Network Device Agent for Automatic Application Repair
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Solution Overview
Problem
The complexity of modern web services infrastructure makes it challenging to maintain high service performance and user experience, particularly in monitoring and repairing network devices with non-computing functions, such as smart appliances and point of sale systems, due to the difficulty in tracking and monitoring distributed applications across disparate systems.
Innovation Solution
An automatic network device agent that monitors, analyzes data, detects performance issues, identifies remedies based on training data, and applies fixes to network devices, enabling them to operate in limited capacity until permanent repairs can be made, using wireless communication and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and repair methods are used for network devices in distributed web services infrastructure, then service performance can be maintained through human intervention, but the complexity of tracking and monitoring distributed applications across disparate systems makes it challenging and time-consuming
Solution Approach 1:
The network device is equipped with an agent that enables self-diagnosis and self-repair capabilities. The agent autonomously monitors device performance, detects anomalies, identifies remedies from training data, and applies fixes without requiring external human intervention, allowing the device to service itself
Solution Approach 2:
The system pre-populates a training data repository with anomaly patterns and corresponding remedies before runtime. When an anomaly occurs, the agent can quickly match it against pre-analyzed training data to identify appropriate remedies, eliminating the need for real-time complex analysis and enabling faster response
2Measurement precision
If extensive monitoring and analysis resources are allocated to detect and repair network device anomalies, then detection accuracy and response time can be improved, but network devices with non-computing functions have limited processing power and resources
Solution Approach 1:
The monitoring and repair system is segmented into two parts: a lightweight agent residing on the resource-constrained network device for data collection and anomaly detection, and a external training data repository for storing pre-analyzed anomaly patterns and remedies. This segmentation allows the device to maintain accurate detection capabilities without requiring extensive local processing resources
Solution Approach 2:
Instead of performing complex analysis directly on the network device, the system creates a copy of anomaly patterns and remedies in a training data repository. The agent on the network device copies and matches against these pre-analyzed patterns, achieving accurate anomaly detection without consuming significant processing resources on the device itself
3Productivity
If automatic repair mechanisms are implemented on network devices, then service continuity can be improved by enabling devices to operate in limited capacity until permanent repairs are made, but the complexity of identifying and applying appropriate remedies increases
Solution Approach 1:
The system performs preliminary analysis by pre-populating a training data repository with anomaly patterns and their corresponding remedies before runtime. This advance preparation simplifies the real-time repair process, as the agent only needs to match current anomalies against pre-analyzed patterns rather than performing complex analysis during service disruptions
Solution Approach 2:
The training data repository acts as an intermediary between anomaly detection and remedy application. It stores pre-analyzed anomaly patterns and corresponding remedies, serving as a lookup table that simplifies the repair decision-making process. The agent queries this intermediary to identify appropriate remedies without requiring complex real-time analysis
Data Source
AI summary
In one aspect, a system for automatic application repair by a network device agent in a monitored environment includes a processor; a memory; and one or more modules stored in the memory and executable by a processor to perform operations including: capture network device application data for a monitored application, the network device performing a function other than computing, analyze the captured data to detect a performance issue, identify a remedy associated with training data that corresponds to the captured data, and automatically applying the remedy to the network device.