Network Event Remediation Service Prioritization
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Solution Overview
Problem
As organizations manage large fleets of computing devices, monitoring and identifying the sources of operational issues becomes increasingly complex, especially in distributed systems with multiple layers of dependency, making it difficult to efficiently address problems and their solutions.
Innovation Solution
A network event remediation service that generates and prioritizes tasks based on event logs from network computing devices, using historical data to prevent abnormal tasks, calculate device scores for prioritization, and automatically or manually remediate issues to ensure efficient resource allocation and issue resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If monitoring is extended to cover larger numbers of devices and more layers of dependency, then system coverage and detection capability are improved, but the complexity of identifying problem sources and solutions increases
Solution Approach 1:
The patent segments the complex monitoring problem into distinct components: event generation at device level, event correlation at service level, and remediation task management. This segmentation allows each component to handle specific aspects independently, reducing overall system complexity while maintaining comprehensive coverage across distributed devices and dependency layers.
Solution Approach 2:
The event correlation service acts as an intermediary between event-generating devices and remediation systems. It receives events from multiple sources, correlates them to identify root causes, and generates remediation tasks. This intermediary layer simplifies problem identification by centralizing the complex correlation logic, allowing individual devices to remain simple event sources without needing complex analysis capabilities.
2Speed
If automatic remediation tasks are generated for all detected events, then response speed is improved, but unnecessary resource consumption and abnormal task execution increase
Solution Approach 1:
The system performs preliminary actions by pre-defining remediation tasks and their associated criteria before events occur. When events are detected, the correlation service matches them against predefined task criteria rather than creating ad-hoc remediation actions. This preliminary preparation enables rapid response while avoiding resource waste, as only events matching specific criteria trigger predefined remediation tasks.
Solution Approach 2:
The system implements feedback mechanisms where remediation task execution results are monitored and fed back into the correlation service. This feedback allows the system to learn from previous remediation outcomes and adjust future task generation, preventing unnecessary resource consumption by avoiding redundant or ineffective remediation actions while maintaining rapid response to genuine issues.
3Measurement precision
If device scores are calculated based on comprehensive health data to prioritize tasks accurately, then task prioritization precision is improved, but data processing time and computational overhead increase
Solution Approach 1:
The system changes parameters by using a scoring mechanism that transforms multiple health data dimensions into a single prioritization metric. Instead of analyzing all health parameters simultaneously for each task prioritization decision, the system pre-calculates device scores based on health data, then uses these scores for rapid task prioritization. This parameter transformation maintains precision while reducing processing time during critical remediation decisions.
Data Source
AI summary
A network event remediation service receives event logs from network computing devices to identify issues associated with these network computing devices. Based on these issues, the service generates various tasks to address the identified issues. The service obtains historical task data in order to determine whether any tasks do not comport with historical tasks performed by the network computing devices and removing such tasks. The service obtains health data from the network computing devices to calculate device scores for each device. The service uses the device scores to prioritize the tasks for each network computing device. These tasks are then dispatched to the network computing devices.