. Each event map can be tailored to collect only the relevant data for specific monitoring objectives, avoiding unnecessary data collection. This localized optimization of data collection quality reduces resource wastage while maintaining comprehensive coverage where needed.
Solution Approach 2:
The patent implements partial action by allowing selective collection and processing of event data based on priority and relevance. Not all event data is processed with the same level of detail or retained in memory - critical events receive full attention while less important events can be aggregated or discarded. This partial processing approach reduces resource wastage while maintaining adequate monitoring coverage.
Data Source
AI summary
A real-time event data reporting system is disclosed that makes real-time and near-real-time monitoring and reporting possible in edge devices. For example, in various instances, the real-time event data reporting system embeds services within traditional event data collectors of edge devices to obtain, organize, and publish event data for local computing devices in real time utilizing in-memory storage. Additionally, the real-time event data reporting system further processes the published event data to generate aggregated data that is persisted to a persistence storage. In this manner, the real-time reporting system efficiently and accurately provides event data reports to client devices with processed metric data in real time, or in near-real time when utilizing additional fallback safeguards. Indeed, the real-time reporting system provides a highly available, fault-tolerant, distributed, scalable, and efficient mechanism for collecting and managing various metrics from services in edge or cloud environments.