Predictive Scan Engine Runtime Duration Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing security management systems lack accurate estimates of security service runtimes, leading to inefficiencies in scheduling and resource allocation, and potential unawareness of issues during security service operations.
Innovation Solution
A security platform that uses a prediction model, trained with datasets of successfully completed security service scans, to estimate runtime durations of security services, allowing for improved scheduling and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If security service runtimes are estimated without a prediction model, then the system is simpler to implement, but scheduling efficiency and resource allocation are degraded
Solution Approach 1:
The system performs preliminary actions by training a prediction model using historical security service scan data before actual scheduling operations. The model learns from past scan durations, configuration parameters, and performance metrics to predict future runtime durations, enabling more efficient scheduling decisions without real-time complexity
Solution Approach 2:
The system creates a virtual copy of the security service execution environment through the prediction model. Instead of running actual scans to determine runtime, the model replicates the prediction function using learned patterns from historical data, eliminating the need for complex real-time monitoring infrastructure
2Productivity
If security service runtimes are estimated without a prediction model, then the system requires fewer computing resources, but resource allocation becomes inefficient
Solution Approach 1:
The system performs preliminary training using historical data to build the prediction model, which then enables efficient resource allocation for future security services. The model captures resource consumption patterns from past executions, allowing accurate prediction without requiring additional computing resources during actual operations
Solution Approach 2:
The system implements feedback mechanisms by continuously updating the prediction model with new scan data and performance metrics. This feedback loop refines the model's accuracy over time, improving resource allocation efficiency while the model itself requires minimal additional computing resources compared to running actual scans
3Reliability
If security service runtimes are estimated without a prediction model, then the system is easier to operate, but users cannot detect issues during service execution
Solution Approach 1:
The system implements feedback by comparing actual scan durations against predicted durations from the model. When deviations exceed thresholds, the system generates alerts indicating potential issues such as network problems, resource constraints, or security anomalies, enabling reliable issue detection without requiring complex manual monitoring procedures
Solution Approach 2:
The prediction model performs self-service by automatically learning from historical data and updating its predictions without manual intervention. The system autonomously identifies patterns and generates runtime estimates, maintaining ease of operation while providing reliable issue detection through automated comparisons
Data Source
AI summary
Various embodiments include systems and methods to implement predictive scan engine runtime durations by a security platform to predict runtime durations associated with computing resources. Predictive scan engine runtime durations may be determined by training a prediction model using a multiple linear regression analysis. For example, the security platform may determine a prediction model using training data that associates runtime durations with configuration inputs associated with a security service that operates with respect to a computing resource. Based on the prediction model, the security platform may determine a runtime estimate for a security service run that is configured similarly to a previous security service run used to train the prediction model.