本研究分析方法採用 Jack Freund & Jack Jones 發展之 Factor Analysis of Information Risk (FAIR) 模型，經由Panopta 公司所提供之監控服務獲取各大租借伺服器之供應商斷線資料，並利用蒙地卡羅方法模擬斷線歷時量化其斷線風險。 此外，本研究也同時提供一套新興指標作為企業評估伺服器服務商之方法，此方法為 Robert J. Aumann and Roberto Serrano在 An Economic Index of Riskiness 一文中所提出，可以作為企業初步選擇伺服器服務商時一個評估風險之指標。
研究結果說明了若公司採用了根據 An Economic Index of Riskness 提出之指標計算出的風險值作為選擇伺服器服務商之依據，可以有效的減少伺服器斷線所帶來的衝擊。 此外，在FAIR 方法發現斷線次數較斷線時長更會影響風險值之大小。;With the growing applications of cloud technology, this study aims to investigate the potential impact of network disconnections, thereby better assisting enterprises in their transition, as well as reducing risks from unstable infrastructure when using this technology in the future. This will facilitate risk transfer.
The analytical method of this study adopts the Factor Analysis of Information Risk (FAIR) model developed by Jack Freund & Jack Jones. Disconnection data from major server providers are obtained through the monitoring services provided by Panopta. The Monte Carlo method is used to simulate the duration of disconnection and quantify its risks. Additionally, this study provides a novel index as a method for enterprises to assess server service providers. This method, proposed by Robert J. Aumann and Roberto Serrano in their paper "An Economic Index of Riskiness," serves as a preliminary indicator for enterprises to evaluate the risks when choosing server service providers.
The research findings demonstrate that if a company uses the risk value calculated based on the index proposed in "An Economic Index of Riskiness" as the basis for selecting server service providers, it can effectively mitigate the impact of server disconnection. Moreover, the FAIR method reveals that the number of disconnections influences the risk value more significantly than the length of disconnection.
