Modular Outpost Pipeline for Network Scan Workload Balancing
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Solution Overview
Problem
Complex workflows involving large data sets require substantial computational resources, and internal scans and enrichments in network environments often face delays or errors due to the need for querying or scanning multiple locations.
Innovation Solution
A modular outpost system comprising a server and a modular outpost engine with primary and secondary orchestrator modules, allowing for dynamic definition of a modular outpost pipeline with single or branching paths and integration with external systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a modular outpost system with dynamic pipeline definition is implemented, then productivity and flexibility in processing large data sets are improved, but device complexity increases
Solution Approach 1:
The outpost system is divided into multiple independent orchestrator modules (primary orchestrator and secondary orchestrators) that each handle specific stages of the scanning pipeline. This segmentation allows the system to process large data sets in parallel through multiple stages simultaneously, improving productivity while keeping each module's complexity manageable.
Solution Approach 2:
The pipeline configuration is made dynamic through the ability to define branching paths and modify scan parameters in real-time based on data flow characteristics. The system can adaptively route data through different pipeline stages and modify scanning behavior without requiring complete system reconfiguration, thereby improving productivity without proportionally increasing complexity.
2Reliability
If scans are conducted across multiple network locations to identify vulnerabilities, then measurement precision and reliability are improved, but loss of time increases due to delays and errors
Solution Approach 1:
The pipeline architecture enables continuous scanning operations across multiple network locations by maintaining an unbroken data flow through sequential stages. The primary orchestrator continuously feeds targets to secondary orchestrators, which continuously process and pass results along the pipeline, eliminating idle time and ensuring that scanning operations proceed without interruption, thereby reducing total scan duration while maintaining comprehensive coverage.
Solution Approach 2:
The system performs preliminary actions by pre-configuring the pipeline with multiple scanning stages and pre-defining branching logic before scans begin. This allows the system to be prepared to handle various scan scenarios in advance, reducing the time required during actual scanning operations while ensuring comprehensive and accurate vulnerability identification across all target locations.
3Reliability
If a hierarchical and modular avoid list is implemented to avoid adverse targets, then reliability is improved by preventing scanning errors, but device complexity increases
Solution Approach 1:
The avoid list functionality is segmented into a separate hierarchical module that operates independently from the main scanning pipeline. This modular avoid list system maintains reliability by filtering adverse targets at multiple hierarchical levels without requiring the main orchestrator logic to become more complex. The segmentation isolates the complexity of avoid list management into a dedicated component.
Data Source
AI summary
An example of the modular outpost pipeline can make complex branching decisions at stages along the pipeline based on attributes of the data already gathered. This allows modifications to scans in progress within the outpost-based pipeline, and near-real-time sharing of specific scan data to external tools. The logic necessary can be implemented in any outpost pipeline stage but is preferentially accomplished by using secondary orchestrator stages inserted after the pipeline stages where relevant data has been acquired.