Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87815
Title: From cloud to fog computing : a review and a conceptual live VM migration framework
Authors: Osanaiye, Opeyemi
Chen, Shuo
Yan, Zheng
Lu, Rongxing
Choo, Kim-Kwang Raymond
Dlodlo, Mqhele
Keywords: Cloud Computing
Edge Computing
Issue Date: 2017
Source: Osanaiye, O., Chen, S., Yan, Z., Lu, R., Choo, K.-K. R., & Dlodlo, M. (2017). From cloud to fog computing : a review and a conceptual live VM migration framework. IEEE Access, 5, 8284-8300.
Series/Report no.: IEEE Access
Abstract: Fog computing, an extension of cloud computing services to the edge of the network to decrease latency and network congestion, is a relatively recent research trend. Although both cloud and fog offer similar resources and services, the latter is characterized by low latency with a wider spread and geographically distributed nodes to support mobility and real-time interaction. In this paper, we describe the fog computing architecture and review its different services and applications. We then discuss security and privacy issues in fog computing, focusing on service and resource availability. Virtualization is a vital technology in both fog and cloud computing that enables virtual machines (VMs) to coexist in a physical server (host) to share resources. These VMs could be subject to malicious attacks or the physical server hosting it could experience system failure, both of which result in unavailability of services and resources. Therefore, a conceptual smart pre-copy live migration approach is presented for VM migration. Using this approach, we can estimate the downtime after each iteration to determine whether to proceed to the stop-and-copy stage during a system failure or an attack on a fog computing node. This will minimize both the downtime and the migration time to guarantee resource and service availability to the end users of fog computing. Last, future research directions are outlined.
URI: https://hdl.handle.net/10356/87815
http://hdl.handle.net/10220/45512
DOI: http://dx.doi.org/10.1109/ACCESS.2017.2692960
Rights: © 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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