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Information technology systems deployed by enterprises should not only fulfil their business functionalities but also cater to Quality of Service concerns such as Availability, Scalability, and Performance. To enhance the system performance, the system availability is an important factor and to improve the system availability, one of the strategies is replicating the frequently accessed data to multiple suitable locations which is a practical choice as the users can access the data from a nearby site. This is, however, not the case for replicas which must have a preset number of copies on several locations. How to decide a sensible number and right location for replicas have become an important issue in cloud computing. In this paper, we show a dynamic data replication strategy to enhance the performance of software system. To identify the suitable file to replicate and to decide respective number of replicas, we calculate popularity degree and replica factor. We use the fuzzy logic system to identify the system to place the replicas and we use the round robin method to place the replicas in the identified systems. We compare the performance of our technique with the existing technique.

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Abstract-Query Optimization is at the core for contribution towards performance improvements in application systems. A lot of ideas have been proposed towards Query Optimization and there is lot of On-going research happening in this area. Virtually every commercial query optimizer chooses the best plan for a query using a cost model which is based on cardinality estimation. If cardinality estimation is inaccurate, then this may result in optimizer to choose a sub-optimal plan. But once the optimizer chooses an optimal plan for execution based on the approach of POP, the need for generating an optimal plan for subsequent execution of the same query at a later point in time can be minimized/reduced/exempted by storing the execution plan. This paper proposes a Model for building Dynamic Indexes & Storage and Re-Use of Optimal Query plans generated thru Progressive Optimization (POP) for performance gains. This approach is an extension to the work implemented in "Robust Query Processing through Progressive Optimization". This paper proposes a model to build Learning system within the database to analyze the stream of incoming queries and project viable indexes as against the initial indexes created by the Administrator and also store and re-use of Optimal Query Plans generated thru Progressive Query Optimization (POP).Index Terms-QoS, PoP.

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