Building and evaluating an OpenStack based private Cloud for studying data migration
Date
2019-08-15
Authors
PANDEY, ANURAG KUMAR
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Abstract
Cloud computing is essentially a data center with an arrangement of multiple system
resources connected to each other for better storage and management of data. This concept of Cloud computing has grown quickly and is now a technology that is widely used. Large data centers are created to serve large number of clients' workload from various walks of life. When a resource in a data center reaches its end-of-life, it may be realized that instead of investing in upgrading, replacing the resource or legacy applications running on that resource, it is time to possibly decommission such a resource and migrate workloads to the cloud and to other resources in the data center. Data migration between different Cloud resources and the servers of the same private Cloud is risky due to the possibility of data loss and also due to high temporal cost.
For my thesis, I have proposed a novel architecture and algorithm to study this phenomenon. I have used MapReduce data processing software framework within a private Cloud environment to determine the data loss. I have proposed metrics such as efficiency, speed, computation time and the cost of data migration and formulae for these metrics to test and evaluate my proposed framework.
The data migration technique I have introduced in my experiments shows better performance than the previously available work in cloud computing literature. I have done
experiments using image files like png, jpeg and tiff, audio fi les like wav, video fi les like mp4 and documents like xls and csv. Whatever small amount of data loss occurred during my experiments could be avoided with a stable network connectivity. I have used large heterogeneous files (text, images, audio and videos) for my algorithm to compute execution time and efficiency.
With the limited resources available for the current study, the size of files migrated were
bounded due to system limitations and also data loss could not be avoided completely. By improving the confi guration of the system architecture and physical servers I might expect to improve the performance further.
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Keywords
Private Cloud, OpenStack, Data Migration, MaaS, Juju