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HyPar-FCA+: an improved workload-aware elastic framework for FCA

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Abstract

Formal concept analysis (FCA) algorithms are computationally expensive. Several parallel and distributed FCA algorithms are proposed to reduce the execution time by taking advantage of the parallelism available in the cloud environment. These approaches can be broadly classified into replication-based and partitioning-based strategies. Replication-based strategies require the entire input data to be in memory throughout execution, and therefore, they suffer from memory bottlenecks while dealing with large datasets. Horizontal partitioning-based approaches overcome the memory bottlenecks but incur enormous communication overhead during concept discovery as they require all-to-all worker communication per concept. Thus, the state-of-the-art frameworks implementing the above strategies do not scale to large datasets in cloud environments running commodity hardware. In this paper, we propose HyPar-FCA+, an improved workload-aware elastic framework for FCA, that overcomes the above scalability issues and also improves resource utilization/cost in a cloud environment. Its salient features are a novel vector bin packing-based algorithm for partitioning the input context, a new workload estimator, and elastic provisioning of cloud resources using a feedback-based predictor. Compared with state-of-the-art distributed FCA frameworks, HyPar-FCA+ is 3–11% and 15–36% faster for real-world datasets (Susy, Webdocs) and synthetic datasets, respectively. In terms of execution cost in the cloud, HyPar-FCA+ takes 27–38% and \(\sim\) 85% less cost for real-world datasets and synthetic datasets, respectively.

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Notes

  1. We have plotted the RMSE using only the first-level concepts.

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Funding

This work is partially funded by SPARC, a Govt. of India Initiative under Grant No. SPARC/2018-2019/P682/SL.

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Authors and Affiliations

  1. Indian Institute of Technology Mandi, Mandi, India

    Muneeswaran Packiaraj

  2. Department of Computer Science and Engineering, National Institute of Technology Warangal, Warangal, India

    Sriram Kailasam

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  1. Muneeswaran Packiaraj
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  2. Sriram Kailasam
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Contributions

MP and SK contributed to the design and implementation of the research, to the analysis of the results, and the writing of the manuscript. SK supervised the project.

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Correspondence to Muneeswaran Packiaraj.

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Supported by SPARC, a Govt. of India Initiative under Grant No. SPARC/2018-2019/P682/SL.

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Packiaraj, M., Kailasam, S. HyPar-FCA+: an improved workload-aware elastic framework for FCA. J Supercomput 79, 11767–11796 (2023). https://doi.org/10.1007/s11227-023-05116-3

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