BC-BSP: A BSP-Based Parallel Iterative Processing System for Big Data on Cloud Architecture

Bao, Yukun; Wang, Zhigang; Gu, Yu; Yu, Ge; Leng, Fangling; Zhang, Hongxu; Chen, Bairen; Deng, Chao; Guo, Leitao

doi:10.1007/978-3-642-40270-8_3

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…The frequent subgraph mining algorithm in this paper is implemented on the BC-BSP system [31]. BC-BSP is an open-source BSP [32] framework that leverages disk-cached data, exhibits robust fault tolerance, and supports multiple data input sources.…”

Section: Bc-bsp Calculation Modelmentioning

confidence: 99%

FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP

Leng,

Li,

Bao

et al. 2024

Applied Sciences

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As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the complexity of frequent subgraph mining and proposes a frequent subgraph mining (FSM) algorithm. This FSM algorithm is developed within a distributed graph iterative system, designed for the Big Cloud (BC) environment of the China Mobile Corp., and is based on the bulk synchronous parallel (BSP) model, named FSM-BC-BSP. Its aim is to address the challenge of mining frequent subgraphs within a single, large graph. This study advocates for the incorporation of a message sending and receiving mechanism to facilitate data sharing across various stages of the frequent subgraph mining algorithm. Additionally, it suggests employing a standard coded subgraph and sending it to the same node for global support calculation on the large graph. The adoption of the rightmost path expansion strategy in generating candidate subgraphs helps to mitigate the occurrence of redundant subgraphs. The use of standard coding ensures the unique identification of subgraphs, thus eliminating the need for isomorphism calculations. Support calculation is executed using the Minimum Image (MNI) measurement method, aligning with the downward closure attribute. The experimental results demonstrate the robust performance of the FSM-BC-BSP algorithm across diverse input datasets and parameter configurations. Notably, the algorithm exhibits exceptional efficacy, particularly in scenarios with low support requirements, showcasing its superior performance under such conditions.

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Section: Bc-bsp Calculation Modelmentioning

confidence: 99%

FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP

Leng,

Li,

Bao

et al. 2024

Applied Sciences

Self Cite

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“…However, there is a mismatch problem between the simplicity of MapReduce computation model and the complex nature of vertex-link relationship. This incurs MapReduce model cannot directly meet the requirement of solving the iterative algorithm [8][9]. Moreover, it requires MapReduce computation to restart a job between two iterations, resulting in a large number of I/O overhead, and therefore declines the computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

MyBSP: An Iterative Processing Framework Based on the Cloud Platform for Graph Data

Liu¹,

Ye²,

Zeng³

et al. 2014

2014 Second International Conference on Advanced Cloud and Big Data

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Massive cloud-based data-intensive applications (e.g., iterative MapReduce-based) could involve graph data processing. How to effectively analyze and process large-scale graph data is an unsolved challenging problem. We present a parallel computation framework, named MyBSP, which is inspired by Google's Pregel system. MyBSP supports and implements the Bulk Synchronous Parallel (BSP) programming model, and introduces a module of parallel execution unit to achieve iterative processing, which avoids the restart cost of computation jobs, and therefore reduces the I/O overhead (e.g., network communication and disk access). Furthermore, we implement the MyBSP-based PageRank algorithm. Some experiments are conducted to evaluate and compare the performance and scalability of our MyBSP prototype system with MapReduce model. The experimental results show that the speedup in MyBSP compared to MapReduce is about 3.5X for the small-size graph dataset. Meanwhile, the performance improvement of MyBSP also outperforms MapReduce a factor of 2.1 when processing the large-scale dataset.

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“…However, there is also a mismatch problem between the simplicity of MapReduce computation model and the complex nature of vertex-link relationship [4]. For example, the MapReduce computation between two iterations need to restart a job, resulting in a large amount of network and disk overheads.…”

Section: Introductionmentioning

confidence: 99%

A Comparison Study of Graph Data Processing Based on MyBSP and MapReduce

Liu

Yao

Tang

et al. 2014

2014 International Conference on Cloud Computing and Big Data

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How to effectively process massive graph data is an intractable challenging issue. In this paper, two types of parallel computation approaches were compared: MapReduce and MyBSP. MyBSP is our open source implementation which adopts the Bulk Synchronous Parallel (BSP) programming model to support iterative processing. The MapReduce-based and MyBSP-based PageRank algorithms were implemented respectively. The experimental studies were conducted to evaluate and compare the performance and scalability of our MyBSP prototype system with MapReduce model. The results revealed that the MyBSP approach outperforms MapReduce approach for iterative graph data processing with vary size of datasets.

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BC-BSP: A BSP-Based Parallel Iterative Processing System for Big Data on Cloud Architecture

Cited by 5 publications

References 2 publications

FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP

FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP

MyBSP: An Iterative Processing Framework Based on the Cloud Platform for Graph Data

A Comparison Study of Graph Data Processing Based on MyBSP and MapReduce

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