An Ensemble Learning Approach For Task Failure Prediction In Cloud Data Centers
Due to cloud computing’s extensive use and diverse nature, they experience failures in terms of software, service, and platform, which lead to the failure of task execution, resource waste and performance deterioration. Most studies focused on failure prediction resulted in lower prediction accuraci...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01072.pdf |
| _version_ | 1797807771670806528 |
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| author | Dugyala Raman Kumar T. Naveen E Umamaheshwar Vijendar G. |
| author_facet | Dugyala Raman Kumar T. Naveen E Umamaheshwar Vijendar G. |
| author_sort | Dugyala Raman |
| collection | DOAJ |
| description | Due to cloud computing’s extensive use and diverse nature, they experience failures in terms of software, service, and platform, which lead to the failure of task execution, resource waste and performance deterioration. Most studies focused on failure prediction resulted in lower prediction accuracies due to limited attributes and a single prediction model. Hence, in this paper, an efficient ensemble model for task failure prediction is put forth. Initially, the input dataset is collected and pre-processed. In pre-processing, the dataset is cleaned up of all null values. Then, the dimensionality of the pre-processed dataset is reduced by using the PCA algorithm. Thus, the reconstructed dataset is split into training and testing sets to train failure prediction models. The proposed model employs an ensemble learning approach based on different ML and DL algorithms. Then, a comparative study is performed, and the results show that task failure in the cloud system can be effectively predicted using the proposed ensemble method. |
| first_indexed | 2024-03-13T06:27:38Z |
| format | Article |
| id | doaj.art-3500ba898b7f4fa8beb5ee171c31c514 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:27:38Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-3500ba898b7f4fa8beb5ee171c31c5142023-06-09T09:11:31ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013910107210.1051/e3sconf/202339101072e3sconf_icmed-icmpc2023_01072An Ensemble Learning Approach For Task Failure Prediction In Cloud Data CentersDugyala Raman0Kumar T. Naveen1E Umamaheshwar2Vijendar G.3Department of Computer Science and Engineering, Chaitanya Bharathi Institute of TechnologyDepartment of Computer Science and Engineering, Chaitanya Bharathi Institute of TechnologyDepartment of Computer Science and Engineering, Chaitanya Bharathi Institute of TechnologyDepartment of Information and Technology, Gokaraju Rangaraju Institute of TechnologyDue to cloud computing’s extensive use and diverse nature, they experience failures in terms of software, service, and platform, which lead to the failure of task execution, resource waste and performance deterioration. Most studies focused on failure prediction resulted in lower prediction accuracies due to limited attributes and a single prediction model. Hence, in this paper, an efficient ensemble model for task failure prediction is put forth. Initially, the input dataset is collected and pre-processed. In pre-processing, the dataset is cleaned up of all null values. Then, the dimensionality of the pre-processed dataset is reduced by using the PCA algorithm. Thus, the reconstructed dataset is split into training and testing sets to train failure prediction models. The proposed model employs an ensemble learning approach based on different ML and DL algorithms. Then, a comparative study is performed, and the results show that task failure in the cloud system can be effectively predicted using the proposed ensemble method.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01072.pdf |
| spellingShingle | Dugyala Raman Kumar T. Naveen E Umamaheshwar Vijendar G. An Ensemble Learning Approach For Task Failure Prediction In Cloud Data Centers E3S Web of Conferences |
| title | An Ensemble Learning Approach For Task Failure Prediction In Cloud Data Centers |
| title_full | An Ensemble Learning Approach For Task Failure Prediction In Cloud Data Centers |
| title_fullStr | An Ensemble Learning Approach For Task Failure Prediction In Cloud Data Centers |
| title_full_unstemmed | An Ensemble Learning Approach For Task Failure Prediction In Cloud Data Centers |
| title_short | An Ensemble Learning Approach For Task Failure Prediction In Cloud Data Centers |
| title_sort | ensemble learning approach for task failure prediction in cloud data centers |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01072.pdf |
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