Reliably Scalable Name Prefix Lookup! Haowei Yuan and Patrick Crowley! Washington University in St. Louis!! ANCS 2015! 5/8/2015!
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1 Reliably Scalable Name Prefix Lookup! Haowei Yuan and Patrick Crowley! Washington University in St. Louis!! ANCS 2015! 5/8/2015!
2 ! My Topic for Today! Goal: a reliable longest name prefix lookup performance baseline with large FIB tables regardless of the specific characteristics of the forwarding rules.! Approach: binary search of hash tables.!! Results: 10 Gbps throughput with 256-byte packets.! On a general-purpose multicore platform.! One billion name prefixes, each containing up to seven components.! 2!
3 Longest Name Prefix Lookup! Named Data Networking (NDN)! Names data rather than hosts.! URL-like hierarchical names: /wustl/content/name/.! Two types of packets: Interest and Data.! Interests are forwarded based on longest name prefix lookup.! IP! Src IP ! Dst IP! ! NDN! Name! /wustl/content/name! (Data)! Challenges! Names are much longer than fixed-length IP addresses.! The forwarding table is much larger than seen with IP.! 3!
4 ! Motivation! Existing solutions have shown encouraging performance.! Most are optimized for or evaluated with a limited number of URL datasets.! URLs may not fully characterize future NDN forwarding rules.! Some schemes require O(k) string lookups in the worst case, where k is the number of components in the prefixes.! 4!
5 ! Existing Solutions! Hash table-based! Existing solutions differ mostly in prefix searching strategy.! Longest first: begins with longest prefix, reduces one component if no match.! Optimization based on component number distribution.! Worst case: O(k) string lookups.! Bloom filter-based! Prefix bloom filter: stores prefixes with the same first-level component in the same cache-line sized bloom filter block.! Possibly requires multiple block expansions.! Worst case: O(k) string lookups.! Trie-based! Naturally requires linear search.! 5!
6 Performance Baseline! Can we provide a performance baseline regardless of the characteristics of the forwarding rules?! Researchers can choose the most efficient namespace design without concern for forwarding performance.! More efficient solutions can be engineered when NDN forwarding tables become available.! 6!
7 !! Binary Search of Hash Tables! Originally proposed for accelerating IP lookup.! Paper: Scalable High Speed IP Routing Lookups, SIGCOMM 1997.! Binary search of hash tables for name prefix lookup.! Organized by the numbers of name components in the prefixes.! Number of string lookups: O(log(k)).! Example: seven hash tables.! 4: /a/b/c/d 2: /a/b 6: /a/b/c/d/e/f 1: /a 3: /a/b/c 5: /a/b/c/d/e 7: /a/b/c/d/e/f/g 7!
8 Binary Search Example! Query: /a/b/c/d/e/ndn! Start from the root hash table node! Proceed to the right subtree if there is a match! Proceed to the left subtree if there is no match! 4: /a/b/c/d 2: /a/b 6: /a/b/c/d/e/f 1: /a 3: /a/b/c 5: /a/b/c/d/e 7: /a/b/c/d/e/f/g 8!
9 ! Implementation! Fingerprint-based hash tables.! Hash computation + memory access.! Hash bucket access.! Name prefix string access.! FP_1! Addr_1!!! FP_n! Addr_n! Hash bucket! /content/name/! Prefix string! String matching strategies when fingerprints match.! Always perform! string matching! Perform string matching only at the end! Worst case! 2 x log(k)! memory accesses! log(k) + 1! memory accesses! Hash function! Non-cryptographic! (CityHash)! Cryptographic! (SipHash)! 9!
10 When Tables Are Large! Large memory requirements and limited access locality.! Software optimization.! Our implementation benefited from existing works.! Cisco s NDN router work, ANCS 2013.! CMU s CuckooSwitch work, CoNext 2013.! Use large pages to reduce TLB misses.! 2 MB or 1 GB pages, rather than the default 4 KB pages.! Software prefetching.! Prefetch hash buckets.! Prefetch only the first-visited hash bucket.! Prefetch all of the k buckets.! 10!
11 Evaluation! Goal:demonstrate line-rate longest name prefix lookup is feasible with modest software optimization.! Datasets.! 1 billion randomly generated name prefixes.! Each name: 7 name components.! Each component: 6 to 8 characters.! Populated the hash tables in two phases to construct the worst case.! A general purpose multicore platform.! Intel Xeon E5-2630, 2.3 GHz, 192 GB of DDR3 DRAM.!! 11!
12 Large Pages & Software Prefetching! Larger pages.! From 4 KB to 2 MB: significant performance improvement.! From 2 MB to 1 GB: performance improved slightly.! Prefetching: small performance improvement.! Larger Pages! Prefetching (1 GB pages)! 12!
13 Datasets with 15 Name Components! Performance degraded due to higher hashing costs.! K hash values were all computed in a row.! Keys (name prefixes) were longer.! More efficient software hash function & hardware hashing units could be employed.! Precalculated hash values! - String lookup bounded by log(k)! - 4 lookups vs. 15 lookups! 13!
14 Evaluation with Real Network Traffic! Implementation.! DPDK as the underlying packet I/O and multicore framework.! Using Pktgen-DPDK to generate packets! Transferred on top of UDP! Packets were distributed by I/O threads to worker threads.! Example: 2 I/O threads & 4 worker threads on one NUMA node.! RTE Rings Core 4 RTE Rings DPDK! Pktgen! (sender)! NIC Port 0 Queue 0 Queue1 Core 0 Core 2 Core 6 Core 8 Core 2 NIC Port 1 DPDK! Pktgen! (receiver)! Core 10 Processor 0 14!
15 Results! One billion names with up to seven name components.! Four worker threads.! CityA! SipE! Prefetch! MPPS! Gbps! MPPS! Gbps! None! 4.1! 9.1! 3.8! 8.4! First! 4.2! 9.4! 4.0! 8.9! All! 4.4! 9.7! 4.1! 9.1! Eight worker threads (two NUMA nodes).! All achieved 10 Gbps throughput.! 15!
16 Conclusion & Future Work! A reliably scalable name prefix lookup design based on binary search of hash tables.! 10 Gbps throughput with one billion synthetic names, each containing up to seven components.!! Also in the paper:! Multicore performance evaluation.! Level pulling, a general method to reduce the number of string lookups.! Are there new characteristics in the URL datasets?! Store prefixes with large number of suffixes in cache.! Future work.! Integrate other NDN data plane components.! Support updates.! Code: 16!
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Reliably Scalable Name Prefix Lookup
Reliably Scalable Name Prefix Lookup Haowei Yuan & Patrick Crowley Washington University Department of Computer Science & Engineering St. Louis, Missouri 63130 {hyuan, pcrowley}@wustl.edu ABSTRACT Name
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