Proceedings of the 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)

Research on Virtual Device Management in Real-time Environment

Authors
Wenjia Gong, Yongchao Tao, Wencheng Xiang, Xianghu Wu
Corresponding Author
Wenjia Gong
Available Online December 2017.
DOI
https://doi.org/10.2991/anit-17.2018.16How to use a DOI?
Keywords
Virtualization, scheduling algorithm, mixed load, Xen
Abstract
In this paper, we present improved methods of credit scheduling algorithm of Xen. By using gray-box knowledge reasoning technology, we are able to identify the IO task in the internal workload of each virtual machine. By using this information of the internal workload, we proposed a Boosting mechanism based on the selection. Selectively raise the priority of the virtual processor to give priority to the corresponding field of the I/O Tasks improve the response speed, and reduce the response time to achieve real-time purposes. In addition, we propose four complementary mechanisms for the shortcomings of the proposed BOOST scheduling algorithm based on the selection. The four complementary mechanisms are the cancellation BOOST priority mechanism, reducing Credit precise mechanism, setting Rate value mechanisms and UNDER queue mechanisms. These four mechanisms can effectively fill the insufficient of BOOST scheduling algorithm based selection, and improve the stability of the algorithm.
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Proceedings
2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2017
ISBN
978-94-6252-447-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/anit-17.2018.16How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Wenjia Gong
AU  - Yongchao Tao
AU  - Wencheng Xiang
AU  - Xianghu Wu
PY  - 2017/12
DA  - 2017/12
TI  - Research on Virtual Device Management in Real-time Environment
BT  - 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/anit-17.2018.16
DO  - https://doi.org/10.2991/anit-17.2018.16
ID  - Gong2017/12
ER  -