Hybrid Cloud Predictive Maintenance for On-Prem Server Anomalies
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Solution Overview
Problem
Hybrid Cloud systems face challenges in detecting and proactively addressing issues with on-prem servers due to intermittent connectivity, leading to difficulties in monitoring and diagnosing malfunctions, which results in reactive support methods and potential downtime.
Innovation Solution
Implementing predictive maintenance through coordinated efforts between on-prem servers and centralized datacenters, where on-prem servers collect and analyze log data and telemetry to generate monitor rules, enabling proactive alerts and maintenance without full data transmission, thus anticipating and resolving potential issues before they cause downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If on-prem servers transmit all system logs and telemetry data to centralized datacenters for analysis, then issue detection capability is improved, but network bandwidth consumption increases and latency worsens
Solution Approach 1:
The patent extracts and transmits only essential diagnostic data and anomaly indicators from on-prem servers to centralized datacenters, rather than transmitting complete system logs and telemetry data. This selective data transmission maintains issue detection capability while significantly reducing network bandwidth consumption.
Solution Approach 2:
The monitoring system is segmented into local anomaly detection components on on-prem servers and centralized analysis components at datacenters. Each segment performs specialized functions, with local segments filtering and preprocessing data before transmission, thereby reducing overall network traffic while preserving detection accuracy.
2Measurement precision
If multiple on-prem servers each generate their own diagnostic logs, then local issue detection is improved, but diagnostic complexity increases
Solution Approach 1:
The patent implements universal monitor rules that can be applied across multiple on-prem servers regardless of their specific configurations or locations. These standardized rules provide a common diagnostic framework that simplifies troubleshooting while maintaining the ability to detect server-specific issues.
Solution Approach 2:
The system incorporates feedback mechanisms where diagnostic results and anomaly patterns from multiple servers are communicated back to update and refine monitor rules. This continuous feedback loop enables the system to learn from accumulated data and improve diagnostic accuracy while maintaining consistent evaluation criteria across all servers.
3Productivity
If reactive support methods are used where users open support tickets after detecting issues, then support resources are conserved, but system downtime increases
Solution Approach 1:
The patent implements proactive anomaly detection that identifies potential issues before they cause system failures or require user intervention. By detecting and alerting to problems in advance, the system enables preventive maintenance and resolves issues before they impact operations, thereby reducing both downtime and the need for reactive support resources.
Data Source
AI summary
The techniques disclosed herein enable predictive maintenance features in Hybrid Cloud systems. The system can analyze system data defining one or more operating conditions of an on-prem server and determine if one or more predetermined conditions are met. If a predetermined condition is met, the on-prem server can generate and transmit log data to a primary server. The primary server can generate one or more monitor rules having one or more updated predetermined conditions for detecting an anomaly at the on-prem server. Using the monitor rules, the on-prem server can detect and proactively resolve potential issues. The on-prem server can also transmit diagnostic data to the primary server for generating an updated monitor rule that is further tailored to the conditions of the on-prem server.