Article: Enhanced speculative approach for big data processing using BM-LOA algorithm in cloud environment Journal: International Journal of Grid and Utility Computing (IJGUC) 2025 Vol.16 No.4 pp.297 - 309 Abstract: In the event that one of the several tasks is being allocated to an undependable or jam-packed machine, a hugely parallel processing job can be delayed considerably. Hence, the majority of the parallel processing methodologies, namely MR, have espoused diverse strategies to conquer the issue called the straggler problem. Here, the scheme may speculatively introduce extra copies of a similar task if its development is unnaturally slow when an additional idling resource is present. In the strategies-centred processes, the dead node is exhibited. The RT along with backup time of the slow task is assessed. The slow task is rerun with the aid of BM-LOA subsequent to the evaluation. In both heterogeneous and homogeneous environments, the proposed approach is performed. Centred on the performance metrics, the proposed research technique's performance is scrutinised in experimental investigation. Thus, when weighed against the other approaches, the proposed technique achieves superior performance. Inderscience Publishers - linking academia, business and industry through research

Title: Enhanced speculative approach for big data processing using BM-LOA algorithm in cloud environment

Authors: Hetal A. Joshiara; Chirag S. Thaker; Sanjay M. Shah; Darshan B. Choksi

Addresses: L.D. College of Engineering (Ahmedabad), Gujarat Technological University, Ahmedabad, Chandkheda, Gujarat, India ' L.D. College of Engineering (Ahmedabad), Gujarat Technological University, Ahmedabad, Chandkheda, Gujarat, India ' Government Engineering College (Rajkot), Gujarat Technological University, Ahmedabad, Chandkheda, Gujarat, India ' Department of Computer Science, Sardar Patel University, Vallabh Vidyanagar, Gujarat, India

Abstract: In the event that one of the several tasks is being allocated to an undependable or jam-packed machine, a hugely parallel processing job can be delayed considerably. Hence, the majority of the parallel processing methodologies, namely MR, have espoused diverse strategies to conquer the issue called the straggler problem. Here, the scheme may speculatively introduce extra copies of a similar task if its development is unnaturally slow when an additional idling resource is present. In the strategies-centred processes, the dead node is exhibited. The RT along with backup time of the slow task is assessed. The slow task is rerun with the aid of BM-LOA subsequent to the evaluation. In both heterogeneous and homogeneous environments, the proposed approach is performed. Centred on the performance metrics, the proposed research technique's performance is scrutinised in experimental investigation. Thus, when weighed against the other approaches, the proposed technique achieves superior performance.

Keywords: MEWMA; modified exponentially weighted moving average; speculative execution; strategy; Hadoop-SRP; Hadoop supreme rate performance; big data processing and rerun.

DOI: 10.1504/IJGUC.2025.147674

International Journal of Grid and Utility Computing, 2025 Vol.16 No.4, pp.297 - 309

Received: 10 Sep 2022
Received in revised form: 26 Oct 2022
Accepted: 14 Nov 2022
Published online: 25 Jul 2025 *

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Title: Enhanced speculative approach for big data processing using BM-LOA algorithm in cloud environment

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