Vectored-Bloom Filter for IP Address Lookup: Algorithm and Hardware Architectures

Byun, Hayoung; Li, Qingling; Lim, Hyesook

doi:10.3390/app9214621

Cited by 9 publications

(9 citation statements)

References 33 publications

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“…A functional Bloom filter (FBF) can return a value corresponding to an input as well as membership information regarding whether the input is an element of a set [35], [37]. The FBF has been used as a key-value structure in various network applications owing to its space-efficient characteristics [19], [34], [38]- [40]. The FBF utilizes the fact that various combinations of hash indexes of each key can be used to distinguish one key from other keys.…”

Section: B Functional Bloom Filtermentioning

confidence: 99%

Dynamically Allocated Bloom Filter-Based PIT Architectures

2022

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As a key component in implementing Named Data Networking (NDN), Pending Interest Table (PIT) requires an efficient exact-matching algorithm for a scalable and fast PIT lookup. A Bloom filter (BF) is a memory-efficient data structure for performing exact matching operations. In this paper, three different BF-based PIT architectures are proposed: PIT using functional Bloom filters (FBF-PIT), PIT using counting Bloom filters with return values (rCBF-PIT), and a refined rCBF-PIT with signatures (R-rCBF-PIT). The proposed BF-based PITs incrementally allocate a new BF for storing multiple incoming faces of Interest packets with the same content name. For a Data packet lookup, the proposed PIT architectures simultaneously access every BF structure to find matching faces and delete the faces (i.e., matching Interest packet information). The functional Bloom filter (FBF) used in an FBF-PIT is a key-value data structure that stores values only without keys. However, because the number of non-reusable conflict cells in the FBF increases as the number of stored packets increases in the FBF-PIT, the indeterminable rate increases. To decrease the indeterminable rate, we propose the rCBF-PIT, which uses counting Bloom filters with return values (rCBFs), allowing reusable conflict cells. False positives for Interest packets lead to incorrect deletions that can cause false negatives for incoming Data packets. Because most of the false positives occur in the first BF structure, we finally propose the R-rCBF-PIT, in which the first rCBF is replaced with an rCBF with a signature field. The proposed PITs also provide an aging mechanism using a valid bit and a hit bit for entry expiration. Simulation results show that rCBF-PIT and R-rCBF-PIT both reduce the indeterminable rate by more than 81% compared with FBF-PIT. The results also show that R-rCBF-PIT resolves false negatives caused by incorrect deletions by including the signature fields in the first rCBF. INDEX TERMS Bloom filter, dynamic data structure, Named Data Networking, Pending Interest Table.

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Section: B Functional Bloom Filtermentioning

confidence: 99%

Dynamically Allocated Bloom Filter-Based PIT Architectures

2022

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“…A functional Bloom filter (FBF) [20][21][22] is a variant of a standard Bloom filter that returns a value corresponding to a key as well as its membership. Figure 1d shows a functional Bloom filter with m cells and k hash indexes.…”

Section: Functional Bloom Filtermentioning

confidence: 99%

“…It is shown that the theoretical and simulation results become similar as the load factor increases. For the theoretical results of hash tables, Labels ( 16), (21), and (25) are used for α ≤ 1, and Labels (18), (23), and ( 27) are used for α > 1. In cases of the multi-hash table and the cuckoo hash table, parameters determining the performance of a data structure, such as the number of buckets and the number of hash functions, are the same, and therefore these tables have almost the same theoretical upper bounds.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Comparison on Search Failure between Hash Tables and a Functional Bloom Filter

Byun

Lim

2020

Applied Sciences

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Hash-based data structures have been widely used in many applications. An intrinsic problem of hashing is collision, in which two or more elements are hashed to the same value. If a hash table is heavily loaded, more collisions would occur. Elements that could not be stored in a hash table because of the collision cause search failures. Many variant structures have been studied to reduce the number of collisions, but none of the structures completely solves the collision problem. In this paper, we claim that a functional Bloom filter (FBF) provides a lower search failure rate than hash tables, when a hash table is heavily loaded. In other words, a hash table can be replaced with an FBF because the FBF is more effective than hash tables in the search failure rate in storing a large amount of data to a limited size of memory. While hash tables require to store each input key in addition to its return value, a functional Bloom filter stores return values without input keys, because different index combinations according to each input key can be used to identify the input key. In search failure rates, we theoretically compare the FBF with hash-based data structures, such as multi-hash table, cuckoo hash table, and d-left hash table. We also provide simulation results to prove the validity of our theoretical results. The simulation results show that the search failure rates of hash tables are larger than that of the functional Bloom filter when the load factor is larger than 0.6.

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“…However, the process of finding an LMP is very complex and consumes a lot of cycles. Therefore, many researchers have been working on high-performance IP address lookup [1][2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Prefix Cache with Two-Level Bloom Filters in IP Address Lookup

Kim¹,

Ko²,

Shin³

2020

Applied Sciences

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Prefix caching is one of the notable techniques in enhancing the IP address lookup performance which is crucial in packet forwarding. A cached prefix can match a range of IP addresses, so prefix caching leads to a higher cache hit ratio than IP address caching. However, prefix caching has an issue to be resolved. When a prefix is matched in a cache, the prefix cannot be the result without assuring that there is no longer descendant prefix of the matching prefix which is not cached yet. This is due to the aspect of the IP address lookup seeking to find the longest matching prefix. Some prefix expansion techniques avoid the problem, but the expanded prefixes occupy more entries as well as cover a smaller range of IP addresses. This paper proposes a novel prefix caching scheme in which the original prefix can be cached without expansion. In this scheme, for each prefix, a Bloom filter is constructed to be used for testing if there is any matchable descendant. The false positive ratio of a Bloom filter generally grows as the number of elements contained in the filter increases. We devise an elaborate two-level Bloom filter scheme which adjusts the filter size at each level, to reduce the false positive ratio, according to the number of contained elements. The experimental result shows that the proposed scheme achieves a very low cache miss ratio without increasing the number of prefixes. In addition, most of the filter assertions are negative, which means the proposed prefix cache effectively hits the matching prefix using the filter.

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Vectored-Bloom Filter for IP Address Lookup: Algorithm and Hardware Architectures

Cited by 9 publications

References 33 publications

Dynamically Allocated Bloom Filter-Based PIT Architectures

Dynamically Allocated Bloom Filter-Based PIT Architectures

Comparison on Search Failure between Hash Tables and a Functional Bloom Filter

A Novel Prefix Cache with Two-Level Bloom Filters in IP Address Lookup

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