Zhiming Shen
John Wilkes
ABSTRACT
Elastic resource scaling lets cloud systems meet application service level objectives (SLOs) with minimum resource provisioning costs. In this paper, we present CloudScale, a system that automates fine-grained elastic resource scaling for multi-tenant cloud computing infrastructures. CloudScale employs online resource demand prediction and prediction error handling to achieve adaptive resource allocation without assuming any prior knowledge about the applications running inside the cloud. CloudScale can resolve scaling conflicts between applications using migration, and integrates dynamic CPU voltage/frequency scaling to achieve energy savings with minimal effect on application SLOs. We have implemented CloudScale on top of Xen and conducted extensive experiments using a set of CPU and memory intensive applications (RUBiS, Hadoop, IBM System S). The results show that CloudScale can achieve significantly higher SLO conformance than other alternatives with low resource and energy cost. CloudScale is non-intrusive and light-weight, and imposes negligible overhead (< 2% CPU in Domain 0) to the virtualized computing cluster.
- Amazon Elastic Compute Cloud. http://aws.amazon.com/ec2/.Google Scholar
- Apache Hadoop System. http://hadoop.apache.org/core/.Google Scholar
- KVM (Kernel-based Virtual Machine). http://www.linux-kvm.org/page/Main_Page.Google Scholar
- RUBiS Online Auction System. http://rubis.ow2.org/.Google Scholar
- The IRCache Project. http://www.ircache.net/.Google Scholar
- Virtual Computing Lab. http://vcl.ncsu.edu/.Google Scholar
- VMware Virtualization Technology. http://www.vmware.com/.Google Scholar
- Xen Credit Scheduler. http://wiki.xensource.com/xenwiki/CreditScheduler.Google Scholar
- M. Armbrust, A. Fox, D. A. Patterson, N. Lanham, B. Trushkowsky, J. Trutna, and H. Oh. Scads: Scale-independent storage for social computing applications. In Proc. CIDR, 2009.Google Scholar
- P. Barham and et al. Xen and the art of virtualization. In Proc. SOSP, 2003. Google ScholarDigital Library
- D. Breitgand, M. B.-Yehuda, M. Factor, H. Kolodner, V. Kravtsov, and D. Pelleg. NAP: a building block for remediating performance bottlenecks via black box network analysis. In Proc. ICAC, 2009. Google ScholarDigital Library
- A. Chandra, W. Gong, and P. Shenoy. Dynamic resource allocation for shared data centers using online measurements. In Proc. IWQoS, 2004. Google ScholarDigital Library
- J. Chase, D. Anderson, P. N. Thakar, and A. M. Vahdat. Managing energy and server resources in hosting centers. In Proc. SOSP, 2001. Google ScholarDigital Library
- C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield. Live migration of virtual machines. In Proc. NSDI, 2005. Google ScholarDigital Library
- J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. Dec. 2004.Google Scholar
- G. Dhiman and T. S. Rosing. Dynamic voltage frequency scaling for multi-tasking systems using online learning. In Proc. ISLPED, 2007. Google ScholarDigital Library
- R. Doyle, J. Chase, O. Asad, W. Jin, and A. Vahdat. Model-based resource provisioning in a web service utility. In Proc. USITS, 2003. Google ScholarDigital Library
- X. Fan, W.-D. Weber, and L. A. Barroso. Power provisioning for a warehouse-sized computer. In Proc. ISCA, 2007. Google ScholarDigital Library
- J. Flinn and M. Satyanarayanan. Energy-aware adaptation for mobile applications. In Proc. SOSP, 1999. Google ScholarDigital Library
- A. Ganapathi, H. Kuno, and et al. Predicting multiple metrics for queries: Better decisions enabled by machine learning. In Proc. ICDE, 2009. Google ScholarDigital Library
- B. Gedik, H. Andrade, K.-L. Wu, P. S. Yu, and M. Doo. SPADE: the System S declarative stream processing engine. Proc. SIGMOD, 2008. Google ScholarDigital Library
- D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. Capacity management and demand prediction for next generation data centers. In Proc. ICWS, 2007.Google ScholarCross Ref
- Z. Gong and X. Gu. PAC: Pattern-driven Application Consolidation for Efficient Cloud Computing. In Proc. MASCOTS, 2010. Google ScholarDigital Library
- Z. Gong, X. Gu, and J. Wilkes. PRESS: PRedictive Elastic ReSource Scaling for Cloud Systems. In Proc. CNSM, 2010.Google Scholar
- S. Govindan, J. Choi, and et al. Statistical profiling-based techniques for effective power provisioning in data centers. In Proc. Eurosys, 2009. Google ScholarDigital Library
- B. Guenter, N. Jain, and C. Williams. Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning. In Proc. INFOCOM, 2011.Google ScholarCross Ref
