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Partial storage optimization and load control strategy of cloud data centers.

Al Nuaimi K, Mohamed N, Al Nuaimi M, Al-Jaroodi J - ScientificWorldJournal (2015)

Bottom Line: Our focus is to improve the performance and optimize the storage usage by providing the DaaS on the cloud.Reducing the space needed will help in reducing the cost of providing such space.Moreover, performance is also increased since multiple cloud servers will collaborate to provide the data to the cloud clients in a faster manner.

View Article: PubMed Central - PubMed

Affiliation: UAE University, P.O. Box 15551, Al Ain, UAE.

ABSTRACT
We present a novel approach to solve the cloud storage issues and provide a fast load balancing algorithm. Our approach is based on partitioning and concurrent dual direction download of the files from multiple cloud nodes. Partitions of the files are saved on the cloud rather than the full files, which provide a good optimization to the cloud storage usage. Only partial replication is used in this algorithm to ensure the reliability and availability of the data. Our focus is to improve the performance and optimize the storage usage by providing the DaaS on the cloud. This algorithm solves the problem of having to fully replicate large data sets, which uses up a lot of precious space on the cloud nodes. Reducing the space needed will help in reducing the cost of providing such space. Moreover, performance is also increased since multiple cloud servers will collaborate to provide the data to the cloud clients in a faster manner.

No MeSH data available.


Storage consumption comparison using 2 cloud servers.
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Related In: Results  -  Collection


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fig10: Storage consumption comparison using 2 cloud servers.

Mentions: Our algorithm proved to reduce the need of storage needed on the cloud servers by at least 50% of the total storage. The difference is because we partition the file and save only the blocks that are usually provided by that server and we remove the other partitions. The other partitions are available on other servers. Moreover, saving about 3 GB of storage when the file size is very large is a very promising result, especially if many of the files on the cloud are large files. The test case we used is when having only two servers processing the file on the cloud. However, if there are more servers, the replicated blocks will increase based on the number of servers as described earlier. The differences in storage use among the three techniques are shown in Figure 10.


Partial storage optimization and load control strategy of cloud data centers.

Al Nuaimi K, Mohamed N, Al Nuaimi M, Al-Jaroodi J - ScientificWorldJournal (2015)

Storage consumption comparison using 2 cloud servers.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4417988&req=5

fig10: Storage consumption comparison using 2 cloud servers.
Mentions: Our algorithm proved to reduce the need of storage needed on the cloud servers by at least 50% of the total storage. The difference is because we partition the file and save only the blocks that are usually provided by that server and we remove the other partitions. The other partitions are available on other servers. Moreover, saving about 3 GB of storage when the file size is very large is a very promising result, especially if many of the files on the cloud are large files. The test case we used is when having only two servers processing the file on the cloud. However, if there are more servers, the replicated blocks will increase based on the number of servers as described earlier. The differences in storage use among the three techniques are shown in Figure 10.

Bottom Line: Our focus is to improve the performance and optimize the storage usage by providing the DaaS on the cloud.Reducing the space needed will help in reducing the cost of providing such space.Moreover, performance is also increased since multiple cloud servers will collaborate to provide the data to the cloud clients in a faster manner.

View Article: PubMed Central - PubMed

Affiliation: UAE University, P.O. Box 15551, Al Ain, UAE.

ABSTRACT
We present a novel approach to solve the cloud storage issues and provide a fast load balancing algorithm. Our approach is based on partitioning and concurrent dual direction download of the files from multiple cloud nodes. Partitions of the files are saved on the cloud rather than the full files, which provide a good optimization to the cloud storage usage. Only partial replication is used in this algorithm to ensure the reliability and availability of the data. Our focus is to improve the performance and optimize the storage usage by providing the DaaS on the cloud. This algorithm solves the problem of having to fully replicate large data sets, which uses up a lot of precious space on the cloud nodes. Reducing the space needed will help in reducing the cost of providing such space. Moreover, performance is also increased since multiple cloud servers will collaborate to provide the data to the cloud clients in a faster manner.

No MeSH data available.