A genetic algorithm is applied for the optimal design of skeletal building structures accounting for discrete sizing, geometrical and topological variables. An approxirtiate designfitness evaluation technique is investigated with the aim to improve the numerical efficiency of the genetic search. Two design examples are presented to illustrate the principles involved.

No abstract

Packet classification is a core function in network and security systems; hence, hardware-based solutions, such as packet classification accelerator chips or T-CAM (Ternary Content Addressable Memory), have been widely adopted for high-performance systems. With the rapid improvement of general hardware architectures and growing popularity of multi-core multi-threaded processors, software-based packet classification algorithms are attracting considerable attention, owing to their high flexibility in satisfying various industrial requirements for security and network systems. For high classification speed, these algorithms internally use large tables, whose size increases exponentially with the ruleset size; consequently, they cannot be used with a large rulesets. To overcome this problem, we propose a new software-based packet classification algorithm that simultaneously supports high scalability and fast classification performance by merging partition decision trees in a search table. While most partitioning-based packet classification algorithms show good scalability at the cost of low classification speed, our algorithm shows very high classification speed, irrespective of the number of rules, with small tables and short table building time. Our test results confirm that the proposed algorithm enables network and security systems to support heavy traffic in the most effective manner.Index Terms-Packet classification, partitioning, cache-aware table structure, integrated inter-and intra-table search.

No abstract