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Dynamic Replication Management Scheme for Cloud Storage
http://hdl.handle.net/20.500.12678/0000006254
http://hdl.handle.net/20.500.12678/0000006254a64c20e9-d2a8-45e7-ac76-ab3b18f3a522
3b0eaf1b-326f-4e2c-83e6-aea67bee5733
Name / File | License | Actions |
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© 2017 ICAIT
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Publication type | ||||||
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Conference paper | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Dynamic Replication Management Scheme for Cloud Storage | |||||
Language | en | |||||
Publication date | 2017-11-02 | |||||
Authors | ||||||
May Phyo Thu | ||||||
Khine Moe Nwe | ||||||
Kyar Nyo Aye | ||||||
Description | ||||||
Nowadays, replication technique is widely used in data center storage systems to prevent data loss. Data popularity is a key factor in data replication as popular files are accessed most frequently and then they become unstable and unpredictable. Moreover, replicas placement is one of key issues that affect the performance of the system such as load balancing, data locality etc. Data locality is a fundamental problem to data-parallel applications that often happens (i.e., a data block should be copied to the processing node when a processing node does not possess the data block in its local storage), and this problem leads to the decrease in performance. To address these challenges, this paper proposes a dynamic replication management scheme based on data popularity and data locality; it includes replica allocation and replica placement algorithms. Data locality, disk bandwidth, CPU processing speed and storage utilization are considered in the proposed data placement algorithm in order to achieve better data locality and load balancing effectively. Our proposed scheme will be effective for large-scale cloud storage. | ||||||
Keywords | ||||||
Replication, Data Popularity, Data locality, Storage utilization, Disk Bandwidth | ||||||
Conference papers | ||||||
ICAIT 2017 | ||||||
1-2 November, 2017 | ||||||
1st International Conference on Advanced Information Technologies | ||||||
Yangon, Myanmar | ||||||
Cloud Computing and Big Data Analytics | ||||||
https://www.uit.edu.mm/icait-2017/ |