Optimized hash for network path encoding with minimized false positives

Abstract

The Bloom filter is a space efficient randomized data structure for representing a set and supporting membership queries. Bloom filters intrinsically allow false positives. However, the space savings they offer outweigh the disadvantage if the false positive rates are kept sufficiently low. Inspired by the recent application of the Bloom filter in a novel multicast forwarding fabric, this paper proposes a variant of the Bloom filter, the optihash. The optihash introduces an optimization for the false positive rate at the stage of Bloom filter formation using the same amount of space at the cost of slightly more processing than the classic Bloom filter. Often Bloom filters are used in situations where a fixed amount of space is a primary constraint. We present the optihash as a good alternative to Bloom filters since the amount of space is the same and the improvements in false positives can justify the additional processing. Specifically, we show via simulations and numerical analysis that using the optihash the false positives occurrences can be reduced and controlled at a cost of small additional processing. The simulations are carried out for in-packet forwarding. In this framework, the Bloom filter is used as a compact link/route identifier and it is placed in the packet header to encode

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

“…Optihash. Optihash [67] is designed to extend the BF in the context of PSIRP (Publish/Subscribe Internet Routing Parading) which is a new redesign of the whole Internet architecture as far as the physical layer [70]. The in-packet BFs are employed as encoding to identify routes and links between nodes.…”

Selecting Bloom-filter header lengths for secure information centric networking

“…The probability of false positives f can be calculated if the fill factor of a zFilter is known [26,27]: (2) A number of proposals have been introduced to mitigate the effect of false positives [14,22,28]. In [14] the link ID tag (LIT) mechanism is proposed.…”

“…That is, k is the number of hash functions used to create a LID. Typically k is much smaller than m and its value may be selected based to some optimization objectives [22], e.g. m = 256 bits and k = 5 are typically chosen values [14,13].…”

Scalability of information centric networking using mediated topology management

“…In the extreme case an all "1s" FID will match every LID. In practice the parameters of the Bloom filter need to be chosen carefully in order to reduce the false positives, this is discussed further by (Carrea et al, 2014).…”