Breaking the Giant: An Open and Dispersed Cloud Platform for Financial Software Development

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基本資料

Project title

Code/計畫編號

MOST104-2221-E019-050

Translated Name/計畫中文名

解構巨人：建立開放及分散式財務工程軟體之雲端平台

Project Coordinator/計畫主持人

William Wei-Yuan Hsu

Funding Organization/主管機關

National Science and Technology Council

Department/Unit

Department of Computer Science and Engineering

Website

https://www.grb.gov.tw/search/planDetail?id=11578009

Year

2015

Start date/計畫起

01-08-2014

Expected Completion/計畫迄

31-07-2016

Bugetid/研究經費

583千元

ResearchField/研究領域

資訊科學--軟體

預測金融資產的價格與風險評量一直都是研究的重點，亦是個人與金融公司迫切需要的。為了打破傳統的金融軟體 - 集中且壟斷式的設計，我們在此計畫中提出了FinancialCloud架構構想。 FinancialCloud之設計是基於雲端計算之開放分散式架構，可在其中設置各種金融計算軟體與服務，並可依照使用者需求動態的調整各模組之間的連結。在此架構之頂端，會有許多不同的金融軟體服務的提供者，如市場資料供應商會收集市場資料並重新整理販售。線上亦可添購巨觀經濟指標或利率資料與金融商品分析之模組。仲介商可能選用信用評價或商品評價服務，交易員可能使用參數分析服務來微調他們私有之模型來獲取最大利益。服務提供者會專注於增進他們演算法之效能。而另一方面，使用者只須負擔所使用到的服務費用，與之前使用金融軟體必須照單全買形成對比。 FiancialCloud之原型待解決之問題如下。第一，在這軟體生態圈之內需要標準的訊息溝通協定，才能讓新加入者無痕進入。第二，須提供3-A: Accounting, Authorizing, and Authentication。Accounting管轄著整個系統的帳務，誰用了什麼與貢獻者之間如何拆帳。Authorizing管轄了誰買了什麼服務。而Authentication則是用來確保FinancialCloud中傳遞的資料是真實可信的。最後，是科學計算模組之需求。科學模組所需的資源大於一般服務，可能需要巨量CPU與I/O，進而拖延後進之工作要求。這些節點需要高效率之演算法與彈性的資源來支撐著。在本計畫內我們將深入的探討這些議題。 Predicting prices and risk measures of assets and derivatives and rating of financial products have been studied and widely used by financial institutions and individual investors. In contrast to the centralized and oligopoly nature of the existing financial information services, in this project, we bring forth and extend the FinancialCloud, i.e., an open distributed framework based cloud computing to host modularize financial services such that these modularized financial services may easily be integrated on demand to meet users’ needs on demand. This cloud-based architecture of modularized financial services provides several advantages. We may have different types of service provides in the ecosystem on top of the framework. For example, market data resellers may collect and sell a long-term historical market data. Statistical analyses of macroeconomic indices, interest rates, and correlation of a set of assets may also be purchased online. Some agencies might be interested in providing services based on rating or pricing values of financial products in the future. Traders may use the statistically estimated parameters to fine-tune their trading algorithm to maximize the profit of his customers Providers of each service module may focus on effectiveness, performance, robustness, and security of their innovative products. On the other hand, a user pays for exactly what one uses to optimally manage their assets. A user may also acquire services through an online agent who is an expert in assessing the structural model and quality of existing products and thus assembles service modules matching user’s risk taking behavior. The prototype FinancialCloud reveled problems yet to be solved. First, it is the way messages are passed through the ecosystem. It must be standardized to let other join seamlessly. Second, the 3-A’s: Accounting, Authorizing, and Authentication must be developed. Accounting keeps track of who used what and how should the profit be split between contributors. Authorizing allows or denies services to users according to their subscription. Finally, authentication helps ensure information transferred throughout the FinancialCloud is correct and authentic. Last, high resource consumption nodes for scientific computation must be designed. These nodes require high CPU or I/O that may deny further requests for a long period of time. It must be fitted with good algorithms and flexible enough resource to support the ecosystem. We will address these issues thoroughly in this project.

DSpace CRIS

Breaking the Giant: An Open and Dispersed Cloud Platform for Financial Software Development

基本資料

Description