Downtime-Free Live Migration in a Multitenant Database

  • Nicolas MichaelEmail author
  • Yixiao Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8904)


Multitenant databases provide database services to a large number of users, called tenants. In such environments, an efficient management of resources is essential for providers of these services in order to minimize their capital as well as operational costs. This is typically achieved by dynamic sharing of resources between tenants depending on their current demand, which allows providers to oversubscribe their infrastructure and increase the density (the number of supported tenants) of their database deployment. In order to react quickly to variability in demand and provide consistent quality of service to all tenants, a multitenant database must be very elastic and able to reallocate resources between tenants at a low cost and with minimal disruption. While some existing database and virtualization technologies accomplish this fairly well for resources within a server, the cost of migrating a tenant to a different server often remains high. We present an efficient technique for live migration of database tenants in a shared-disk architecture which imposes no downtime on the migrated tenant and reduces the amount of data to be copied to a minimum. We achieve this by gradually migrating database connections from the source to the target node of a database cluster using a self-adapting algorithm that minimizes performance impact for the migrated tenant. As part of the migration, only frequently accessed cache content is transferred from the source to the target server, while database integrity is guaranteed at all times. We thoroughly analyze the performance characteristics of this technique through experimental evaluation using various database workloads and parameters, and demonstrate that even databases with a size of 100 GB executing 2500 transactions per second can be migrated at a minimal cost with no downtime or failed transactions.


Database Live migration Multitenancy 


  1. 1.
    Barham, P., Dragovic, B., Fraser, K., Hand, S., et al.: Xen and the art of virtualization. ACM SIGOPS 37(5), 164–177 (2003)CrossRefGoogle Scholar
  2. 2.
    Barker, S., Chi, Y., Moon, H.J., Hacigümüş, H., et al.: Cut me some slack: Latency-aware live migration for databases. In: EDBT, pp. 432–443. ACM (2012)Google Scholar
  3. 3.
    Breitgand, D., Kutiel, G., Raz, D.: Cost-aware live migration of services in the cloud. In: SYSTOR (2010)Google Scholar
  4. 4.
    Chu, H., Kurakake, S., Song,Y.: Communication socket migration among different devices (2001)Google Scholar
  5. 5.
    Clark, C., Fraser, K., Hand, S., Hansen, J.G., et al.: Live migration of virtual machines. In: NSDI, pp. 273–286 (2005)Google Scholar
  6. 6.
    Cooper, B., Silberstein, A., Tam, E., Ramakrishnan, R., et al.: Benchmarking cloud serving systems with YCSB. In: ACM CLOUD, pp. 143–154. ACM (2010)Google Scholar
  7. 7.
    Das, S., Nishimura, S., Agrawal, D., El Abbadi, A.: Albatross: Lightweight elasticity in shared storage databases for the cloud using live data migration. PVLDB 4(8), 494–505 (2011)Google Scholar
  8. 8.
    Elmore, A.J., Das, S., Agrawal, D., El Abbadi, A.: Zephyr: Live migration in shared nothing databases for elastic cloud platforms. In: ACM SIGMOD, pp. 301–312. ACM (2011)Google Scholar
  9. 9.
    Gelhausen, J.: Oracle Database 12c product family. Oracle White Paper (2013)Google Scholar
  10. 10.
    Hankins, R., Diep, T., Annavaram, M., Hirano, B., et al.: Scaling and characterizing database workloads: Bridging the gap between research and practice. In: IEEE/ACM MICRO, p. 151. IEEE Computer Society (2003)Google Scholar
  11. 11.
    Hines, M.R., Gopalan, K.: Post-copy based live virtual machine migration using adaptive pre-paging and dynamic self-ballooning. In: ACM SIGOPS, pp. 51–60 (2009)Google Scholar
  12. 12.
    Hu, W., Hicks, A., Zhang, L., Dow, E.M., et al.: A quantitative study of virtual machine live migration. In: ACM CLOUD, p. 11. ACM (2013)Google Scholar
  13. 13.
    Huang, D., Ye, D., He, Q., Chen, J., et al.: Virt-LM: A benchmark for live migration of virtual machine. ACM SIGSOFT 36, 307–316 (2011)CrossRefGoogle Scholar
  14. 14.
    Jacobs, D., Aulbach, S., et al.: Ruminations on multi-tenant databases. In: BTW vol. 103, pp. 514–521 (2007)Google Scholar
  15. 15.
    Jiang, X., Yan, F., Ye, K.: Performance influence of live migration on multi-tier workloads in virtualization environments. In: IARIA CLOUD, pp. 72–81 (2012)Google Scholar
  16. 16.
    Kivity, A., Kamay, Y., Laor, D., Lublin, U., et al.: kvm: the Linux virtual machine monitor. Linux Symposium 1, 225–230 (2007)Google Scholar
  17. 17.
    Lahiri, T., Srihari, V., Chan, W., Macnaughton, N., et al.: Cache fusion: Extending shared-disk clusters with shared caches. VLDB 1, 683–686 (2001)Google Scholar
  18. 18.
    Liu, H., Jin, H., Xu, C.-Z., Liao, X.: Performance and energy modeling for live migration of virtual machines. Cluster Comput. 16(2), 249–264 (2013)CrossRefGoogle Scholar
  19. 19.
    Llewellyn, B.: Oracle Multitenant. Oracle White Paper (2013)Google Scholar
  20. 20.
    Mensah, K.: Oracle Database 12c Application Continuity for Java. Oracle White Paper (2013)Google Scholar
  21. 21.
    Michalewicz, M.: Oracle Real Application Clusters (RAC). Oracle White Paper (2013)Google Scholar
  22. 22.
    Microsoft: Server virtualization: Windows Server 2012 (2012)Google Scholar
  23. 23.
    Minhas, U., Rajagopalan, S., Cully, B., Aboulnaga, A., et al.: Remusdb: Transparent high availability for database systems. PVLDB 22(1), 29–45 (2013)Google Scholar
  24. 24.
    Nelson, M., Lim, B.-H., Hutchins, G., et al.: Fast transparent migration for virtual machines. In: USENIX, pp. 391–394 (2005)Google Scholar
  25. 25.
    Oracle: Best practices for building a virtualized SPARC computing environment. Oracle White Paper (2012)Google Scholar
  26. 26.
    Schroeder, B., Wierman, A., Harchol-Balter, M.: Open versus closed: A cautionary tale. NSDI 6, 18–18 (2006)Google Scholar
  27. 27.
    Shen, Y., Michael, N.: Oracle Multitenant on SuperCluster T5–8: Scalability study. Oracle White Paper (2014)Google Scholar
  28. 28.
    Stoica, R., Ailamaki, A.: Enabling efficient OS paging for main-memory OLTP databases. In: DaMoN, p. 7. ACM (2013)Google Scholar
  29. 29.
    The Transaction Processing Performance Council: TPC-C benchmark revision 5.11 (2010)Google Scholar
  30. 30.
    Waldspurger, C.A.: Memory resource management in VMware ESX server. ACM SIGOPS 36(SI), 181–194 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Oracle CorporationSanta ClaraUSA

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