Workload and Exception Engines for IT Anomaly Detection
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Solution Overview
Problem
Current IT operations face challenges in managing data centers and applications due to increasing complexity, with limited methods to track minute performance and resource consumption, leading to anomalies such as coding errors and inefficiencies that waste resources, cause delays, and threaten business operations.
Innovation Solution
A method involving a workload engine and exception engine that retrieve and analyze metrics from a database to identify operational anomalies, associate workloads, and provide recommendations for mitigation, optimizing IT resource usage and preventing inefficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring and evaluation methods are used to track IT infrastructure performance, then basic performance tracking is possible, but they fail to identify coding errors, rogue software, application and system inefficiencies, and other anomalies that consume much capacity and utility
Solution Approach 1:
The monitoring system is segmented into multiple specialized components: workload engines for collecting and analyzing metrics, exception engines for detecting anomalies, and database systems for storing data. This segmentation allows each component to focus on specific tasks, improving anomaly detection precision while distributing system complexity across modular units rather than requiring a monolithic complex system.
Solution Approach 2:
The patent introduces intermediary components including database systems that store metrics and anomaly data, and communication interfaces that connect monitoring components to IT infrastructure. These intermediaries enable precise anomaly detection by facilitating data flow between components without requiring direct complex interactions between all system elements.
2Productivity
If IT staff manually evaluate and manage IT resources, then some level of control is possible, but staff become overwhelmed and cannot keep pace with increasing complexity and demand
Solution Approach 1:
The monitoring system performs self-service by automatically collecting metrics from IT infrastructure, analyzing data to detect anomalies, and generating notifications without requiring manual intervention. Workload engines automatically gather performance data, exception engines autonomously identify anomalies, and the system self-manages the complexity of monitoring tasks, thereby improving productivity while the automation handles system complexity.
Solution Approach 2:
The patent replaces manual mechanical evaluation methods with automated electronic monitoring systems. Instead of IT staff manually checking and evaluating IT resources, electronic workload engines and exception engines automatically collect, analyze, and interpret performance metrics, substituting human manual processes with automated computational systems that handle complexity more efficiently.
3Loss of energy
If anomalies and inefficiencies are not identified, then system operations continue without interruption, but capacity and utility are wasted, leading to over-resourcing and over-purchasing of assets
Solution Approach 1:
The monitoring system implements feedback mechanisms where exception engines continuously analyze metrics and provide feedback about anomalies to system operators. When coding errors, rogue software, or inefficiencies are detected, the system generates notifications that enable timely correction, preventing resource waste. This continuous feedback loop makes anomaly detection manageable by automatically identifying issues that would otherwise be difficult to detect.
Solution Approach 2:
The system performs preliminary analysis of IT infrastructure performance by continuously collecting and analyzing metrics before significant resource waste occurs. Exception engines proactively detect anomalies in coding errors, rogue software, and inefficiencies before they cause substantial capacity consumption, enabling preventive action rather than reactive response to already-wasted resources.
4Reliability
If more IT assets are purchased to meet apparent needs, then capacity requirements are satisfied, but utilization is wasted on activity that is not real work, leading to further over-purchasing
Solution Approach 1:
The monitoring system provides feedback about actual resource utilization patterns by detecting and eliminating anomalies that cause wasted capacity. By identifying and correcting coding errors, rogue software, and inefficiencies, the system ensures that IT assets are used for productive work rather than wasted activity, providing accurate feedback on true capacity requirements without the distortion of waste-driven over-purchasing.
Data Source
AI summary
A method for anomaly identification and IT resource optimization includes retrieving, by a workload engine executing on a first computing resource, from a database populated by a client agent executing on a second computing resource, a metric associated with a process. The method includes analyzing, by the workload engine, the retrieved metric and the process. The method includes associating, by the workload engine, at least one workload with the process, responsive to the analysis. The method includes analyzing, by an exception engine executing on the first computing resource, the retrieved metric, the process, and the at least one workload; analyzing includes applying at least one workload rule to the at least one workload. The method includes identifying, by the exception engine, an operational anomaly within the process, responsive to the analysis by the exception engine. The method includes providing a recommendation for mitigating the operational anomaly.