arXiv subjects

Ripon Patgiri

Publications and source records attributed to Ripon Patgiri.

21 records · Page 2Linked to original sources

arXiv2019

scaleBF: A High Scalable Membership Filter using 3D Bloom Filter

Bloom Filter is extensively deployed data structure in various applications and research domain since its inception. Bloom Filter is able to reduce the space consumption in an order of magnitude. Thus, Bloom Filter is used to keep information of a very large scale data. There are numerous variants of Bloom Filters available, however, scalability is a serious dilemma of Bloom Filter for years. To solve this dilemma, there are also diverse variants of Bloom Filter. However, the time complexity and space complexity become the key issue again. In this paper, we present a novel Bloom Filter to address the scalability issue without compromising the performance, called scaleBF. scaleBF deploys many 3D Bloom Filter to filter the set of items. In this paper, we theoretically compare the contemporary Bloom Filter for scalability and scaleBF outperforms in terms of time complexity.

cs.DS↗

arXiv2018

Preventing DDoS using Bloom Filter: A Survey

Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, loss of business revenue. Therefore, DDoS is a grand challenge to defeat. There are numerous mechanism to defend DDoS, however, this paper surveys the deployment of Bloom Filter in defending a DDoS attack. The Bloom Filter is a probabilistic data structure for membership query that returns either true or false. Bloom Filter uses tiny memory to store information of large data. Therefore, packet information is stored in Bloom Filter to defend and defeat DDoS. This paper presents a survey on DDoS defending technique using Bloom Filter.

cs.NI↗

arXiv2017

MapReduce Scheduler: A 360-degree view

Undoubtedly, the MapReduce is the most powerful programming paradigm in distributed computing. The enhancement of the MapReduce is essential and it can lead the computing faster. Therefore, here are many scheduling algorithms to discuss based on their characteristics. Moreover, there are many shortcoming to discover in this field. In this article, we present the state-of-the-art scheduling algorithm to enhance the understanding of the algorithms. The algorithms are presented systematically such that there can be many future possibilities in scheduling algorithm through this article. In this paper, we provide in-depth insight on the MapReduce scheduling algorithm. In addition, we discuss various issues of MapReduce scheduler developed for large-scale computing as well as heterogeneous environment.

cs.DC↗