Adapt: adaptive database schema design for multi-tenant applications
Proceedings of the 21st ACM international conference on Information and knowledge management
Towards Elastic Multi-Tenant Database Replication with Quality of Service
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
PMAX: tenant placement in multitenant databases for profit maximization
Proceedings of the 16th International Conference on Extending Database Technology
A demonstration of SQLVM: performance isolation in multi-tenant relational database-as-a-service
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
RTP: robust tenant placement for elastic in-memory database clusters
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Characterizing tenant behavior for placement and crisis mitigation in multitenant DBMSs
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A vision for personalized service level agreements in the cloud
Proceedings of the Second Workshop on Data Analytics in the Cloud
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As traditional and mission-critical relational database workloads migrate to the cloud in the form of Database-as-a-Service (DaaS), there is an increasing motivation to provide performance goals in Service Level Objectives (SLOs). Providing such performance goals is challenging for DaaS providers as they must balance the performance that they can deliver to tenants and the data center's operating costs. In general, aggressively aggregating tenants on each server reduces the operating costs but degrades performance for the tenants, and vice versa. In this paper, we present a framework that takes as input the tenant workloads, their performance SLOs, and the server hardware that is available to the DaaS provider, and outputs a cost-effective recipe that specifies how much hardware to provision and how to schedule the tenants on each hardware resource. We evaluate our method and show that it produces effective solutions that can reduce the costs for the DaaS provider while meeting performance goals.