Recent years have witnessed a rapid increase in cyber-attacks through unauthorized accesses and DDoS attacks. Since packet classification is a fundamental technique to prevent such illegal communications, it has gained considerable attention. Packet classification is achieved with a linear search on a classification rule list that represents the packet classification policy. As such, a large number of rules can result in serious communication latency. To decrease this latency, the problem is formalized as optimal rule ordering (ORO). In most cases, this problem aims to find the order of rules that minimizes latency while satisfying the dependency relation of the rules, where rules ri and rj are dependent if there is a packet that matches both ri and rj and their actions applied to packets are different. However, there is a case in which although the ordering violates the dependency relation, the ordering satisfies the packet classification policy. Since such an ordering can decrease the latency compared to an ordering under the constraint of the dependency relation, we have introduced a new model, called relaxed optimal rule ordering (RORO). In general, it is difficult to determine whether an ordering satisfies the classification policy, even when it violates the dependency relation, because this problem contains unsatisfiability. However, using a zero-suppressed binary decision diagram (ZDD), we can determine it in a reasonable amount of time. In this paper, we present a simulated annealing method for RORO which interchanges rules by determining whether rules ri and rj can be interchanged in terms of policy violation using the ZDD. The experimental results show that our method decreases latency more than other heuristics.

For subgraph enumeration problems, very efficient algorithms have been proposed whose time complexities are far smaller than the number of subgraphs. Although the number of subgraphs can exponentially increase with the input graph size, these algorithms exploit compressed representations to output and maintain enumerated subgraphs compactly so as to reduce the time and space complexities. However, they are designed for enumerating only some specific types of subgraphs, e.g., paths or trees. In this paper, we propose an algorithm framework, called the frontier-based search, which generalizes these specific algorithms without losing their efficiency. Our frontier-based search will be used to resolve various practical problems that include constrained subgraph enumeration.

Minato has proposed canonical representation for polynomial functions using zero-suppressed binary decision diagrams (ZBDDs). In this paper, we extend binary moment diagrams (BMDs) proposed by Bryant and Chen to handle variables with degrees higher than l. The experimental results show that this approach is much more efficient than the previous ZBDDs' approach. The proposed approach is expected to be useful for various problems, in particular, for computer algebra.

Ordered Binary Decision Diagrams (OBDDs) are graph-based representations of Boolean functions which are widely used because of their good properties. In this paper, we introduce nondeterministic OBDDs (NOBDDs) and their restricted forms, and evaluate their expressive power. In some applications of OBDDs, canonicity, which is one of the good properties of OBDDs, is not necessary. In such cases, we can reduce the required amount of storage by using OBDDs in some non-canonical form. A class of NOBDDs can be used as a non-canonical form of OBDDs. In this paper, we focus on two particular methods which can be regarded as using restricted forms of NOBDDs. Our aim is to show how the size of OBDDs can be reduced in such forms from theoretical point of view. Firstly, we consider a method to solve satisfiability problem of combinational circuits using the structure of circuits as a key to reduce the NOBDD size. We show that the NOBDD size is related to the cutwidth of circuits. Secondly, we analyze methods that use OBDDs to represent Boolean functions as sets of product terms. We show that the class of functions treated feasibly in this representation strictly contains that in OBDDs and contained by that in NOBDDs.

Recently, various efficient algorithms for solving combinatorial optimization problems using BDD-based set manipulation techniques have been developed. Minato proposed O-suppressed BDDs (ZBDDs) which is suitable for set manipulation, and it is utilized for various search problems. In terms of practical limits of space, however, there are still many search problems which are solved much better by using conventional branch-and-bound techniques than by using BDDs or ZBDDs, while the ability of conventional branch-and-bound approaches is limited by computation time. In this paper, an extension of APPLY operation, named APPRUNE (APply + PRUNE) operation, is proposed, which performs APPLY operation (ZBDD construction) and pruning simultaneously in order to reduce the required space for intermediate ZBDDs. As a prototype, a specific algorithm of APPRUNE operation is shown by assuming that the given condition for pruning is a threshold function, although it is expected that APPRUNE operation will be more effective if more sophisticated condition are considered. To reduce size of ZBDDs in intermediate steps, this paper also pay attention to the number of cared variables. As an application, an exact-minimization algorithm for generalized Reed-Muller expressions (GRMs) is implemented. From experimental results, it is shown that time and memory usage improved 8.8 and 3.4 times, respectively, in the best case using APPRUNE operation. Results on generating GRMs of exact-minimum number of not only product terms but also literals is also shown.