Network Configuration Repair via Weighted MaxSAT Optimization
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Solution Overview
Problem
Current methods for network configuration require manual human intervention to set configuration variables, leading to a high incidence of errors due to the large conceptual gap between requirements and configuration settings, and existing optimization techniques are inadequate for hybrid symbolic, arithmetic, and bit-vector constraints.
Innovation Solution
The method employs a MaxSAT solver within the SMT language to automatically generate or repair network configurations by specifying infinite change costs for requirements and associating weights with configuration variables, ensuring minimal cost changes to restore compliance with network constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual human intervention is used to set configuration variables, then flexibility and control are maintained, but error rate increases due to the large conceptual gap between requirements and configuration settings
Solution Approach 1:
The patent introduces an automated configuration generation system that acts as an intermediary between high-level security requirements and low-level configuration variables. The system translates abstract security policies into concrete configuration settings, eliminating the need for manual intervention while maintaining accuracy. This mediator handles the conceptual gap automatically through constraint-based reasoning and optimization algorithms.
Solution Approach 2:
The configuration system performs self-service by automatically generating and optimizing network configurations based on security requirements without human intervention. The automated system uses weighted MaxSAT solvers and SMT languages to independently resolve configuration variables, ensuring both accuracy and compliance while reducing operational complexity.
2Adaptability or versatility
If traditional optimization techniques such as linear programming are used, then arithmetic constraints are handled effectively, but hybrid symbolic, arithmetic, and bit-vector constraints cannot be processed
Solution Approach 1:
The patent employs a universal SMT solver framework that can handle multiple constraint types (symbolic, arithmetic, and bit-vector) within a single unified system. Rather than using separate specialized solvers for each constraint type, the system integrates them all into one versatile MaxSAT solver that processes hybrid constraints simultaneously, enhancing adaptability without proportionally increasing complexity.
Solution Approach 2:
The system changes the parameter representation by using weighted MaxSAT formulation where different constraint types are encoded with appropriate weights. This parameter transformation allows the solver to handle diverse constraint types uniformly by converting them into a common optimization framework, managing complexity through standardized parameter handling.
3Reliability
If configuration variables are changed to restore compliance, then security requirements are met, but ripple effects may cause additional changes across network requirements
Solution Approach 1:
The patent applies preliminary action by using weighted MaxSAT optimization to predict and account for ripple effects before finalizing configuration changes. The solver evaluates potential changes in advance, calculating their impact on other constraints and optimizing the overall solution to minimize propagation. This preliminary analysis prevents unnecessary changes and reduces the time required for compliance restoration.
Solution Approach 2:
The system implements feedback through the optimization solver that continuously evaluates the impact of configuration changes on all constraints. When a variable is modified to restore compliance, the solver immediately assesses the ripple effects on other requirements and adjusts the solution accordingly, providing real-time feedback to minimize unnecessary changes and accelerate convergence to a compliant state.
Data Source
AI summary
Changing a network configuration to restore compliance to one requirement may invalidate the network compliance with another requirement. A method changes a configuration to restore compliance to all requirements at minimum cost. The requirements are a hybrid of symbolic, arithmetic and bit-vector constraints, so traditional optimization techniques such as linear programming, that work only for purely arithmetic constraints, do not apply. The requirements are represented as SMT (satisfiability-modulo-theory) constraints on configuration variables, and then a weighted Max-SAT solver is used to compute the optimal configuration changes in order to minimize the cost.