Automated Recovery Plan Generation via Network Traffic Dependency Analysis
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Solution Overview
Problem
Current data management and storage systems face challenges in efficiently managing and recovering complex software applications due to the complexity of compute infrastructure and the need for manual cataloging and sequencing of dependencies between machines, which increases maintenance costs and recovery time.
Innovation Solution
An automated system that monitors network traffic to determine dependencies between machines, enabling the automatic development of recovery plans, detection of inter-system dependencies through statistical sampling, and adjustment of recovery plans based on system configuration changes, thereby reducing the burden of manual sequencing and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual cataloging and sequencing of dependencies between machines is performed, then recovery plans can be developed, but maintenance costs and recovery time increase
Solution Approach 1:
The system automatically monitors network traffic between machines to detect dependencies and generate recovery plans without human intervention. The automated dependency detection mechanism continuously tracks communication patterns, identifies service relationships, and constructs recovery sequences based on observed network behavior, eliminating the need for manual cataloging while maintaining accurate recovery planning
Solution Approach 2:
The system continuously monitors actual network traffic and uses this feedback to refine and update recovery plans. By observing real-time communication patterns between machines, the system adapts its dependency mappings and adjusts recovery sequences dynamically, ensuring accuracy while reducing the time required for manual maintenance
2Reliability
If manual sequencing of recovery steps is performed, then dependency order can be established, but maintenance costs increase
Solution Approach 1:
The automated dependency detection system continuously monitors network traffic and independently determines service relationships between machines. It maintains its own knowledge base of dependencies and generates recovery sequences without requiring human experts to manually document and update these relationships, significantly reducing maintenance costs while preserving sequence accuracy
Solution Approach 2:
The system performs continuous monitoring and automatic updating of dependency relationships as part of its ongoing operation. Rather than requiring periodic manual reviews and updates, the system maintains its dependency knowledge base continuously through automated network traffic analysis, eliminating the need for expensive manual maintenance while ensuring ongoing accuracy
3Extent of automation
If statistical sampling of network traffic is used to detect dependencies, then automated dependency detection is achieved, but measurement precision may be affected
Solution Approach 1:
The system performs continuous statistical sampling of network traffic over extended periods to build robust dependency knowledge. By maintaining continuous monitoring and accumulating sufficient sample data, the system achieves high measurement precision through statistical significance while preserving full automation of the dependency detection process
Solution Approach 2:
The system uses feedback from actual network traffic patterns to validate and refine its dependency detections. By continuously comparing observed communication patterns against its dependency models, the system can verify measurement accuracy and adjust its sampling and detection algorithms to maintain high precision while remaining fully automated
Data Source
AI summary
An automated system monitors network traffic to determine dependencies between different machines. These dependencies can be used to automatically develop a recovery plan for the machines, for example restoring servers in a certain order. This approach can also automatically adjust the recovery plan for changes in system configuration, for example as different servers come online or are taken offline or change their roles.