Automated Log Aggregation for Multi-Tenant Performance Monitoring
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Solution Overview
Problem
Large-scale computer systems with diverse client configurations and hardware setups pose a challenge for manual monitoring and performance management, as a single team of administrators cannot adequately monitor and address issues across all clients effectively, leading to unrecognized and uncorrected performance problems.
Innovation Solution
An automated system that aggregates log data from multiple tenants to determine performance metrics, generates composite metrics, and triggers automated actions or manual responses based on these metrics to address performance issues, utilizing machine learning for complex behavior analysis and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual monitoring by computer system administrators is used, then individual system performance can be addressed, but the system cannot scale to handle very large numbers of clients with diverse configurations
Solution Approach 1:
The system enables automated self-monitoring and self-diagnosis of computer systems. The performance monitoring system automatically collects metrics, identifies issues, and triggers alerts without requiring manual intervention by administrators, allowing the system to serve itself and scale independently of administrative team size.
Solution Approach 2:
The patent replaces the mechanical system of manual monitoring by human administrators with an automated electronic monitoring system. The system uses automated agents, performance metrics collection, and algorithmic analysis to substitute human administrative tasks, enabling scalable monitoring of large numbers of clients.
2Reliability
If a large team of computer system administrators is deployed, then more systems can be monitored, but the cost and complexity of management increases significantly
Solution Approach 1:
The monitoring system performs automated self-assessment of system performance, collecting metrics and identifying issues without human intervention. This self-service capability maintains high reliability of monitoring while eliminating the need for large administrative teams and their associated complexity.
Solution Approach 2:
The system implements automated feedback loops where performance metrics are continuously collected, analyzed, and used to trigger alerts or automated responses. This feedback mechanism ensures reliable performance monitoring while reducing the need for manual administrative oversight.
3Ease of repair
If manual fixing of system issues is performed, then problems can be resolved, but response time increases and issues may go unrecognized
Solution Approach 1:
The system continuously monitors performance metrics and identifies potential issues before they become critical problems. By detecting anomalies and performance degradation early, the system enables preventive action rather than reactive repair, reducing both response time and the severity of issues.
Solution Approach 2:
The automated monitoring system provides continuous feedback on system performance, immediately detecting and alerting administrators to issues as they occur. This real-time feedback eliminates the delay inherent in manual checking and enables rapid response to problems.
4Adaptability or versatility
If diverse client configurations are supported, then service coverage is expanded, but monitoring and management becomes infeasible
Solution Approach 1:
The monitoring system is designed with universal capabilities that can handle diverse client configurations through standardized metrics collection and analysis. The system uses configuration-agnostic performance metrics and automated adaptation to work across different hardware, software, and network configurations without requiring configuration-specific monitoring logic.
Solution Approach 2:
The system automatically adapts to diverse client configurations by dynamically adjusting monitoring parameters, thresholds, and metrics based on the specific characteristics of each client system. This parameter adaptation enables the system to effectively monitor varied configurations without increasing overall system complexity.
Data Source
AI summary
A system for automated process performance determination includes an interface and a processor. The interface is configured to receive log data associated with a plurality of tenants. The log data comprises one or more log data types. The processor is configured to aggregate the log data into an aggregated set of log data; and determine a set of metrics based at least in part on the aggregated set of log data. A metric of the set of metrics is associated with a tenant of the plurality of tenants and one of the one or more log data types. The processor is further configured to determine a composite metric for the tenant by combining metrics of the set of metrics associated with the tenant; determine a response based at least in part on the composite metric; and, in the event the response indicates an automated action, perform the automated action.