Computing Environment Remediation with Trained Learning Models
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Solution Overview
Problem
Existing report generation systems for enterprise-managed devices fail to consider the impact on customer computing environments due to degraded device performance, unavailability, increased carbon footprint, and potential benefits from remediation plans, leading to resource waste and inefficiency.
Innovation Solution
An apparatus and method that utilizes machine learning models to analyze telemetry data from devices, predict future operational states, and generate context-aware remediation plans considering network topology and customer-specific impacts, including performance degradation, data loss, and carbon footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring systems collect and analyze device data, then device performance can be tracked, but the system cannot predict future operational states or assess customer-specific impacts
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical device data before actual prediction needs arise. This pre-training enables the system to quickly predict future operational states, customer impacts, and remediation benefits without complex real-time analysis, resolving the contradiction between prediction accuracy and system complexity
Solution Approach 2:
Machine learning models serve as intermediaries between raw device monitoring data and actionable insights. These models translate complex telemetry data into predicted operational states and customer impact assessments, eliminating the need for complex rule-based systems while maintaining high prediction accuracy
2Productivity
If remediation plans are generated without considering customer context, then resource allocation is simplified, but resource waste increases due to lack of context-aware optimization
Solution Approach 1:
The system applies local quality by customizing remediation plans according to specific customer contexts, device dependencies, and operational priorities. Instead of generic one-size-fits-all approaches, each remediation plan is optimized for the local customer environment, improving operational efficiency while minimizing resource waste through context-aware resource allocation
Solution Approach 2:
The system incorporates feedback loops that continuously learn from the outcomes of remediation actions and customer-specific conditions. This feedback mechanism enables the system to refine predictions and optimize resource allocation over time, achieving both high productivity and reduced resource waste through adaptive learning
3Reliability
If comprehensive device monitoring is implemented, then device health can be tracked, but the system cannot assess dependencies or generate context-aware remediation strategies
Solution Approach 1:
The system merges device monitoring data with customer context information, device dependencies, and operational priorities into a unified analysis framework. This integration ensures that no critical context information is lost while maintaining comprehensive device health monitoring, enabling the system to generate context-aware remediation strategies that consider the full operational landscape
Data Source
AI summary
An apparatus includes at least one processing device comprising a processor coupled to a memory, wherein the at least one processing device is configured to receive data corresponding to operation of at least one device and one or more device components, and predict, using a plurality of machine learning models, a future operational state of the at least one device, at least one impact of the future operational state, and at least one trend associated with the at least one impact, wherein the predictions are based at least in part on the received data. A remediation plan for the at least one device is generated based at least in part on the predictions.