Anomaly Detection System for Network Resource Utilization
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Solution Overview
Problem
In network-based computing, users face challenges in tracking and managing resource utilization, leading to unexpected costs due to sudden increases in resource usage, which can be exacerbated by malicious activity or accidental overutilization, making it difficult to identify the sources of anomalous usage patterns in real-time.
Innovation Solution
Implementing an anomaly detection system that uses machine learning models to identify anomalies in resource utilization and automatically diagnose root causes, allowing for real-time notification and corrective action, such as suspending linked accounts responsible for excessive usage, thereby preventing further resource exhaustion and cost escalation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users utilize network-based computing resources on a pay-as-you-go model, then infrastructure costs are minimized and resource flexibility is improved, but tracking utilization becomes difficult and unexpected costs arise from sudden utilization increases
Solution Approach 1:
The system continuously monitors resource utilization data and provides feedback to users through anomaly detection and notification mechanisms. When unusual utilization patterns are detected, the system sends alerts to users, enabling them to understand and respond to utilization changes in real-time, thus resolving the information loss problem while maintaining resource flexibility.
Solution Approach 2:
The system automatically detects anomalies and generates notifications without requiring manual monitoring or intervention from users. The anomaly detection system self-monitors utilization patterns and autonomously identifies when something is amiss, freeing users from the burden of manual tracking while maintaining their flexibility in resource usage.
2Adaptability or versatility
If service providers provide a large variety of computer resources, then user needs are better met and service versatility is improved, but it becomes difficult for users to track utilization across different resource types
Solution Approach 1:
The anomaly detection system serves multiple functions: it monitors various resource types (compute, storage, networking), detects anomalies across different service categories, and provides unified notifications to users. This multi-functional approach consolidates what would otherwise be separate tracking mechanisms into a single system, reducing complexity while maintaining comprehensive monitoring of diverse resources.
3Use of energy by moving object
If users are charged based on resource utilization, then cost efficiency is improved, but sudden cost increases occur due to unmonitored utilization changes
Solution Approach 1:
The system performs preliminary detection of unusual utilization patterns before they result in significant cost increases. By monitoring utilization trends continuously and detecting anomalies early, the system allows users to take corrective action before unexpected bills arrive, maintaining cost efficiency while improving cost predictability through proactive rather than reactive monitoring.
4Measurement precision
If real-time monitoring of resource utilization is implemented, then anomaly detection capability is improved, but system complexity and processing requirements increase
Solution Approach 1:
The system replaces complex manual monitoring and analysis mechanisms with automated machine learning-based anomaly detection. Instead of requiring sophisticated real-time processing and complex monitoring architectures, the system uses pre-trained models that can identify patterns and anomalies efficiently, reducing computational complexity while maintaining high detection precision through intelligent rather than purely mechanical monitoring approaches.
Data Source
AI summary
This disclosure describes systems, devices, and techniques for detecting and diagnosing anomalies in utilization levels of network-based resources. In an example method, at least one utilization level of one or more computer resources by an account during a time interval may be received. A possible anomaly can be detected if the at least one utilization level is determined to be outside of a predetermined range. The anomaly can be confirmed determining that at least a threshold number of multiple discrimination layers identify an anomaly in the at least one utilization level. An action can be performed in response to confirming the anomaly.