Packet Classification via Improved Space Decomposition Techniques

P ack et Classification is a common task in moder n Inter net r outers. The goal is to classify pack ets into "classes" or "flo ws" according to some ruleset that looks at multiple fields of each pack et. Differ entiated actions can then be applied to the traffic depending on the r esult of the classification. Ev en though rulesets can be expr essed in a r elati v ely compact way by using high le v el languages, the r esulting decision tr ees can partition the sear ch space (the set of possible attrib ute v alues) in a potentially v ery lar ge ( and mor e) number of r egions. This calls f or methods that scale to such lar ge pr oblem sizes, though the only scalable pr oposal in the literatur e so far is the one based on a F at In v erted Segment T r ee [1 ]. In this paper we pr opose a new geometric technique called G-filter f or pack et classification on dimensions. G-filter is based on an impr o v ed space decomposition technique. In addition to a theor etical analysis sho wing that classification in G-filter has time complexity and slightly super -linear space in the number of rules, we pr o vide thor ough experiments sho wing that the constants in v olv ed ar e extr emely small on a wide range of pr oblem sizes, and that G-filter impr o v e the best r esults in the literatur e f or lar ge pr oblem sizes, and is competiti v e f or small sizes as well

Packet Classification via Improved Space Decomposition Techniques

Packet Classification is a common task in modern Internet routers. In a nutshell, the goal is to classify packets into ``classes\u27\u27 or ``flows\u27\u27 according to some ruleset that looks at multiple fields of each packet. Differentiated actions can then be applied to the traffic depending on the result of the classification. One way to approach the task is to model it as a point location problem in a multidimensional space, partitioned into a large number of regions, (up to $10^6$ or more, generated by the number of possible paths in the decision tree resulting from the specification of the ruleset). Many solutions proposed in the literature not to scale well with the size of the problem, with the exception of one based on a Fat Inverted Segment Tree. In this paper we propose a new geometric filtering technique, called {em g-filter}, which is competitive with the best result in the literature, and is based on an improved space decomposition technique. A theoretical worst case asymptotic analysis shows that classification in {em g-filter} has $O(1)$ time complexity, and space complexity close to linear in the number of rules. Additionally, thorough experiments show that the constants involved are extremely small on a wide range of problem sizes, and improve the best results in the literature. Finally, the g-filter method is not limited to 2-dimensional rules, but can handle any number of attributes with only a moderate increased overhead per additional dimension

Packet Classification Algorithms: From Theory to Practice

Abstract—During the past decade, the packet classification problem has been widely studied to accelerate network applications such as access control, traffic engineering and intrusion detection. In our research, we found that although a great number of packet classification algorithms have been proposed in recent years, unfortunately most of them stagnate in mathematical analysis or software simulation stages and few of them have been implemented in commercial products as a generic solution. To fill the gap between theory and practice, in this paper, we propose a novel packet classification algorithm named HyperSplit. Compared to the well-known HiCuts and HSM algorithms, HyperSplit achieves superior performance in terms of classification speed, memory usage and preprocessing time. The practicability of the proposed algorithm is manifested by two facts in our test: HyperSplit is the only algorithm that can successfully handle all the rule sets; HyperSplit is also the only algorithm that reaches more than 6Gbps throughput on the Octeon3860 multi-core platform when tested with 64-byte Ethernet packets against 10K ACL rules. Keywords-algorithm; classification; multi-core; performance I

Packet classification via improved space decomposition techniques

Packet classification is a common task in modern Internet routers. The goal is to classify packets into "classes" or "flows" according to some ruleset that looks at multiple fields of each packet. Differentiated actions can then be applied to the traffic depending on the result of the classification. Even though rulesets can be expressed in a relatively compact way by using high level languages, the resulting decision trees can partition the search space (the set of possible attribute values) in a potentially very large (106 and more) number of regions. This calls for methods that scale to such large problem sizes, though the only scalable proposal in the literature so far is the one based on a fat inverted segment tree (A. Feldmann and S. Muthukrishnan). In this paper we propose a new geometric technique called G-filter for packet classification on d dimensions. G-filter is based on an improved space decomposition technique. In addition to a theoretical analysis showing that classification in G-filter has O(1) time complexity and slightly super-linear space in the number of rules, we provide thorough experiments showing that the constants involved are extremely small on a wide range of problem sizes, and that G-filter improve the best results in the literature for large problem sizes, and is competitive for small sizes as well

Packet classification via improved space decomposition techniques

Abstract — Packet Classification is a common task in modern Internet routers. The goal is to classify packets into “classes ” or “flows ” according to some ruleset that looks at multiple fields of each packet. Differentiated actions can then be applied to the traffic depending on the result of the classification. Even though rulesets can be expressed in a relatively compact way by using high level languages, the resulting decision trees can partition the search space (the set of possible attribute values) in a potentially very large (¢¤£¦¥ and more) number of regions. This calls for methods that scale to such large problem sizes, though the only scalable proposal in the literature so far is the one based on a Fat Inverted Segment Tree [1]. In this paper we propose a new geometric technique called G-filter for packet classification on § dimensions. G-filter is based on an improved space decomposition technique. In addition to a theoretical analysis showing that classification in G-filter has ¨�©�¢� � time complexity and slightly super-linear space in the number of rules, we provide thorough experiments showing that the constants involved are extremely small on a wide range of problem sizes, and that G-filter improve the best results in the literature for large problem sizes, and is competitive for small sizes as well. Index Terms — System design I