Evaluating Multi-tenant Live Migrations Effects on Performance
Multitenancy is an important feature for all Everything as a Service providers like Business Process Management as a Service. It allows to reduce the cost of the infrastructure since multiple tenants share the same service instances. However, tenants have dynamic workloads. The resource they share may not be sufficient at some point in time. It may require Cloud resource (re-)configurations to ensure a given Quality of Service. Tenants should be migrated without stopping the service from a configuration to another to meet their needs while minimizing operational costs on the provider side. Live migrations reveal many challenges: service interruption must be minimized and the impact on co-tenants should be minimal. In this paper, we investigate live tenants migrations duration and its effects on the migrated tenants as well as the co-located ones. To do so, we propose a generic approach to measure these effects for multi-tenant Software as a Service. Further, we propose a testing framework to simulate workloads, and observe the impact of live migrations on Business Process Management Systems. The experimental results highlight the efficiency of our approach and show that migration time depends on the size of data that have to be transferred and that the effects on co-located tenants should not be neglected.
KeywordsLive migration Multitenancy BPMS Performance
This work has been partly supported by the German Research Foundation (HO 5721/1-1, DECLARE), and by the Swiss National Science Foundation (project no. 178653). This work has been supported by Azure Research Grant. We thank heartfully Bonitasoft without whom this analysis could not have been done.
- 1.Clark, C., et al.: Live migration of virtual machines. In: Proceedings of the 2nd Conference on Symposium on Networked Systems Design and Implementation, vol. 2, pp. 273–286. USENIX Association (2005)Google Scholar
- 5.Elmore, A.J., Das, S., Agrawal, D., El Abbadi, A.: Zephyr: live migration in shared nothing databases for elastic cloud platforms. In: 2011 ACM SIGMOD. ACM, June 2011Google Scholar
- 7.Lang, W., Shankar, S., Patel, J.M., Kalhan, A.: Towards multi-tenant performance SLOs. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 702–713, April 2012Google Scholar
- 9.Liu, Z., Hacigümüs, H., Moon, H.J., Chi, Y., Hsiung, W.P.: PMAX: tenant placement in multitenant databases for profit maximization. In: EDBT (2013)Google Scholar
- 11.Schaffner, J., et al.: RTP: robust tenant placement for elastic in-memory database clusters. In: SIGMOD Conference (2013)Google Scholar
- 12.Taft, R., Lang, W., Duggan, J., Elmore, A.J., Stonebraker, M., DeWitt, D.: STeP: scalable tenant placement for managing database-as-a-service deployments. In: Proceedings of the Seventh ACM Symposium on Cloud Computing, pp. 388–400. SoCC 2016. ACM, New York (2016)Google Scholar