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Efficient energy and processes time algorithm for offloading using cloud computing

Aldmour, Rakan, Yousef, Sufian, Albaadani, Faris and Yaghi, Mohammad 2017. Efficient energy and processes time algorithm for offloading using cloud computing. Presented at: International Conference on Global Security, Safety, and Sustainability, London, UK, 18-20 January 2017. Published in: Jahankhani, Hamid, Carlile, Alex, Emm, David, Hosseinian-Far, Amin, Brown, Guy and Sexton, Graham eds. Global Security, Safety and Sustainability - The Security Challenges of the Connected World. ICGS3 2017. Communications in Computer and Information Science Cham: Springer, pp. 364-370. 10.1007/978-3-319-51064-4_29

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Abstract

The best way to execute big files in better performance and short times while the available resources on the core server is the new technique called offloading in cloud computing. However, the offloading technique is not the right place to execute, so it is much better to execute files on the node in some cases. In this issue there is a trade-off, while the power limitation in the local node, so in this paper an innovative algorithm is proposed based on the file size. So in this issue the file size is measured and after that the decision is taken for the execution file, if is it locally on the node, or offloading by sending the file to the core cloud. The most important issue is to preserve time while the performing file. However, the second and important issue especially for the small nodes, is to preserve the energy limitation for big files, because of the power consumption is very high. The cost of the power consumption, execution time, and file size for the core cloud, and local node is calculated to denote an input to the execution decision.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Uncontrolled Keywords: Cloud computing, Mobile cloud computing, Execution time, Power consumption, Offloading
Publisher: Springer
ISBN: 978-3-319-51063-7
ISSN: 1865-0937
Last Modified: 09 Jun 2020 01:41
URI: http://orca.cf.ac.uk/id/eprint/114609

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