Jia Rao
ABSTRACT
Virtual machine (VM) technology enables multiple VMs to share resources on the same host. Resources allocated to the VMs should be re-configured dynamically in response to the change of application demands or resource supply. Because VM execution involves privileged domain and VM monitor, this causes uncertainties in VMs' resource to performance mapping and poses challenges in online determination of appropriate VM configurations. In this paper, we propose a reinforcement learning (RL) based approach, namely VCONF, to automate the VM configuration process. VCONF employs model-based RL algorithms to address the scalability and adaptability issues in applying RL in systems management. Experimental results on both controlled environments and a testbed of clouds with Xen VMs and representative server workloads demonstrate the effectiveness of VCONF. The approach is able to find optimal (near optimal) configurations in small scale systems and shows good adaptability and scalability.
- http://www.research.ibm.com/autonomic.Google Scholar
- C. G. Atkesonand J. C. Santamar'ia. A comparison of direct and model-based reinforcement learning. In In ICRA 1997.Google Scholar
- P. Barham, B. Dragovic, K. Fraser, S. Hand, T. L. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield. Xenand the art of virtualization. In SOSP 2003. Google ScholarDigital Library
- X. Bu, J. Rao, C. -Z. Xu. A reinforcement learning approach to online web systems auto-configuration. In ICDCS 2009. Google ScholarDigital Library
- G. Candea, E. Kiciman, S. Kawamoto, and A. Fox. Autonomous recovery in componentized internet applications. Cluster Computing 2006. Google ScholarDigital Library
- J. P. Cassaza, M. Greenfield, and K. shi. Redefining server performance characterization for virtualization benchmarking. In Intel technology Journal 2006.Google Scholar
- C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield. Live migration of virtual machines. In NSDI 2005. Google ScholarDigital Library
- I. Cohen, S. Zhang, M. Goldszmidt, J. Symons, T. Kelly, and A. Fox. Capturing, indexing, clustering, and retrieving system history. In SOSP 2005. Google ScholarDigital Library
- D. Gupta, L. Cherkasova, R. Gardner, and A. Vahdat. Enforcing performance isolation across virtual machines in xen. In Middleware 2006. Google ScholarDigital Library
- Hyper-V server. http://www.microsoft.com/servers/hyper-v-server.Google Scholar
- E. Ipek, O. Mutlu, J. F. Martinez, andR. Caruana. Self-optimizing memory controllers: A reinforcement learning approach. In ISCA 2008. Google ScholarDigital Library
- A. Kamra, V. Misra, and E. M. Nahum. Yaksha:a self-tuning controller for managing the performance of 3-tiered web sites. In IWQoS 2004.Google Scholar
- M. Karlsson, C. T. Karamanolis, and X. Zhu. Triage: performance isolation and differentiation for storage systems. In IWQoS 2004.Google Scholar
- X. Liu, L. Sha, Y. Diao, S. Froehlich, J. L. Hellerstein, and S. S. Parekh. Online response time optimization of apache web server. In IWQoS 2003. Google ScholarDigital Library
- D. Ongaro, A. L. Cox, and S. Rixner. Scheduling i/o in virtual machine monitors. In VEE 2008. Google ScholarDigital Library
- P. Padala, K. G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, A. Merchant, and K. Salem. Adaptive control of virtualized resources in utility computing environments. In EuroSys 2007. Google ScholarDigital Library
- J. Rao and C.-Z. Xu. Online measurement the capacity of multi-tier websites using hardware performance counters. In ICDCS 2008. Google ScholarDigital Library
- A. A. Soror, U. F. Minhas, A. Aboulnaga, K. Salem, P. Kokosielis, and S. Kamath. Automatic virtual machine configuration for database workloads. In SIGMOD 2008. Google ScholarDigital Library
- Y.-Y. Su, M. Attariyan, and J. Flinn. Autobash:improving configuration management with operating system causality analysis. In SOSP 2007. Google ScholarDigital Library
- R. S. Sutton. Generalization in reinforcement learning: Successful examples using sparse coarse coding. In Advances in Neural Information Processing Systems 1996.Google Scholar
- R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction MIT Press, 1998. Google ScholarDigital Library
- G. Tesauro. Online resource allocation using decompositional reinforcement learning. In AAAI 2005. Google ScholarDigital Library
- G. Tesauro, R. Das, H. Chan, J. Kephart, D. Levine, F. Rawson, and C. Lefurgy. Managing power consumption and performance of computing systems using reinforcement learning. In Advances in Neural Information Processing Systems 2007.Google Scholar
- G. Tesauro, N. K. Jong, R. Das, and M. N. Bennani. On the use of hybrid reinforcement learning for autonomic resource allocation. Cluster Computing 2007. Google ScholarDigital Library
- The SPECweb benchmark. http://www.spec.org/web2005.Google Scholar
- http://www.tpc.org/tpcw.Google Scholar
- http://www.tpc.org/tpcc.Google Scholar
- VMware. http://www.vmware.com.Google Scholar
- VMware VMmark. http://www.vmware.com/products/vmmark.Google Scholar
- J. Wei and C.-Z. Xu. A self-tuning fuzzy control approach for end-to-end qos guarantees in web servers. In IWQoS 2005. Google ScholarDigital Library
- A. Whitaker, R. S. Cox, and S. D. Gribble. Configuration debugging as search: Finding the needle in the haystack. In OSDI 2004. Google ScholarDigital Library
- J. Wildstrom, P. Stone, and E. Witchel. Carve: A cognitive agent for resource value estimation. In ICAC 2008. Google ScholarDigital Library
- S. Zhang, I. Cohen, M. Goldszmidt, J. Symons, and A. Fox. Ensembles of models for automated diagnosis of system performance problems. In DSN 2005. Google ScholarDigital Library
Index Terms
- VCONF: a reinforcement learning approach to virtual machines auto-configuration
Recommendations
A Study on Performance of Processes in Migrating Virtual Machines
ISADS '11: Proceedings of the 2011 Tenth International Symposium on Autonomous Decentralized SystemsIn a cloud computing environment, virtual machines are migrated with two kind of methods. One is non-live migration, and the other is live migration. In case of non-live migration, a virtual machine stops their processes during migrations. In case of ...
Performance Evaluation of Hypervisors for Cloud Computing
The virtualization of IT infrastructure enables consolidation and pooling of IT resources so they are shared over diverse applications to offset the limitation of shrinking resources and growing business needs. Virtualization provides a logical ...
Towards VM Consolidation Using a Hierarchy of Idle States
VEE '15Typical VM consolidation approaches re-pack VMs into fewer physical machines, resulting in energy and cost savings [13, 19, 23, 40]. Recent work has explored a just-in time approach to VM consolidation by transitioning VMsto an inactive state when idle ...
Comments