A roadmap on improved performance-centric cloud storage estimation approach for database system deployment in cloud environment

Abstract

Cloud computing has taken the limelight with respect to the present industry scenario due to its multi-tenant and pay-as-you-use models, where users need not bother about buying resources like hardware, software, infrastructure, etc. on an permanently basis. As much as the technological benefits, cloud computing also has its downside. By looking at its financial benefits, customers who cannot afford initial investments, choose cloud by compromising on its concerns, like security, performance, estimation, availability, etc. At the same time due to its risks, customers - relatively majority in number, avoid migration towards cloud. Considering this fact, performance and estimation are being the major critical factors for any application deployment in cloud environment; this paper brings the roadmap for an improved performance-centric cloud storage estimation approach, which is based on balanced PCTFree allocation technique for database systems deployment in cloud environment. Objective of this approach is to highlight the set of key activities that have to be jointly done by the database technical team and business users of the software system in order to perform an accurate analysis to arrive at estimation for sizing of the database. For the evaluation of this approach, an experiment has been performed through varied-size PCTFree allocations on an experimental setup with 100000 data records. The result of this experiment shows the impact of PCTFree configuration on database performance. Basis this fact, we propose an improved performance-centric cloud storage estimation approach in cloud. Further, this paper applies our improved performance-centric storage estimation approach on decision support system (DSS) as a case study.