Practical Approach for Achieving Minimum Data Sets Storage Cost In Cloud

Traditionally, computing has meant calculating results and then storing those results for later use. Unfortunately, committing large volume of rarely used data to storage wastes space and energy, making it a very expensive strategy. Cloud computing, with its readily available and flexibly allocatable computing resources, suggests an alternative: storing the provenance data, and means to recomputing results as needed. It is used to deploy computation and data intensive application without infrastructure investment. Large application datasets can be stored in the cloud. They are based on Pay as you go model. It is used for Cost Efficient Storage of large volume of generated datasets in the cloud. All these are done for achieving the minimum cost Benchmark in cloud. The main focus of this strategy is the local-optimization for the trade off between computation and storage, while secondarily also taking users’ (optional) preferences on storage into consideration. Both theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications with Amazon’s cost model show that the cost effectiveness of our strategy is close to or even the same as the minimum cost benchmark, and the efficiency is very high for practical runtime utilization in the cloud. KEYWORDS— cloud computing, computation-storage, data intensive application, dataset storage, minimum cost benchmark, trade off, data set cost