A Pareto-Based Data Placement Strategy in Database as a Service Model

DaaS (database as a service) as a new data provisioning pattern, has been widely applied. With the arrival of the era of big data, the amount of data is increasing dramatically and the data placement problem in DaaS model becomes more and more important. How to place the data according to their different performance requirements has an important effect on improving the quality of service, enhancing user experience and reducing the service cost. However, for service providers, improving service quality and reducing service cost are contradictory. It can't use less cost to buy the higher performance servers so that it is difficult to provide better service quality for users. It models the DaaS data placement problem as the DaaS data placement graph. Then using the pareto optimal thought, it puts forward a DaaS data placement strategy based on performance-cost equilibrium, including the initial solutions generation stage, multi-objective optimization stage and the final solution generation stage. It supports that DaaS service providers use less cost to provide the better service quality for users. Compared with the random strategy and greedy strategy through experiments, the strategy proposed in this paper can effectively reduce the resources cost while provide better service quality. It achieves the trade-off of the service performance and resources cost.