Improved rsync algorithm to minimize communication cost using multi-agent systems for synchronization in multi-learning management systems
Remote file synchronization (RFS) is based on updating the outdated version of a file that resides on one machine to be similar to the new version of the updated file in another machine at a minimum computation time (cost). The problem of rsync algorithm synchronization process is that rsync trie...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/71209/1/FK%202017%2073%20-%20IR.pdf |
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| Summary: | Remote file synchronization (RFS) is based on updating the outdated version of a file
that resides on one machine to be similar to the new version of the updated file in
another machine at a minimum computation time (cost). The problem of rsync
algorithm synchronization process is that rsync tries to check folders and files one by
one, which takes long time to synchronize them. Therefore, the aim of this study is to
minimize the computation time during RFS by improving the standard rsync
algorithm.
Previously, several algorithms and techniques have been proposed for partial file
synchronization but many of them were based on controlling the block size,
checksums, and delta compression of the matched blocks, to solve the searching
problem of the rsync algorithm. This study proposed several techniques to improve
rsync (irsync) algorithm in order to reduce computation time during RFS, by
encompasses a Multi-Agent system (MAS) framework. This algorithm involves
several agents, such as: initiator, sense_agent (SA), log_agent (LA), and search_agent
(SeA). These types of agents have different capabilities, actions, and efficiency to the
irsync algorithm in file synchronization.
The study proposed MAS framework in the Learning Management System (LMS) that
involves the transfer of data from one machine to another. To meet this requirement,
a new Multi LMS (MLMS) model using Sharable Content Object Reference Model
(SCORM) specifications to share learning materials among different higher learning
institutions (HLIs) has been presented. This model enhances the interoperability and
collaboration of HLIs in terms of synchronization and sharing of learning contents.
To evaluate the computation time of the new techniques, standard datasets, which
include two versions of source codes emacs-19.28 with emacs-19.29, and gcc-4.8.1 with gcc-4.8.2, were used. The experimental results show that the improved rsync
(irsync) algorithm yields a better performance against two previous algorithms, rsync
algorithm and hierarchical folder synchronization algorithm (HFSA) in terms of
reducing computation time and improve synchronization response time.
Therefore, the MAS framework was performed and the reduction of computation time
was obtained by 19.86% compared to 42.25% of standard rsync. The results also
indicated that reducing the searching time could enhance the irsync algorithm
responsiveness time by 32.07% compared to 67.93% of the standard rsync. The
integration of the proposed MLMS model with irsync algorithm was further tested
through a prototype with MAS and show significant improvement over the cloud
synchronization system which based on CDMI technology. These two systems were
evaluated in terms of synchronization rate. The results revealed that the MLMS
system with irsync (MAS) outperformed the cloud system. |
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