Edge Fault Tree Remediation for Low-Latency Self-Healing
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Solution Overview
Problem
Existing cloud computing architectures face challenges in latency, availability, bandwidth usage, data privacy, network security, and the capacity to process large volumes of data in real-time, especially for edge computing applications that require immediate processing.
Innovation Solution
Implementing resiliency and redundancy in edge computing devices through automated and redundant provisioning of management and workload clusters, using machine learning and artificial intelligence models for self-healing and fault remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized data centers are used for data processing, then data processing capacity is improved, but latency and bandwidth usage worsen
Solution Approach 1:
The patent segments the centralized data center architecture into distributed edge computing nodes deployed throughout the network. These edge nodes process data locally near the source, eliminating the need to transmit all data to centralized data centers. This segmentation resolves the contradiction by maintaining processing capacity through distributed computation while reducing latency through localized data handling.
2Loss of time
If edge computing nodes are deployed, then latency is reduced, but system reliability worsens due to potential single points of failure
Solution Approach 1:
The patent implements local quality by deploying multiple edge computing nodes with differentiated roles and capabilities at various network locations. Each edge node is configured with specific functions and redundancy relationships, allowing the system to maintain high reliability through distributed architecture while preserving low latency through local processing. The local quality principle enables tailored configurations at each edge location to optimize both reliability and performance.
Solution Approach 2:
The patent applies beforehand cushioning by pre-configuring redundant edge computing nodes and establishing failover mechanisms before failures occur. The system proactively distributes computational workloads across multiple edge nodes and prepares backup configurations, ensuring that if one edge node fails, others can immediately assume its responsibilities. This preventive approach maintains system reliability while preserving the low-latency benefits of edge computing.
3Reliability
If redundant nodes are provisioned for fault tolerance, then reliability is improved, but device complexity worsens
Solution Approach 1:
The patent implements self-service through automated provisioning systems that enable edge computing nodes to automatically configure themselves and their redundant counterparts. The system uses self-healing algorithms and intelligent automation to manage the complexity of provisioning redundant nodes, eliminating the need for manual configuration. This automation resolves the contradiction by providing high reliability through redundancy while keeping provisioning complexity manageable through self-managing systems.
4Ease of repair
If automated self-healing mechanisms are implemented, then fault remediation is improved, but system complexity worsens
Solution Approach 1:
The patent applies feedback principles by implementing continuous monitoring systems that track the health status of edge computing nodes and automatically trigger remediation actions when faults are detected. The system uses feedback loops to detect anomalies, diagnose issues, and execute self-healing procedures without human intervention. This feedback mechanism resolves the contradiction by providing easy fault remediation through automation while managing system complexity through structured monitoring and response protocols.
Data Source
AI summary
Systems and techniques are provided for self-healing at the edge. A plurality of faults can be detected for an edge device based on monitoring log information of the edge device, with remediation information indicative of remediation actions performed for individual faults or combinations of faults included in the plurality of faults. A hierarchical fault tree data structure can be generated mapping between the plurality of faults and the remediation information, each fault comprising root or child node of the fault tree, and each remediation action comprising a leaf node of the fault tree. An indication of one or more faults can be provided to a self-healing machine-learning or artificial intelligence engine configured to generate a corresponding fault remediation action based on traversing the hierarchical fault tree according to the indicated one or more faults. The fault remediation action can be output for self-healing of the indicated one or more faults.