TY - GEN
T1 - Cross-layer optimization for virtual machine resource management
AU - Zhao, Ming
AU - Wang, Lixi
AU - Lv, Yun
AU - Xu, Jing
N1 - Funding Information:
This research is sponsored by National Science Foundation under the National Science Foundation CAREER award CNS-1619653, CNS-1629888, IIS-1633381, and CMMI-1610282
Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/16
Y1 - 2018/5/16
N2 - Virtualized systems (e.g., public and private clouds) are playing an increasingly vital role to support the computing of applications from different domains. Existing resource management solutions in such systems typically treat virtual machines (VMs) as black boxes, which presents a hurdle to achieving application-desired Quality of Service (QoS). This paper advocates the cooperation between VM host- and guest-layer schedulers for optimizing the resource management and application performance. It presents an approach to such cross-layer optimization by enabling the host-layer scheduler to feedback resource allocation decisions and adapt guest-layer application configurations. As case studies, the proposed approach is applied to virtualized databases and map services which have challenging dynamic and complex resource demands as well as sophisticated configurations. Specifically, for databases, the proposed approach adapts query executions by tuning the cost model parameters according to the available storage bandwidth and memory capacity. For map services, it adapts the quality of returned map imagery in order to meet the response time target as the workload intensity and available network bandwidth change over time. A prototype of the proposed approach is implemented on Xen and Hyper-V VMs, and evaluated using a TPC-H based database workload and a TerraFly-based map service workload. The results show that with the proposed host-to-guest application adaptation, the TPC-H workload improves its performance by 33.5%, and the TerraFly workload improves the map imagery quality by 40% and always meets its response time target, compared to the schemes without adaptation.
AB - Virtualized systems (e.g., public and private clouds) are playing an increasingly vital role to support the computing of applications from different domains. Existing resource management solutions in such systems typically treat virtual machines (VMs) as black boxes, which presents a hurdle to achieving application-desired Quality of Service (QoS). This paper advocates the cooperation between VM host- and guest-layer schedulers for optimizing the resource management and application performance. It presents an approach to such cross-layer optimization by enabling the host-layer scheduler to feedback resource allocation decisions and adapt guest-layer application configurations. As case studies, the proposed approach is applied to virtualized databases and map services which have challenging dynamic and complex resource demands as well as sophisticated configurations. Specifically, for databases, the proposed approach adapts query executions by tuning the cost model parameters according to the available storage bandwidth and memory capacity. For map services, it adapts the quality of returned map imagery in order to meet the response time target as the workload intensity and available network bandwidth change over time. A prototype of the proposed approach is implemented on Xen and Hyper-V VMs, and evaluated using a TPC-H based database workload and a TerraFly-based map service workload. The results show that with the proposed host-to-guest application adaptation, the TPC-H workload improves its performance by 33.5%, and the TerraFly workload improves the map imagery quality by 40% and always meets its response time target, compared to the schemes without adaptation.
KW - Cross-layer optimization
KW - Resource management
KW - Virtual machine
UR - http://www.scopus.com/inward/record.url?scp=85048338955&partnerID=8YFLogxK
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U2 - 10.1109/IC2E.2018.00031
DO - 10.1109/IC2E.2018.00031
M3 - Conference contribution
AN - SCOPUS:85048338955
T3 - Proceedings - 2018 IEEE International Conference on Cloud Engineering, IC2E 2018
SP - 90
EP - 98
BT - Proceedings - 2018 IEEE International Conference on Cloud Engineering, IC2E 2018
A2 - Li, Jie
A2 - Chandra, Abhishek
A2 - Guo, Tian
A2 - Cai, Ying
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Cloud Engineering, IC2E 2018
Y2 - 17 April 2018 through 20 April 2018
ER -