TY - GEN
T1 - The Status Prediction of Physical Machine in IaaS Cloud Environment
AU - Xia, Qingxin
AU - Lan, Yuqing
AU - Xiao, Limin
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/26
Y1 - 2015/10/26
N2 - At present, in researches of Iaas cloud resource scheduling strategies, it is focused that SLA violation or overloaded physical machine can trigger the migration of virtual machines, which will reduce the performance of the system and cause extra energy cost. In this paper, we model the resource of IaaS cloud based on Hidden Markov process to predict the status and the time that the physical machine is overloading, which will serve as a guideline for the resource scheduling in the IaaS cloud. Specifically, the resource status of physical machine will be chosen as the hidden status, meanwhile, the operations of virtual machine will be an observation set of the visible status, which are a modelling process. And then, we present the optimal path of the status transition probability as the core method of the physical machine status prediction. Finally, through real experimental scenarios, we verify the effectiveness of physical machine status prediction in the IaaS cloud environment.
AB - At present, in researches of Iaas cloud resource scheduling strategies, it is focused that SLA violation or overloaded physical machine can trigger the migration of virtual machines, which will reduce the performance of the system and cause extra energy cost. In this paper, we model the resource of IaaS cloud based on Hidden Markov process to predict the status and the time that the physical machine is overloading, which will serve as a guideline for the resource scheduling in the IaaS cloud. Specifically, the resource status of physical machine will be chosen as the hidden status, meanwhile, the operations of virtual machine will be an observation set of the visible status, which are a modelling process. And then, we present the optimal path of the status transition probability as the core method of the physical machine status prediction. Finally, through real experimental scenarios, we verify the effectiveness of physical machine status prediction in the IaaS cloud environment.
KW - Hidden Markov Process
KW - IaaS
KW - energy aware
KW - prediction
UR - https://www.scopus.com/pages/publications/84962498096
U2 - 10.1109/CyberC.2015.100
DO - 10.1109/CyberC.2015.100
M3 - 会议稿件
AN - SCOPUS:84962498096
T3 - Proceedings - 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2015
SP - 302
EP - 305
BT - Proceedings - 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2015
Y2 - 17 September 2015 through 19 September 2015
ER -