A Genetic-Algorithm-Based Approach for Task Migration in Pervasive Clouds
Pervasive computing is converging with cloud computing which becomes pervasive cloud computing as an emerging computing paradigm. Users can run their applications or tasks in pervasive cloud environment in order to gain better execution efficiency and performance leveraging powerful computing and st...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2015-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/463230 |
| _version_ | 1797764794315440128 |
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| author | Weishan Zhang Shouchao Tan Qinghua Lu Xin Liu Wenjuan Gong |
| author_facet | Weishan Zhang Shouchao Tan Qinghua Lu Xin Liu Wenjuan Gong |
| author_sort | Weishan Zhang |
| collection | DOAJ |
| description | Pervasive computing is converging with cloud computing which becomes pervasive cloud computing as an emerging computing paradigm. Users can run their applications or tasks in pervasive cloud environment in order to gain better execution efficiency and performance leveraging powerful computing and storage capacities of pervasive clouds through task migration. During task migration, there are possibly a number of conflicting objectives to be considered when making migration decisions, such as less energy consumption and quick response, in order to find an optimal migration path. In this paper, we propose a genetic algorithms- (GAs-) based approach which is effective in addressing multiobjective optimization problems. We have performed some preliminary evaluations of the proposed approach which shows quite promising results, using one of the classical genetic algorithms. The conclusion is that GAs can be used for decision making in task migrations in pervasive clouds. |
| first_indexed | 2024-03-12T20:00:49Z |
| format | Article |
| id | doaj.art-66fbb195e49544eb82de05d97c6c7bc3 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T20:00:49Z |
| publishDate | 2015-08-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-66fbb195e49544eb82de05d97c6c7bc32023-08-02T02:24:46ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-08-011110.1155/2015/463230463230A Genetic-Algorithm-Based Approach for Task Migration in Pervasive CloudsWeishan Zhang0Shouchao Tan1Qinghua Lu2Xin Liu3Wenjuan Gong4 Department of Software Engineering, China University of Petroleum, No. 66 Changjiang West Road, Qingdao 266580, China Department of Software Engineering, China University of Petroleum, No. 66 Changjiang West Road, Qingdao 266580, China Software Systems Research Group, National ICT Australia (NICTA), 13 Garden Street, Eveleigh, NSW 2015, Australia Department of Software Engineering, China University of Petroleum, No. 66 Changjiang West Road, Qingdao 266580, China Department of Software Engineering, China University of Petroleum, No. 66 Changjiang West Road, Qingdao 266580, ChinaPervasive computing is converging with cloud computing which becomes pervasive cloud computing as an emerging computing paradigm. Users can run their applications or tasks in pervasive cloud environment in order to gain better execution efficiency and performance leveraging powerful computing and storage capacities of pervasive clouds through task migration. During task migration, there are possibly a number of conflicting objectives to be considered when making migration decisions, such as less energy consumption and quick response, in order to find an optimal migration path. In this paper, we propose a genetic algorithms- (GAs-) based approach which is effective in addressing multiobjective optimization problems. We have performed some preliminary evaluations of the proposed approach which shows quite promising results, using one of the classical genetic algorithms. The conclusion is that GAs can be used for decision making in task migrations in pervasive clouds.https://doi.org/10.1155/2015/463230 |
| spellingShingle | Weishan Zhang Shouchao Tan Qinghua Lu Xin Liu Wenjuan Gong A Genetic-Algorithm-Based Approach for Task Migration in Pervasive Clouds International Journal of Distributed Sensor Networks |
| title | A Genetic-Algorithm-Based Approach for Task Migration in Pervasive Clouds |
| title_full | A Genetic-Algorithm-Based Approach for Task Migration in Pervasive Clouds |
| title_fullStr | A Genetic-Algorithm-Based Approach for Task Migration in Pervasive Clouds |
| title_full_unstemmed | A Genetic-Algorithm-Based Approach for Task Migration in Pervasive Clouds |
| title_short | A Genetic-Algorithm-Based Approach for Task Migration in Pervasive Clouds |
| title_sort | genetic algorithm based approach for task migration in pervasive clouds |
| url | https://doi.org/10.1155/2015/463230 |
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