System design tools are often only available as input–output blackboxes: for a given design as input, they compute an output representing system behavior. Blackboxes are intended to be run in the forward direction. This article presents a new method of solving the “inverse design problem,” namely, given requirements or constraints on output, find an input that also optimizes an objective function. This problem is challenging for several reasons. First, blackboxes are not designed to be run in reverse. Second, inputs and outputs can be discrete and continuous. Third, finding designs concurrently satisfying a set of requirements is hard because designs satisfying individual requirements may conflict with each other. Fourth, blackbox evaluations can be expensive. Finally, evaluations can sometimes fail to produce an output due to nonconvergence of underlying numerical algorithms. This article presents CNMA, a new method of solving the inverse problem that overcomes these challenges. CNMA tries to sample only the part of the design space relevant to solving the inverse problem, leveraging the power of neural networks, mixed-integer linear programs, and a new learning-from-failure feedback loop. This article also presents a parallel version of CNMA that improves the efficiency and quality of solutions over the sequential version and tries to steer it away from local optima. CNMA’s performance is evaluated against conventional optimization methods for seven nonlinear design problems of 8 (two problems), 10, 15, 36 and 60 real-valued dimensions and one with 186 binary dimensions. Conventional methods evaluated are stable, off-the-shelf implementations of the Bayesian optimization with the Gaussian Processes, Nelder–Mead, and Random Search. The first two do not even produce a solution for problems that are high dimensional, having both discrete and continuous variables or whose blackboxes fail to return values for some inputs. CNMA produces solutions for all problems. When conventional methods do produce solutions, CNMA improves upon their performance by 1%–87%.

Network operators often need to change their routing policy in response to network failures, new load balancing strategies, or stricter security requirements. While several recent works have aimed at solving this problem, they all assume that a fast and conveniently dimensioned out-of band network is available to communicate with any device. Unfortunately, such a parallel network is often not practical. This paper presents a technique for performing such updates in-band: it enables reconfiguration control messages to be sent directly within the fast production network. Performing such updates is hard because intermediate configurations can lock out the controller from devices before they are updated. Thus, updates have to be carefully sequenced. Our technique also minimizes the total update time by updating the network in parallel, whenever possible. Our technique takes into account in-band middle boxes, such as firewalls. We have implemented our framework using Integer Linear Programming, and experimentally validated it on problems of realistic scale.

Firewalls are widely deployed to safeguard the security of networks and it is critical for enterprise networks to have firewalls to prevent malicious attacks and to guarantee the normal functioning of the network. Firewalls prevent dangerous packets from entering the inner network by looking up the Access Control List (ACL) to permit or drop certain packets. However, ACLs often suffer from redundancy problems, which can degrade the performance of firewalls and the network. The contribution of this paper is threefold: 1) we present a Boolean Satisfiability (SAT) based technique that can compare the equivalence and inclusion relationship between two firewalls, which is very valuable for the testing between a given firewall and an optimized one, 2) we present a technique to discover redundancies within a firewall, and 3) we formulate the ACL optimization problem as a Quantified Boolean Formula problem (QBF) and explore its practical application using a QBF solver.

The nine papers in this special issue focus on network infrastructure configuration and some of the problems encountered in the areas of specification, diagnosis, repair, synthesis, and anonymization.

We present alternatives for the integration of Mobile Internet Protocol (Mobile IP) versions with IP Security (IPSec) to secure IP communications over the wireless link. We focus on network layer security and provisioning of security services while roaming to a foreign network, without having to trust the foreign network. Additionally, such a scheme does not require the correspondent host (CH) to participate in protecting the wireless link. We note, however, that end-to-end security can still be provisioned, should the end user and CH have the capability to do so. Finally, our implementation allows seamless mobility at the network layer, while supporting higher layer services and security features.

Traditional network fault management systems diagnose hard, localized errors such as fiber cuts or hardware/software component failures. It is quite possible, however, that network components work correctly yet end-to-end services do not. This happens if there are configuration errors, i.e., configuration parameters of components are set to incorrect values. Configuration is a fundamental operation for integrating components to implement end-to-end services. Configuration errors are frequent because transforming end-to-end service requirements into configurations is inherently difficult: in realistic networks there are many components, configuration parameters, values, protocols and requirements. Yet, such transformation is largely, manually performed. This paper describes a toolkit called Service Grammar to diagnose configuration errors. The toolkit is illustrated in the context of an IP virtual private network with routing and security services. It is based on the following assumptions: (1) every component has configuration parameters which can be set to definite values; (2) these values remain fixed during the normal operation of a component; and (3) the set of values of all configuration parameters in a network, called the configuration vector, determines the behavior of the network as a whole.