TY - GEN
T1 - Application-aware cross-layer virtual machine resource management
AU - Wang, Lixi
AU - Xu, Jing
AU - Zhao, Ming
PY - 2012
Y1 - 2012
N2 - Existing resource management solutions in datacenters and cloud systems typically treat VMs as black boxes when making resource allocation decisions. 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 upon fuzzy-modelingbased resource management. This approach exploits guest-layer application knowledge to capture workload characteristics and improve VM modeling, and enables the host-layer scheduler to feedback resource allocation decision and adapt guest-layer application configuration. As a case study, this approach is applied to virtualized databases which have challenging dynamic, complex resource usage behaviors. Specifically, it characterizes query workloads based on a database's internal cost estimation and adapts query executions by tuning the cost model parameters according to changing resource availability. A prototype of the proposed approach is implemented on Xen VMs and evaluated using workloads based on TPC-H and RUBiS. The results show that with guest-to-host workload characterization, resources can be efficiently allocated to database VMs serving workloads with changing intensity and composition while meeting Quality-of- Service (QoS) targets. For TPC-H, the prediction error for VM resource demand is less than 3.5%; for RUBiS, the response time target is met for 92% of the time. Both significantly outperform the resource allocation scheme without workload characterization. With host-to-guest database adaptation, the performance of TPCH- based workloads is also improved by 17% when the VM's available I/O bandwidth is reduced due to contention.
AB - Existing resource management solutions in datacenters and cloud systems typically treat VMs as black boxes when making resource allocation decisions. 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 upon fuzzy-modelingbased resource management. This approach exploits guest-layer application knowledge to capture workload characteristics and improve VM modeling, and enables the host-layer scheduler to feedback resource allocation decision and adapt guest-layer application configuration. As a case study, this approach is applied to virtualized databases which have challenging dynamic, complex resource usage behaviors. Specifically, it characterizes query workloads based on a database's internal cost estimation and adapts query executions by tuning the cost model parameters according to changing resource availability. A prototype of the proposed approach is implemented on Xen VMs and evaluated using workloads based on TPC-H and RUBiS. The results show that with guest-to-host workload characterization, resources can be efficiently allocated to database VMs serving workloads with changing intensity and composition while meeting Quality-of- Service (QoS) targets. For TPC-H, the prediction error for VM resource demand is less than 3.5%; for RUBiS, the response time target is met for 92% of the time. Both significantly outperform the resource allocation scheme without workload characterization. With host-to-guest database adaptation, the performance of TPCH- based workloads is also improved by 17% when the VM's available I/O bandwidth is reduced due to contention.
KW - Autonomic computing
KW - Fuzzy modeling
KW - Resource management
KW - Virtualization
UR - http://www.scopus.com/inward/record.url?scp=84867716067&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867716067&partnerID=8YFLogxK
U2 - 10.1145/2371536.2371541
DO - 10.1145/2371536.2371541
M3 - Conference contribution
AN - SCOPUS:84867716067
SN - 9781450315203
T3 - ICAC'12 - Proceedings of the 9th ACM International Conference on Autonomic Computing
SP - 13
EP - 22
BT - ICAC'12 - Proceedings of the 9th ACM International Conference on Autonomic Computing
T2 - 9th ACM International Conference on Autonomic Computing, ICAC'12
Y2 - 18 September 2012 through 20 September 2012
ER -