kBF: Towards Approximate and Bloom Filter based Key-Value Storage for Cloud Computing Systems

Xiong, Sisi; Yao, Yanjun; Li, Shuangjiang; Cao, Qing; He, Tian; Qi, Hairong; Tolbert, Leon M.; Li, Fangxing

doi:10.1109/tcc.2014.2385063

Cited by 17 publications

(8 citation statements)

References 24 publications

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“…In consequence, graph databases with few vertices and more edges are compressed to a relatively lower size (BSBM, DyLDO-ext in Table 1). In light of this data overhead, future extensions may be to further compress L2 by using approximative data structures such as Bloom filters [28]. However, as pointed out by Fan et al [9], many real-life applications require exact matches.…”

Section: Countmentioning

confidence: 99%

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Blume

Richerby

Scherp

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Graph summarization is the task of finding condensed representations of graphs such that a chosen set of (structural) subgraph features in the graph summary are equivalent to the input graph. Existing graph summarization algorithms are tailored to specific graph summary models, only support one-time batch computation, are designed and implemented for a specific task, or evaluated using static graphs. Our novel, incremental, parallel algorithm addresses all these shortcomings. We support various structural graph summary models defined in our formal language FLUID. All graph summaries defined with FLUID can be updated in time O(∆ •d k), where ∆ is the number of additions, deletions, and modifications to the input graph, d is its maximum degree, and k is the maximum distance in the subgraphs considered. We empirically evaluate the performance of our algorithm on benchmark and real-world datasets. Our experiments show that, for commonly used summary models and datasets, the incremental summarization algorithm almost always outperforms their batch counterpart, even when about 50% of the graph database changes. The source code and the experimental results are openly available for reproducibility and extensibility.

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Section: Countmentioning

confidence: 99%

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Blume

Richerby

Scherp

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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“…We analyze the search failure probability of the proposed structure composed of two FBFs. In this section, S F in (1) represents the event of a search failure for the proposed structure, and the P (I) and P (F P ) in (1) are to be formulated. The P (I|A) and P (I|A c ) probabilities in (2), (4), and (5) need to be modified for the proposed structure.…”

Section: B Theoretical Analysis Of Search Failure Probabilitymentioning

confidence: 99%

“…K ey-value data structures have been used in various applications, such as Facebook, Twitter, Google's BigTable, and Amazon's Dynamo [1]- [4]. As the most representative key-value data structure, a hash table stores both keys and their return values.…”

Section: Introductionmentioning

confidence: 99%

Addition of a Secondary Functional Bloom Filter

et al. 2020

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Key-value data structures have been extensively used in various applications. When a large amount of data needs to be compactly stored in a fixed memory size, a functional Bloom filter is a space-efficient key-value structure. In this paper, we propose a 2-stage functional Bloom filter structure composed of a primary functional Bloom filter and a secondary functional Bloom filter to resolve the indeterminables produced from the primary functional Bloom filter. We analytically present the memory ratio allocated for each of the two Bloom filters to achieve the lowest search failure rate. The analytical result is validated through experiments, thereby demonstrating that the optimal performance is realized when the secondary functional Bloom filter uses 3% of the total memory.

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“…A key-value data structure that returns a value corresponding to an input key has been used in many fields [1][2][3]. Various network applications, such as Internet Protocol (IP) address lookup, packet classification, name lookup in Named Data Networking (NDN), traffic classification in Software-Defined Networking (SDN), and cloud computing [4][5][6][7][8], use key-value structures for data storage.…”

Section: Introductionmentioning

confidence: 99%

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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kBF: Towards Approximate and Bloom Filter based Key-Value Storage for Cloud Computing Systems

Cited by 17 publications

References 24 publications

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Addition of a Secondary Functional Bloom Filter

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

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