A BLOOM FILTERS BASED DATA MANAGEMENT WITH ERROR DETECTION AND CORRECTION
Dr.B.Bazeer Ahamed1, S.Nageswari2
2Associate professor, Department of Computer science and Engineering, Balaji Institute of Technology &
Science, India
2Department of Computer Science, Bharath Niketan Engineering College Theni, India; Email: [email protected]
*Corresponding author email: [email protected] ABSTRACT
Bloom filters were used to reduce the delay in networking and computing applications when a hard and fast membership take a look at is to be applied. Error sources can have an effect on the conduct of bloom filters ensuing in a wrong final result of this membership test and a probable impact inside the device’s output. single occasion transients are a form of transient errors altering the operation of combinational good judgment. a unmarried occasion brief affecting the hash technology logic of a hardware- implemented bloom filter out can produce mistakes including fake negatives. The principle drawback of the existing machine is that counting bloom filter out and compressed bloom filter. In order to overcome the comparator approach. The compares incoming data and storage statistics.
KEYWORDS: Bloom filters, Error detection, counter filter, compressed filter ,modelsim, comparator.
I. INTRODUCTION
Bloom filters are probabilistic data structures used in computing and networking applications when it is necessary to check if an
element belongs to a set[1-14]. They can produce false positives, but false negatives are not possible. Some examples of the application of BFs include data storage systems such as Google Big table , different networking scenarios and privacy protection systems. When the system implemented in hardware, it may suffer reliability problems such as soft errors and manufacturing defects. One of these types of errors are Single Event Transients , which are temporary errors that may affect the combinational logic of a system. SETs may be caused by a number of reasons, including radiation sources. Hash generation circuits included in a Bloom filter may suffer from temporary errors due to SETs. An SET can cause a membership query to return the wrong result, indicating that an element is in the set when it is not or stating that an element is absent when it is in the set . As explained before, false positives are allowed in Bloom filters, and the effect would only be a small reduction in performance.
However, false negatives may cause the system to misbehave and they should be prevented. To do so, a simple approach could be to replicate the circuitry using traditional schemes such as Triple Modular Redundancy and Dual Modular Redundancy. However, in many cases, the increase in power and area consumption of these schemes makes necessary to find other approaches. The protection of Bloom filters against errors has been the studied in different works. In , permanent errors produced in hash
generation circuits of a Bloom filter were studied and a solution oriented to this type of errors was proposed. The scheme uses additional spare hash units that are activated when a possible false negative occurs to verify if there is a faulty unit to replace it with one of the spare ones. In , a scheme to protect the content of the Bloom filter is presented[15-35]. The scheme focuses in the memory protection against single event upsets and does not provide solutions for the hash circuitry. It can be combined with the schemes presented in this paper to build a fault tolerant solution. Bloom filters have also been used to identify defects or errors in other structures such as nano-memories , Content Addressable Memories, or the data set associated with the filter. This paper presents and compares different schemes to protect Bloom filter implementations against SETs. The rest of the paper is organized as follows: first, a descriptions of the architecture and operation of Bloom Filters.
II. RELATED WORK
Bloom filters have an essential role in network services and consequently the growing importance of operations such as information retrieval, distributed databases, packet content inspection, and cooperative caching results in the wide applications of Bloom filters that provide set-membership queries based on a relatively easy hardware implementation. The main design tradeoffs are the number of hash functions used, the size of the filter and the error rate counting Bloom filter generalizes a Bloom filter data structure so as to allow membership queries on a set that can be changing dynamically via insertions and deletions. A pipelined Bloom filter consists of two groups of hash functions.
The first stage always computes the hash values.
By contrast, the second stage of hash functions only compute the hash values if in the first stage there is a match between the input and the signature sought. compressing Bloom filters might lead to significant bandwidth savings at the cost of higher memory requirements and some additional computation time to compress the filter that is sent across the network. We do not detail here all theoretical and practical issues analyzed.
III. PROPOSED WORK
A bloom filter is a binary data management filter used to compare a incoming data with data stored in memory. In my proposed method I am going to use comparator technique. If I give a value which already store means it will not add, if it is not have the value will add.
Set 1 compares the N-bit operands A and B bit-by-bit, using a single level of N ψ type cells. The ψ type cells provide a termination flag Dk to cells in sets 2 and 4, indicating whether the computation should terminate. these cells compute (where 0 ≤ k ≤ N − 1).
ψ: Dk = Ak Bk. (1.1) Set 2 consists of Ʃ2 type cells, which combine the termination flags for each of the four ψ type cells from set 1 (each 2-type cell combines the termination flags of one 4-b partition) using OR-logic to limit the fan-in and fan-out to a maximum of four. The Ʃ2 type cells either continue the comparison for bits of lesser significance if all four inputs are 0s, or terminate the comparison if a final decision can be made. For 0 ≤ m ≤ N/4–1, there is a total of N/4 Ʃ2 type cells, all functioning in parallel .
Set 3 consists of Ʃ3 type cells, which are similar to Ʃ2-type cells, but can have more logic levels, different inputs, and carry different triggering points. A Ʃ3-type cell provides no comparison functionality; the cell’s sole purpose is to limit the fan-in and fan-out regardless of operand bit-width. To limit the Ʃ3-type cell’s local interconnect to four, the number of levels in set 3 increases if the fan-in exceeds four. Set 3 provides functionality similar to set 2 using the same NOR logic to continue or terminate the bitwise comparison activity. If the comparison is terminated, set 3 signals set 4 to set the left bus and right bus bits to 0 for all bits of lower
significance. For 0 ≤ m ≤ N/4 − 1, there is a total of N/4 Ʃ3-type cells per level, with cell function and number of levels as
∑3:C3,m= (1.2) LevelSset3 = ([log16(N)]) (1.3)
From left to right, the first four Ʃ3-type cells in set 3 combine the partition comparison outcomes from the one, two, three, and four partitions of set 2. Since the fourth Ʃ3-type cell has a fan-in of four, the number of levels in set 3 increases and set 3’s fifth Ʃ3-type cell combines the comparison outcomes of the first 16 MSBs with a fan-in of only two and a fan-out of one.
1.1 Details of Implementation Architecture
The simulation-based analysis of leakage power dissipation showed that, whereas the percentage contribution of leakage power increases with each new technology generation, the increase effect is not significant enough to nullify the savings in dynamic power dissipation in near-future technologies. Future work will include additional circuit optimizations to further reduce the power dissipation by adapting dynamic and analog implementations for the comparator resolution module and a high-speed zero-detector circuit for the decision module.
Given that our comparator is composed of two balanced timing modules, the structure can be divided into two or more pipeline stages with balanced delays, based on a set structure, to effectively increase the comparison throughput at the expense of increased power and latency.
SIMULATION RESULTS:
Bloom filters output:
Figure 1.2 : Data Insertion
Figure 1.3 : Bloom filters output
Figure 1.4 : Data Rejection IV. CONCLUSIONS
A new application of BFs has been proposed. We have analysis and simulated by comparator method. We believe the comparator with suitable for many tools. Comparator bloom filter can reduce the delay and increases the speed. My future work is to design the memory efficient.
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