A hybrid genetic algorithm with mapreduce technique for cloud computing energy efficiency
Computer clouds generally comprise large power-consuming data centers as they are designed to support the elasticity and scalability required by customers. However, while cloud computing reduces energy consumption for customers, it is an issue for providers who have to deal with increasing demand an...
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| 語言: | English |
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2015
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| 在線閱讀: | http://eprints.utm.my/id/eprint/79329/1/Iza%27inNurfatehaRuzanMFC2015.pdf |
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my-utm-ep.793292018-10-14T08:44:28Z A hybrid genetic algorithm with mapreduce technique for cloud computing energy efficiency 2015 Ruzan, Iza'in Nurfateha QA75 Electronic computers. Computer science Computer clouds generally comprise large power-consuming data centers as they are designed to support the elasticity and scalability required by customers. However, while cloud computing reduces energy consumption for customers, it is an issue for providers who have to deal with increasing demand and performance expectations. This creates the need for mechanisms to improve the energy-efficiency of cloud computing data centers while maintaining desired levels of performance. This research seeks to formulate a hybrid algorithm based on Genetic algorithm and MapReduce algorithm techniques to further promote energy efficiency in the cloud computing platform. The function of the MapReduce algorithm is to optimize scheduling performance which is one of the more efficient techniques for handling large data in servers. Genetic algorithm is effective in optimally measuring the value of operations and allows for the minimization of energy efficiency where it includes the formula for single optimization energy efficiency. A series of simulations were developed to evaluate the effectiveness of the proposed algorithm. The evaluation results show the amount of Information Technology equipment power required for Power Usage Effectiveness values to optimize energy usage where the performance of the proposed algorithm is 6% better than the previous genetic algorithm and 5% better than Hadoop MapReduce scheduling on low load conditions. On the other hand, the proposed algorithm improved energy efficiency in comparison with the previous work. 2015 Thesis http://eprints.utm.my/id/eprint/79329/ http://eprints.utm.my/id/eprint/79329/1/Iza%27inNurfatehaRuzanMFC2015.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing |
| institution |
Universiti Teknologi Malaysia |
| collection |
UTM Institutional Repository |
| language |
English |
| topic |
QA75 Electronic computers Computer science |
| spellingShingle |
QA75 Electronic computers Computer science Ruzan, Iza'in Nurfateha A hybrid genetic algorithm with mapreduce technique for cloud computing energy efficiency |
| description |
Computer clouds generally comprise large power-consuming data centers as they are designed to support the elasticity and scalability required by customers. However, while cloud computing reduces energy consumption for customers, it is an issue for providers who have to deal with increasing demand and performance expectations. This creates the need for mechanisms to improve the energy-efficiency of cloud computing data centers while maintaining desired levels of performance. This research seeks to formulate a hybrid algorithm based on Genetic algorithm and MapReduce algorithm techniques to further promote energy efficiency in the cloud computing platform. The function of the MapReduce algorithm is to optimize scheduling performance which is one of the more efficient techniques for handling large data in servers. Genetic algorithm is effective in optimally measuring the value of operations and allows for the minimization of energy efficiency where it includes the formula for single optimization energy efficiency. A series of simulations were developed to evaluate the effectiveness of the proposed algorithm. The evaluation results show the amount of Information Technology equipment power required for Power Usage Effectiveness values to optimize energy usage where the performance of the proposed algorithm is 6% better than the previous genetic algorithm and 5% better than Hadoop MapReduce scheduling on low load conditions. On the other hand, the proposed algorithm improved energy efficiency in comparison with the previous work. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Ruzan, Iza'in Nurfateha |
| author_facet |
Ruzan, Iza'in Nurfateha |
| author_sort |
Ruzan, Iza'in Nurfateha |
| title |
A hybrid genetic algorithm with mapreduce technique for cloud computing energy efficiency |
| title_short |
A hybrid genetic algorithm with mapreduce technique for cloud computing energy efficiency |
| title_full |
A hybrid genetic algorithm with mapreduce technique for cloud computing energy efficiency |
| title_fullStr |
A hybrid genetic algorithm with mapreduce technique for cloud computing energy efficiency |
| title_full_unstemmed |
A hybrid genetic algorithm with mapreduce technique for cloud computing energy efficiency |
| title_sort |
hybrid genetic algorithm with mapreduce technique for cloud computing energy efficiency |
| granting_institution |
Universiti Teknologi Malaysia, Faculty of Computing |
| granting_department |
Faculty of Computing |
| publishDate |
2015 |
| url |
http://eprints.utm.my/id/eprint/79329/1/Iza%27inNurfatehaRuzanMFC2015.pdf |
| _version_ |
1747818201885966336 |