TY - GEN
T1 - Introspection of Virtual Machine Memory Resource in the Virtualized Systems
AU - Lee, Minho
AU - Park, Sujin
AU - Song, Yongju
AU - Eom, Young Ik
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Nowadays, data center and cloud servers have commonly adopted virtualization technologies to consolidate multiple servers into a physical one. It is because cloud service providers can achieve low server maintenance cost by improving resource utilization and reducing power consumption through server consolidation with virtualization technologies. However, unlike the physical system resources such as CPU and storage which can be flexibly utilized and shared by using time-based schedulers, memory resource is not easy for flexible utilization and sharing in that the memory size of each virtual machine is fixed by initial configuration. For this reason, sufficient understanding on memory resource usage of each virtual machine is essential in analyzing the existing memory management techniques such as memory ballooning and virtual machine migration. In this paper, we introduce a novel virtual machine memory monitoring tool, called SELF-e, which is developed for tracing the page accesses of each virtual machine in real-time and collecting necessary information on shared pages. Experimental results show that SELF-e efficiently announces the information on classified pages without significant performance degradation.
AB - Nowadays, data center and cloud servers have commonly adopted virtualization technologies to consolidate multiple servers into a physical one. It is because cloud service providers can achieve low server maintenance cost by improving resource utilization and reducing power consumption through server consolidation with virtualization technologies. However, unlike the physical system resources such as CPU and storage which can be flexibly utilized and shared by using time-based schedulers, memory resource is not easy for flexible utilization and sharing in that the memory size of each virtual machine is fixed by initial configuration. For this reason, sufficient understanding on memory resource usage of each virtual machine is essential in analyzing the existing memory management techniques such as memory ballooning and virtual machine migration. In this paper, we introduce a novel virtual machine memory monitoring tool, called SELF-e, which is developed for tracing the page accesses of each virtual machine in real-time and collecting necessary information on shared pages. Experimental results show that SELF-e efficiently announces the information on classified pages without significant performance degradation.
KW - content-based memory sharing
KW - virtual machine migration
KW - virtual-machine monitoring tool
UR - https://www.scopus.com/pages/publications/85064710733
U2 - 10.1109/BIGCOMP.2019.8679210
DO - 10.1109/BIGCOMP.2019.8679210
M3 - Conference contribution
AN - SCOPUS:85064710733
T3 - 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
BT - 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019
Y2 - 27 February 2019 through 2 March 2019
ER -