Efficient and scalable multitenant placement approach for in-memory database over supple architecture

Abstract

Of late Multitenant model with In-Memory database has become prominent area for research. The paper has used advantages of multitenancy to reduce the cost for hardware, labor and make availability of storage by sharing database memory and file execution. The purpose of this paper is to give overview of proposed Supple architecture for implementing in-memory database backend and multitenancy, applicable in public and private cloud settings. Backend in-memory database uses column-oriented approach with dictionary based compression technique. We used dedicated sample benchmark for the workload processing and also adopt the SLA penalty model. In particular, we present two approximation algorithms, Multi-tenant placement (MTP) and Best-fit Greedy to show the quality of tenant placement. The experimental results show that Multi-tenant placement (MTP) algorithm is scalable and efficient in comparison with Best-fit Greedy Algorithm over proposed architecture.