TupleChain: Fast Lookup of OpenFlow Table with Multifaceted Scalability

TL;DR

The paper tackles the OpenFlow flow lookup problem under very large multifield rule sets and rapid online updates. It introduces TupleChain, a framework that partitions rules into tuples, organizes them into chains, and leverages a tuple graph with markers and hints to guide a binary-search style lookup while keeping updates localized. The Extended TupleChain (ETC) further improves practical performance by grouping nearby chains to reduce lookup depth and memory usage. Theoretical analysis yields near-logarithmic worst-case lookup and amortized update costs, and extensive experiments demonstrate robust scalability to tens of millions of rules, high update rates, and hundreds of match fields, with sustained high throughput. These results suggest a scalable, high-performance solution for OpenFlow table lookup in data-center networks.

Abstract

OpenFlow switches are fundamental components of software defined networking, where the key operation is to look up flow tables to determine which flow an incoming packet belongs to. This needs to address the same multi-field rule-matching problem as legacy packet classification, but faces more serious scalability challenges. The demand of fast on-line updates makes most existing solutions unfit, while the rest still lacks the scalability to either large data sets or large number of fields to match for a rule. In this work, we propose TupleChain for fast OpenFlow table lookup with multifaceted scalability. We group rules based on their masks, each being maintained with a hash table, and explore the connections among rule groups to skip unnecessary hash probes for fast search. We show via theoretical analysis and extensive experiments that the proposed scheme not only has competitive computing complexity, but is also scalable and can achieve high performance in both search and update. It can process multiple millions of packets per second, while dealing with millions of on-line updates per second at the same time, and its lookup speed maintains at the same level no mater it handles a large flow table with 10 million rules or a flow table with every entry having as many as 100 match fields.

Figures (16)

• Figure 1: simplified 2-field rules
• Figure 2: constructed tuples
• Figure 3: lookup with TSS.
• Figure 4: lookup with TupleChain.
• Figure 5: a tuple graph

Theorems & Definitions (7)

• Theorem 1
• proof
• Corollary 1.1
• proof
• Corollary 1.2
• Theorem 2
• Theorem 3