Cloud computing has become a popular topic for exploration in both academic and industrial research in recent years. In this paper, network behavior is analyzed to assess and compare the costs and risks associated with traditional local servers versus those associated with cloud computing to determine the appropriate deployment strategy. An analytic framework of a deployment strategy that involves two mathematical models and the analytical hierarchy process is proposed to analyze the costs and service level agreements of services involving using traditional local servers and platform as service platforms in the cloud. Two websites are used as test sites to analyze the costs and risks of deploying services inGoogle App Engine(GAE) (1) the website ofInformation and Finance of Management(IFM) at theNational Chiao Tung University(NCTU) and (2) the NCTU website. If the examined websites were deployed in GAE, NCTU would save over 83.34% of the costs associated with using a traditional local server with low risk. Therefore, both the IFM and NCTU websites can be served appropriately in the cloud. Based on this strategy, a suggestion is proposed for managers and professionals.
In global open economy, transaction price estimation is an important issue in finance under stock price fluctuation. However, the transaction price is depend on the investors’ expect price which is difficult to be captured in used and implemented models for transaction price estimation. Therefore, this paper proposes the Transaction Price Estimation Model (TPEM) and designs a novel Real-time Transaction Price Estimation System (RTPES) which adopts the TPEM based on the efficient market hypothesis to estimate the investors’ expect price. In experiments, the simulation results show that the average accuracy of TPEM is 99.37%. This approach is feasible for stock investment decision-making.
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