NDA-PAD Vs MapReduce.pdf
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This document discusses a new sampling method for efficient cloud data storage that is more efficient than MapReduce in terms of processing time. The method, called NDA/PDA, adopts data distribution characteristics like normal and Poisson distributions to sample data. It creates a buffer zone to control the size of inserted data from original datasets. Results showed the NDA method achieves over 95% similarity to original data.
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With the interconnection of one to many computers online, the data shared by users is multiplying daily. As
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Cloud computing is a type of computing that relies on sharing computing resources rather than having local servers or personal devices to handle applications.
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- 2. • We introduce a new sampling methods for efficient cloud data storage. • Compared with MapReduce, our solution is more efficient in terms of processing time. • Refer to the link for full paper: https://ieeexplore.ieee.org/abstract/document/7172493
- 4. • The NDA/PDA methods adopt data distribution characteristics, i.e. Normal distribution approximation (NDA) and Possion distribution approximation (PDA). • The algorithm is simple. Create a buffer zone Delta, which controls the size of insert data from original datasets as shown on the right.
- 5. Results: The NDA method can achieve above 95% similarity to the original data.
- 7. Reference Paper: “Splitting large medical data sets based on normal distribution in cloud environment”, H Zhang, Y Zhao, C Pang, J He, IEEE Transactions on Cloud Computing 8 (2), 518-531. https://ieeexplore.ieee.org/abstract/document/7172493