Approximation Algorithms for Dependency-Aware Rule-Caching in Software-Defined Networks
Jie Wu, Yang Chen, Huanyang Zheng · 2018
Software-defined networks (SDNs) can support finegrained forwarding policies in the underlying switches. The new content addressable memory, Ternary Content Addressable Memory (TCAM), enables fast lookups for matching rules in message forwarding, represented as binary strings with wildcards. However, the cost and power limit the number of matching rules a TCAM can support. Therefore, rule caching is needed to place high-weight (high-hit) rules in the TCAM hardware, while large, but slow, software switches handle cache-miss traffic. We assume that matching these rules form a forest of trees. A rule R' is a descendant of another rule R if R' is a special case of R. Dependent rules are evaluated in a particular matching order: when a rule is included in the cache, all its descendants in the rule set have to be included as well. Our objective is to maximize the number of rule hits, while limiting the number of cached rules. Three greedy rule-caching algorithms are proposed, including two with approximation ratios of 2 and24/5 , respectively. In addition, we propose a dynamic programming solution that is optimal but slow. The efficiency of the proposed approaches are evaluated through real data-driven simulations.