DBaaS Multitenancy, Auto-tuning and SLA Maintenance in Cloud Environments: a Brief Survey
DOI:
https://doi.org/10.5753/isys.2018.362Keywords:
DBaaS, Resource management, SLA, Multitenancy, Auto-tuningAbstract
Cloud computing is a paradigm that presents many advantages to both costumers and service providers, such as low upfront investment, pay-per-use and easiness of use, delivering/enabling scalable services using Internet technologies. Among many types of services we have today, Database as a Service (DBaaS) is the one where a database is provided in the cloud in all its aspects. Examples of aspects related to DBaaS utilization are data storage, resources management and SLA maintenance. In this context, an important feature, related to it, is resource management and performance, which can be done in many different ways for several reasons, such as saving money, time, and meeting the requirements agreed between client and provider, that are defined in the Service Level Agreement (SLA). A SLA usually tries to protect the costumer from not receiving the contracted service and to ensure that the provider reaches the profit intended. In this paper it is presented a classification based on three main parameters that aim to manage resources for enhancing the performance on DBaaS and guarantee that the SLA is respected for both user and provider sides benefit. The proposal is based upon a survey of existing research work efforts.
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Zhao, L. et al. (2015). A framework for consumer-centric sla management of cloud-hosted databases. IEEE Transactions on Services Computing.
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Abadi, D. et al. (2014). The beckman report on database research.SIGMOD Rec.,43(3):61–70.
Agrawal, D. et al. (2010). Big data and cloud computing: New wine or just new bottles?Proc. VLDB Endow.
Baker, J. et al. (2011). Megastore: Providing scalable, highly available storage for inter-active services. In Proceedings of the Conference on Innovative Data system Research(CIDR), pages 223–234.
Baliga, J. et al. (2011). Green cloud computing: Balancing energy in processing, storage,and transport.Proceedings of the IEEE, 99(1):149–167.
Cecchet, E. et al. (2011). Dolly: Virtualization-driven database provisioning for the cloud. SIGPLAN Not.
Chi, Y. et al. (2011). icbs: Incremental cost-based scheduling under piecewise linear slas.Proc. VLDB Endow.
Curino, C. et al. (2011). Relational cloud: A database service for the cloud. InCIDR.
Das, S. et al. (2011). Albatross: Lightweight elasticity in shared storage databases for the cloud using live data migration.Proc. VLDB Endow.
F. Chong, G. C. and Wolter, R. Multi-tenant data architecture [online].https://msdn.microsoft.com/en-us/library/aa479086.aspx. Accessed: Oc-tober 2016.
Funke, D., Brosig, F., and Faber, M. (2012). Towards truthful resource reservation in cloud computing. In Performance Evaluation Methodologies and Tools (VALUE-TOOLS), 2012 6th International Conference on, pages 253–262.
Gomes, E. and Dantas, M. A. R. (2014). An advance reservation mechanism to enhance throughput in an opportunistic high performance computing environment. In Network Computing and Applications (NCA), 2014 IEEE 13th International Symposium on,pages 221–228.
Hashem, I. A. T. et al. (2015). The rise of big data on cloud computing: Review and open research issues.Information Systems.
He, H. (2015). Virtual resource provision based on elastic reservation in cloud computing.Int. J. Netw. Virtual Organ., 15(1):30–47.
Jacobs, D. and Aulbach, S. (2007). Ruminations on multi-tenant databases. In BTW Proceedings, volume 103 of LNI. GI.
Jalaparti, V. et al. (2012). Bridging the tenant-provider gap in cloud services. In ACM Symposium on Cloud Computing. SoCC.
Kiefer, T. and Lehner, W. (2011). Private table database virtualization for dbaas. In Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on.
Lang, W. et al. (2012). Towards multi-tenant performance slos. In Data Engineering(ICDE), 2012 IEEE 28th International Conference on.
Liu, Z. et al. (2013). Pmax: Tenant placement in multitenant databases for profit maxi-mization. InProceedings of the 16th International Conference on Extending Database Technology, EDBT ’13, New York, NY, USA. ACM.
Mozafari, B. et al. (2013). Dbseer: Resource and performance prediction for building a next generation database cloud. In CIDR.
Narasayya, V. et al. (2013). Sqlvm: Performance isolation in multi-tenant relational database-as-a-service. In CIDR.
Narasayya, V. et al. (2015).Sharing buffer pool memory in multi-tenant relational database-as-a-service.Proc. VLDB Endow.
Ni, J. et al. (2014). Adaptive database schema design for multi-tenant data management.IEEE Transactions on Knowledge and Data Engineering.
O’Mullane, W. et al. (2005). Batch is back: Casjobs, serving multi-tb data on the web. In Web Services, 2005. ICWS 2005. Proceedings. 2005 IEEE International Conference on.
Ortiz, J. et al. (2015). Changing the face of database cloud services with personalized service level agreements. In CIDR.
Puthal, D. et al. (2015). Cloud computing features, issues, and challenges: A big picture. In Computational Intelligence and Networks (CINE), 2015 International Conference on.
Ran, Y. et al. (2013). Sla-driven dynamic resource provisioning for service provider in cloud computing. In Globecom Workshops (GC Wkshps), 2013 IEEE.
Rogers, J. et al. (2010). A generic auto-provisioning framework for cloud databases. In Data Engineering Workshops (ICDEW), 2010 IEEE 26th International Conference on.
Shasha, D. and Bonnet, P. (2002).Database Tuning: Principles, Experiments, and Trouble shooting Techniques. The Morgan Kaufmann Series in Data Management Systems.Morgan Kaufmann.
Stamatakis, D. and Papaemmanouil, O. (2014). Sla-driven workload management for cloud databases. In Data Engineering Workshops (ICDEW), 2014 IEEE 30th International Conference on.
Wang, L. et al. (2008). Scientific cloud computing: Early definition and experience. In High Performance Computing and Communications, 2008. HPCC ’08. 10th IEEEInternational Conference on.
Weissman, C. D. and Bobrowski, S. (2009). The design of the force.com multitenant internet application development platform. In Proceedings of the 2009 ACM SIG-MOD International Conference on Management of Data, SIGMOD ’09, New York,NY, USA. ACM.
Xiong, P. et al. (2011a). Activesla: A profit-oriented admission control framework for database-as-a-service providers. In Proceedings of the 2Nd ACM Symposium on Cloud Computing, SOCC ’11, New York, NY, USA. ACM.
Xiong, P. et al. (2011b). Intelligent management of virtualized resources for database systems in cloud environment. In Data Engineering (ICDE), 2011 IEEE 27th International Conference on.
Zhang, Q. et al. (2010). Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications.
Zhao, L. et al. (2015). A framework for consumer-centric sla management of cloud-hosted databases. IEEE Transactions on Services Computing.
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Published
2018-06-30
Como Citar
da Silveira Segalin, V., Friedrich Dorneles, C., & Ribeiro Dantas, M. A. (2018). DBaaS Multitenancy, Auto-tuning and SLA Maintenance in Cloud Environments: a Brief Survey. ISys - Revista Brasileira De Sistemas De Informação, 11(2), 30–42. https://doi.org/10.5753/isys.2018.362
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Section
Levantamentos (Surveys)
