Data Decomposition Based Partial Replication Model for Software Services

Nowadays many software services are hosted in the Cloud. When there are more requests on these services, there are also more queries sent to the underlying database. In order to keep up with the increasing workload, it is necessary to have multiple servers hosting the data. Some cloud providers offer the full data replication solution. However, this solution only works when the load mainly consists of the read requests, and when the number of write requests increases, it does not scale well. Although data decomposition has been widely used in data-intensive web sites, not much study has been done on how to decompose the underlying data of software services for the purpose of data replication. In this paper, we propose a data-decomposition-based partial replication model for software services. We devise an automatic algorithm for data decomposition under the constraint of the capacity limit of the host machines. We evaluate our approach from two aspects: scalability and performance, using two benchmarks: RUBiS and TPC-W. In the experiment, we test the algorithm using different workload inputs, and also compare our approach with the full data replication approach.