- F. Hermenier, X. Lorca, J.-M. Menaud, G. Muller, and J. Lawall. Entropy: a consolidation manager for clusters. In Proc. VEE, 2009. Google ScholarDigital Library
- C.-H. Hsu and W.-C. Feng. A power-aware run-time system for high-performance computing. In Proc. SC, 2005. Google ScholarDigital Library
- E. Kalyvianaki, T. Charalambous, and S. Hand. Self-adaptive and self-configured CPU resource provisioning for virtualized servers using Kalman filters. In Proc. ICAC, 2009. Google ScholarDigital Library
- H. Lim, S. Babu, and J. Chase. Automated control for elastic storage. In Proc. ICAC, 2010. Google ScholarDigital Library
- P. Padala and et al. Adaptive control of virtualized resources in utility computing environments. In Proc. Eurosys, 2007. Google ScholarDigital Library
- P. Padala, K.-Y. Hou, K. G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, and A. Merchant. Automated control of multiple virtualized resources. In Proc. Eurosys, 2009. Google ScholarDigital Library
- J. Rolia, L. Cherkasova, M. Arlitt, and V. Machiraju. Supporting application QoS in shared resource pools. Communications of the ACM, 2006. Google ScholarDigital Library
- P. Shivam, S. Babu, and J. Chase. Learning application models for utility resource planning. In Proc. USITS, 2003.Google Scholar
- P. Shivam, S. Babu, and J. Chase. Active and accelerated learning of cost models for optimizing scientific applications. In Proc. VLDB, 2006. Google ScholarDigital Library
- S. S. Parekh, N. Gandhi, J. L. Hellerstein, D. M. Tilbury, T. Jayram, and J. P. Bigus. Using control theory to achieve service level objectives in performance management. In Real Time Systems, 2002. Google ScholarDigital Library
- C. Stewart, T. Kelly, A. Zhang, and K. Shen. A dollar from 15 cents: cross-platform management for internet services. In Proc. USENIX Annual Technical Conference, 2008. Google ScholarDigital Library
- B. Urgaonkar, M. S. G. Pacifici, P. J. Shenoy, and A. N. Tantawi. An analytical model for multi-tier internet services and its applications. In Proc. SIGMETRICS, 2005. Google ScholarDigital Library
- B. Urgaonkar, P. Shenoy, and et al. Resource overbooking and application profiling in shared hosting platforms. In Proc. OSDI, 2002. Google ScholarDigital Library
- T. Wood, L. Cherkasova, and et al. Profiling and modeling resource usage of virtualized applications. In Proc. Middleware, 2008. Google ScholarDigital Library
- T. Wood, P. J. Shenoy, A. Venkataramani, and M. S. Yousif. Black-box and gray-box strategies for virtual machine migration. In Proc. NSDI, 2007. Google ScholarDigital Library
- W. Yuan and K. Nahrstedt. Energy-efficient soft real-time CPU scheduling for mobile multimedia systems. In Proc. SOSP, 2003. Google ScholarDigital Library
- W. Zheng, R. Bianchini, and et al. JustRunIt: Experiment-based management of virtualized data centers. In Proc. USENIX Annual Technical Conference, 2009. Google ScholarDigital Library
- X. Zhu and et al. 1000 Islands: integrated capacity and workload management for the next generation data center. In Proc. ICAC, June 2008. Google ScholarDigital Library
- X. Zhu, Z. Wang, and S. Singhal. Utility-driven workload management using nested control design. In Proc. American Control Conference, 2006.Google Scholar
Index Terms
- CloudScale: elastic resource scaling for multi-tenant cloud systems
Recommendations
CloudScale: a novel middleware for building transparently scaling cloud applications
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied ComputingWith the promise of seemingly unlimited IT resources, the trend of cloud computing is currently revolutionizing software engineering. However, at the moment, building applications for the cloud is a rather cumbersome and manual task. In this paper, we ...
Optimal cloud resource auto-scaling for web applications
CCGRID '13: Proceedings of the 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid ComputingIn the on-demand cloud environment, web application providers have the potential to scale virtual resources up or down to achieve cost-effective outcomes. True elasticity and cost-effectiveness in the pay-per-use cloud business model, however, have not ...
Analysing the Migration Time of Live Migration of Multiple Virtual Machines
CLOSER 2014: Proceedings of the 4th International Conference on Cloud Computing and Services ScienceWorkload consolidation is one of the common approaches applied to achieve energy efficiency in data centres. It allows to reduce the number of physical machines needed to run the applications and thus, save energy. Workload consolidation can be realized ...
Comments