Performance Guarantees for Web Server End-Systems: A Control-Theoretical Approach
IEEE Transactions on Parallel and Distributed Systems
Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Feedback Control of Computing Systems
Feedback Control of Computing Systems
Memory resource management in VMware ESX server
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
A capacity management service for resource pools
Proceedings of the 5th international workshop on Software and performance
Understanding The Linux Kernel
Understanding The Linux Kernel
Live migration of virtual machines
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
Adaptive control of virtualized resources in utility computing environments
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Queue - Virtualization
1000 Islands: Integrated Capacity and Workload Management for the Next Generation Data Center
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
Black-box and gray-box strategies for virtual machine migration
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Utilization and SLO-Based control for dynamic sizing of resource partitions
DSOM'05 Proceedings of the 16th IFIP/IEEE Ambient Networks international conference on Distributed Systems: operations and Management
Automated control of multiple virtualized resources
Proceedings of the 4th ACM European conference on Computer systems
Supporting GPU sharing in cloud environments with a transparent runtime consolidation framework
Proceedings of the 20th international symposium on High performance distributed computing
The resource-as-a-service (RaaS) cloud
HotCloud'12 Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing
Managing resource contention in embedded service-oriented systems with dynamic orchestration
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
Application level ballooning for efficient server consolidation
Proceedings of the 8th ACM European Conference on Computer Systems
IBM zEnterprise unified resource manager platform performance management
IBM Journal of Research and Development
Ginseng: market-driven memory allocation
Proceedings of the 10th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Cloudy with a Chance of Load Spikes: Admission Control with Fuzzy Risk Assessments
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
| Hi-index | 0.00 |
The newly emergent cloud computing environments host hundreds to thousands of services on a shared resource pool. The sharing is enhanced by virtualization technologies allowing multiple services to run in different virtual machines (VMs) on a single physical node. Resource overbooking allows more services with time-varying demands to be consolidated reducing operational costs. In the past, researchers have studied dynamic control mechanisms for allocating CPU to virtual machines, when CPU is overbooked with respect to the sum of the peak demands from all the VMs. However, runtime re-allocation of memory among multiple VMs has not been widely studied, except on VMware platforms. In this paper, we present a case study where feedback control is used for dynamic memory allocation to Xen virtual machines in a consolidated environment. We illustrate how memory behaves differently from CPU in terms of its relationship to application-level performance, such as response times. We have built a prototype of a joint resource control system for allocating both CPU and memory resources to co-located VMs in real time. Experimental results show that our solution allows all the hosted applications to achieve the desired performance in spite of their time-varying CPU and memory demands, whereas a solution without memory control incurs significant service level violations.