Network measurement probes the underlying network to support upper-level decisions such as network management, network update, network maintenance, network defense and beyond. Due to the massive, speedy, unpredictable features of network flows, sketches are widely implemented in measurement nodes to record the frequency or estimate the cardinality of flows approximately. At their cores, sketches usually maintain one or multiple counter array(s), and relies on hash functions to select the counter(s) for each flow. Then the space-efficient sketches from the distributed measurement nodes are aggregated to provide statistics of the undergoing flows. Currently, tremendous redesigns and optimizations have been proposed to further improve the sketches for better network measurement performance. However, the existing reviews or surveys mainly focus on one particular aspect of measurement tasks. Researchers and engineers in the network measurement community desire an all-in-one survey which covers the whole processing pipeline of sketch-based network measurement. To this end, we present the first comprehensive survey in this area. We first introduce the preparation of flows for measurement, then detail the most recent investigations of design, aggregation, decoding, application and implementation of sketches for network measurement. To summary the existing efforts, we conduct an in-depth study of the existing literature, covering more than 80 sketch designs and optimization strategies. Furthermore, we conduct a comprehensive analysis and qualitative/quantitative comparison of the sketch designs. Finally, we highlight the open issues for future sketch-based network measurement research.
Set query is a fundamental problem in computer systems. Plenty of applications rely on the query results of membership, association, and multiplicity. A traditional method that addresses such a fundamental problem is derived from Bloom filter. However, such methods may fail to support element deletion, require additional filters or apriori knowledge, making them unamenable to a high-performance implementation for dynamic set representation and query. In this paper, we envision a novel sketch framework that is multi-functional, non-parametric, space efficient, and deletable. As far as we know, none of the existing designs can guarantee such features simultaneously. To this end, we present a general shifting framework to represent auxiliary information (such as multiplicity, association) with the offset. Thereafter, we specify such design philosophy for a hash table horizontally at the slot level, as well as vertically at the bucket level. Theoretical and experimental results jointly demonstrate that our design works exceptionally well with three types of set queries under small memory.
Sketches are widely deployed to represent network flows to support complex flow analysis. Typical sketches usually employ hash functions to map elements into a hash table or bit array. Such sketches still suffer from potential weaknesses upon throughput, flexibility, and functionality. To this end, we propose Ark filter, a novel sketch that stores the element information with either of two candidate buckets indexed by the quotient or remainder between the fingerprint and filter length. In this way, no further hash calculations are required for future queries or reallocations. We further extend the Ark filter to enable capacity elasticity and more functionalities (such as frequency estimation and top-k query). Comprehensive experiments demonstrate that, compared with Cuckoo filter, Ark filter has 2.08×, 1.34×, and 1.68× throughput of deletion, insertion, and hybrid query, respectively; compared with Quotient filter, Ark filter has 4.55×, 1.74×, and 22.12× throughput of deletion, insertion, and hybrid query, respectively; compared with Bloom filter, Ark filter has 2.55× and 2.11× throughput of insertion and hybrid query, respectively.
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