Dr Hamed Alimohammadi

One of the most important tasks of a network device is packet classification, the support of which has become particularly important with the increasing development of the Internet. In packet classification, the input packets are matched to a set of rules, so that the rule corresponding to each packet is found, and the relevant action(s) is applied to the packet. With the emergence of software-defined networks, many-field rule sets have been introduced, leading to further challenges in the area. One of the best methods of packet classification for today's applications is to use hash tables, because of the fast update support and reasonable searching speed. A well-known packet classification algorithm using hash tables is tuple space search (TSS). TSS extracts the non-wildcard bit positions pattern of the rules, to be used as a hash key in the hash table. Rules with the same pattern are placed in the same cluster. On the other hand, machine learning techniques can be useful to cluster the rule set in many-filed packet classification, due to the increase in the number and type of fields. In this paper, we try to cluster rules using neural gas networks. In this method, there is a specific pattern of bit positions for each cluster of rules, which is non-wildcard in all the rules belonging to the cluster and used as a hash key. The rules of each cluster is hashed into a hash table. In the classification task, the hash key for each cluster is extracted from the packets header according to the non-wildcard pattern of that cluster. Then, using a hashing function, the related rules are investigated to find the highest-priority matching rule. The experimental results show that 93% improvement of throughput on average compared to TSS.

Many-field packet classification is a challenging function of the devices in software-defined networking. In this paper, we propose a new algorithm, which partitions a ruleset in a simple way based on non-wildcard portions of the rules. A portion can be a field or a sub-field. The algorithm uses hash tables as the base data structure. In a partition, all the members have a common non-wildcard portion, which is used as the hash key. It means that only a portion of the rules and headers is used for hashing. It simplifies using the hash table for packet classification, which deals with the ternary vectors. The proposed algorithm supports fast updating as a required feature for most of today's networks. Extensive simulations are conducted to evaluate the algorithm and compare it with well-known algorithms. Results show that the proposed algorithm has a 196% higher throughput and 81% faster update than Tuple Space Search as the base classification algorithm of OpenVSwitch.

In a mobile ad hoc network wireless links are broken frequently because of mobility of nodes. It leads to many routing processes and consequently wastage of the network resources such as energy and bandwidth. Using a routing protocol that reduces the link breakages will result in lower overhead and less packet losses. In this paper we propose a reliable multipath routing protocol that finds stable paths from a source node to a destination node. Our protocol verifies the paths that are being discovered link by link. Each link should satisfy a fairly chosen threshold. If it doesn't, it will not be used in the paths. Therefore, the paths which are stable and reliable will be constructed. Simulation results show that our protocol reduces the routing overhead and the energy consumption at the same time with improving packet delivery ratio and throughput, especially in high mobility scenarios.

Packet classification is one of the main core functions of networking. With the advent of Software-Defined Networking, packet classification has become more challenging by introducing many-field rulesets. In this paper, we propose an algorithm, Clustering-Based Packet Classification (CBPC), which divides ruleset into some clusters using a new hybrid clustering method, based on an innovative bit-level view. Those rules that have more common wildcard and non-wildcard bit positions are put into the same cluster. Each cluster uses common non-wildcard positions of its rules to produce keys for a hash table insertion and query stages. This makes possible to use hash tables without involving difficulties with inserting and querying ternary vectors, because our algorithm converts it to simple binary operations. In fact, we ignore a portion of information of the rules in key production for the hash tables. It overcomes the problem of extending wildcards to all of the possible values. It is true that some information is lost but it is covered by full matching at the hash table entries. We propose two versions for CBPC, online and offline. The online version supports update, which is an important requirement for today's packet classification algorithms. The proposed algorithm is evaluated and compared with some well-known and state-of-the-art algorithms by extensive simulations. The results show that Online-CBPC achieves 197% higher throughput and 64% faster update than Tuple Space Search, OpenVSwitch standard algorithm, while using almost the same amount of memory.