CLC number: TP393
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-11-14
Cited: 0
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Reza Sookhtsaraei, Javad Artin, Ali Ghorbani, Ahmad Faraahi, Hadi Adineh. A locality-based replication manager for data cloud[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(12): 1275-1286.
@article{title="A locality-based replication manager for data cloud",
author="Reza Sookhtsaraei, Javad Artin, Ali Ghorbani, Ahmad Faraahi, Hadi Adineh",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="12",
pages="1275-1286",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500391"
}
%0 Journal Article
%T A locality-based replication manager for data cloud
%A Reza Sookhtsaraei
%A Javad Artin
%A Ali Ghorbani
%A Ahmad Faraahi
%A Hadi Adineh
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 12
%P 1275-1286
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500391
TY - JOUR
T1 - A locality-based replication manager for data cloud
A1 - Reza Sookhtsaraei
A1 - Javad Artin
A1 - Ali Ghorbani
A1 - Ahmad Faraahi
A1 - Hadi Adineh
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 12
SP - 1275
EP - 1286
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1500391
Abstract: Efficient data management is a key issue for environments distributed on a large scale such as the data cloud. This can be taken into account by replicating the data. The replication of data reduces the time of service and the delay in availability, increases the availability, and optimizes the distribution of load in the system. It is worth mentioning, however, that with the replication of data, the use of resources and energy increases due to the storing of copies of the data. We suggest a replication manager that decreases the cost of using resources, energy, and the delay in the system, and also increases the availability of the system. To reach this aim, the suggested replication manager, called the locality replication manager (LRM), works by using two important algorithms that use the physical adjacency feature of blocks. In addition, a set of simulations are reported to show that LRM can be a suitable option for distributed systems as it uses less energy and resources, optimizes the distribution of load, and has more availability and less delay.
In this paper, authors proposed a locality-based replication manager (LRM) for data cloud. In LRM, authors utilise genetic algorithm to optimise the availability and delay of the system. Simulation results compared to other two representative replication strategies show the performance improvement of the proposed approach.
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