Enhanced Automated Heterogeneous Data Duplication Model Using Parallel Data Compression And Sorting Technique
A duplicator machine aims to improve the time taken for duplication or data transfer. The process of duplication is done by copying each data bit from the source (master) device to the slaves including the unused memory region. However, to duplicate a 64GB Embedded Multimedia Card (eMMC) memory is u...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://eprints.usm.my/48097/1/Formatting%20Final%20Draf%20-%20Corrections%20New%20cut.pdf |
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| Summary: | A duplicator machine aims to improve the time taken for duplication or data transfer. The process of duplication is done by copying each data bit from the source (master) device to the slaves including the unused memory region. However, to duplicate a 64GB Embedded Multimedia Card (eMMC) memory is usually very time consuming which takes between 2 hours to 7 hours. In addition, the product speed specification promised by the vendor is different from what they claimed to be when it is tested in real life. Moreover, bigger data creates a transmission problem, causing delay during data duplication. Consequently, this will reduce duplication performance in terms of duration. Therefore, this study was proposed to enhance the duplication technique duration. This was achieved by adopting data storage and transmission concepts through sorting and compression techniques. Parallel technique was adopted to enhance data duplication process using multiple slaves. The impact of data type and data structure to the duplication performance was also studied. Four experiments were conducted by using the same size of heterogeneous digital data (i.e. document, picture, audio and movie). Overall, the results showed that data duplication process using different data type render a different duration. The proposed technique has reduced time consumption by 20% to 50% during data duplication depending on the technique and the environment of local and across devices. |
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