Native support of multi-tenancy in RDBMS for software as a service
Proceedings of the 14th International Conference on Extending Database Technology
Predicting system performance for multi-tenant database workloads
Proceedings of the Fourth International Workshop on Testing Database Systems
Workload-aware database monitoring and consolidation
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Database system performance evaluation models: A survey
Performance Evaluation
Future Generation Computer Systems
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
SWAT: a lightweight load balancing method for multitenant databases
Proceedings of the 16th International Conference on Extending Database Technology
CloudOptimizer: multi-tenancy for I/O-bound OLAP workloads
Proceedings of the 16th International Conference on Extending Database Technology
PMAX: tenant placement in multitenant databases for profit maximization
Proceedings of the 16th International Conference on Extending Database Technology
Parallel analytics as a service
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
An index model for multitenant data storage in saas
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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Multi-tenant data management is a form of Software as a Service (SaaS), whereby a third party service provider hosts databases as a service and provides its customers with seamless mechanisms to create, store and access their databases at the host site. One of the main problems in such a system, as we shall discuss in this paper, is scalability, namely the ability to serve an increasing number of tenants without too much query performance degradation. A promising way to handle the scalability issue is to consolidate tuples from different tenants into the same shared tables. However, this approach introduces two problems: 1) The shared tables are too sparse. 2)Indexing on shared tables is not effective. To resolve the problems, we propose a multi-tenant database system called M-Store, which provides storage and indexing services for multi-tenants. To improve the scalability of the system, we develop two techniques in M-Store: Bitmap Interpreted Tuple(BIT) and Multi-Separated Index (MSI). BIT is efficient in that it does not store NULLs from unused attributes in the shared tables and MSI provides flexibility since it only indexes each tenant's own data on frequently accessed attributes. We extended MySQL based on our proposed design and conducted extensive experiments. The experimental results show that our proposed approach is a promising multi-tenancy storage and indexing scheme which can be easily integrated into existing DBMS.