Abstract: A system and method is provided for verifying an access-control policy against a particular constraint for a multi-step operation. In disclosed embodiments, the method includes expressing the access-control policy as a first quantifier-free form (QFF) constraint and identifying the particular constraint as a second QFF constraint. The method also includes identifying an operation vector and providing copies of the operation vector associated with steps in the multi-step operation. The method also includes determining a third QFF constraint using the first QFF constraint, the second QFF constraint, and the copies of the operation vector. The method also includes solving the third QFF constraint to determine a solution and outputting a result of the solving.

Abstract: A system and method is provided for verifying an access-control policy against a particular constraint for a multi-step operation. In disclosed embodiments, the method includes expressing the access-control policy as a first quantifier-free form (QFF) constraint and identifying the particular constraint as a second QFF constraint. The method also includes identifying an operation vector and providing copies of the operation vector associated with steps in the multi-step operation. The method also includes determining a third QFF constraint using the first QFF constraint, the second QFF constraint, and the copies of the operation vector. The method also includes solving the third QFF constraint to determine a solution and outputting a result of the solving.

Abstract: A system and method is provided for verifying an access-control policy against a particular constraint for a multi-step operation. In disclosed embodiments, the method includes expressing the access-control policy as a first quantifier-free form (QFF) constraint and identifying the particular constraint as a second QFF constraint. The method also includes identifying an operation vector and providing copies of the operation vector associated with steps in the multi-step operation. The method also includes determining a third QFF constraint using the first QFF constraint, the second QFF constraint, and the copies of the operation vector. The method also includes solving the third QFF constraint to determine a solution and outputting a result of the solving.

Abstract: A method for solving the reconfiguration planning problem transforms the requirement that an invariant is always preserved into a constraint on the times at which the configuration parameters change. The method then solves this constraint to obtain the required reconfiguration plan. When this constraint is strengthened with a synthesis constraint, it is ensured that any final configuration that is computed is also reachable. An extension of the method allows parameters to take on multiple intermediate values.

Abstract: A solution to the problem of automated policy generation for mobile ad hoc networks includes an optimization-based, utility-driven approach aimed at generating optimal policies with respect to the given network objectives. The combination of optimization heuristics and network simulation is used to solve the problem. Specifically, the problem of automated generation of network management policies based on available network plans and related information is solved by converting the policy generation into the following optimization problem: given network information and objectives as input, generate optimal policies as output. The optimization process is guided by a utility function based on performance evaluation criteria reflecting the network objectives.

Abstract: A method is provided for analyzing the semantic content of network configuration files, comprising the steps of accessing configuration files associated with corresponding network components, the files containing commands that define the configuration of those components; transforming the commands into a structural database based, at least in part, on a non-grammatical analysis of the commands, wherein the structure of the commands is represented as the structural database; and constructing a semantic database of the configuration files by querying the structural database.

Abstract: A system and method provides a solution to the problem of applying end-to-end requirements of connectivity, security, reliability and performance to configure a network and ultimately assign network components to the network. All requirements are modeled as constraints and a constraint solver does the resolution. Not every constraint to be solved is solved by the model-finder. Instead, we “factor away” subsets of a constraint that can be efficiently solved via a special-purpose constraint solver, such as an SQL/Prolog engine, linear programming system, or even an algorithm, leaving behind a constraint that truly requires the power of model-finding, and that is often efficiently solvable by existing model-finders. Such constraints are compiled into quantifier-free constraints that are Boolean combinations of constraints of two forms x=y and x=c where x, y are variables and c is a constant. Such constraints can be efficiently solved by modern SAT-based model-finders.

Abstract: A solution to the problem of automated policy generation for mobile ad hoc networks includes an optimization-based, utility-driven approach aimed at generating optimal policies with respect to the given network objectives. The combination of optimization heuristics and network simulation is used to solve the problem. Specifically, the problem of automated generation of network management policies based on available network plans and related information is solved by converting the policy generation into the following optimization problem: given network information and objectives as input, generate optimal policies as output. The optimization process is guided by a utility function based on performance evaluation criteria reflecting the network objectives.

Abstract: A method for solving the reconfiguration planning problem transforms the requirement that an invariant is always preserved into a constraint on the times at which the configuration parameters change. The method then solves this constraint to obtain the required reconfiguration plan. When this constraint is strengthened with a synthesis constraint, it is ensured that any final configuration that is computed is also reachable. An extension of the method allows parameters to take on multiple intermediate values.

Abstract: A system and method is provided for verifying an access-control policy against a particular constraint for a multi-step operation. In disclosed embodiments, the method includes expressing the access-control policy as a first quantifier-free form (QFF) constraint and identifying the particular constraint as a second QFF constraint. The method also includes identifying an operation vector and providing copies of the operation vector associated with steps in the multi-step operation. The method also includes determining a third QFF constraint using the first QFF constraint, the second QFF constraint, and the copies of the operation vector. The method also includes solving the third QFF constraint to determine a solution and outputting a result of the solving.

Abstract: A method is provided for analyzing the semantic content of network configuration files, comprising the steps of accessing configuration files associated with corresponding network components, the files containing commands that define the configuration of those components; transforming the commands into a structural database based, at least in part, on a non-grammatical analysis of the commands, wherein the structure of the commands is represented as the structural database; and constructing a semantic database of the configuration files by querying the structural database.

Abstract: A solution to the problem of automated policy generation for mobile ad hoc networks includes an optimization-based, utility-driven approach aimed at generating optimal policies with respect to the given network objectives. The combination of optimization heuristics and network simulation is used to solve the problem. Specifically, the problem of automated generation of network management policies based on available network plans and related information is solved by converting the policy generation into the following optimization problem: given network information and objectives as input, generate optimal policies as output. The optimization process is guided by a utility function based on performance evaluation criteria reflecting the network objectives.

Abstract: A system and method provides a solution to the problem of applying end-to-end requirements of connectivity, security, reliability and performance to configure a network and ultimately assign network components to the network. All requirements are modeled as constraints and a constraint solver does the resolution Not every constraint to be solved is solved by the model-finder. Instead, we “factor away” subsets of a constraint that can be efficiently solved via a special-purpose constraint solver, such as an SQL/Prolog engine, linear programming system, or even an algorithm, leaving behind a constraint that truly requires the power of model-finding, and that is often efficiently solvable by existing model-finders. Such constraints are compiled into quantifier-free constraints that are Boolean combinations of constraints of two forms x=y and x=c where x, y are variables and c is a constant. Such constraints can be efficiently solved by modern SAT-based model-finders.

Abstract: A solution to the problem of automated policy generation for mobile ad hoc networks includes an optimization-based, utility-driven approach aimed at generating optimal policies with respect to the given network objectives. The combination of optimization heuristics and network simulation is used to solve the problem. Specifically, the problem of automated generation of network management policies based on available network plans and related information is solved by converting the policy generation into the following optimization problem: given network information and objectives as input, generate optimal policies as output. The optimization process is guided by a utility function based on performance evaluation criteria reflecting the network objectives.