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Method and System for Determining Continued Viability of a Cloud Computing Platform

IP.com Disclosure Number: IPCOM000210396D
Publication Date: 2011-Sep-02
Document File: 5 page(s) / 152K

Publishing Venue

The IP.com Prior Art Database

Abstract

A method and system is provided to determine continued viability of a cloud computing platform. A predictive analytical model is utilized for determining the continued viability of a cloud computing platform by considering different factors associated with a customer such as time, industry type, and supply-demand situation, etc.

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Method and System for Determining Continued Viability of a Cloud Computing Platform

Disclosed is a method and system for determining continued viability of a cloud computing platform.

A fluid predictive analytical model is utilized for determining continued viability of a cloud computing platform. The predictive analytical model considers different factors, such as storage requirements, environment, geography, industry type, historical cases of a customer, and maturity of a business process for predicting if an implementation of the cloud computing platform would be viable for the customer. The customer may implement the cloud computing platform partially or completely based on the predictive analytical model and component architecture of their cloud solution. The predictive analytical model is dynamically fine-tuned as new contexts and technologies emerge or based on predicted service levels of hosting alternatives. The storage requirements, environment, and historical cases of customers are augmented in a repository under different domains such as industry verticals, types of customers, etc. The predictive analytical model is configured to support variable weighting of the factors to be considered. For example, predictive analytical model varies the weight when the factors change based on time, industry type, supply-demand situation, etc.

Furthermore, while transitioning to a cloud computing platform, the predictive analytical model also assesses parameters such as the workload of the customer, the resilience of business process execution to service availability, data security and privacy implications, cost of the current environment, level of flexibility that may be required for using the cloud computing platform, usage patterns of applications, availability of skilled infrastructure management resources, and historical Service Level Agreement (SLA) performances of a dedicated hosting environment against a cloud computing platform in regards to the customer.

Additionally, the predictive analytical model dynamically reassesses the transition between a dedicated hosting environment and a cloud computing platform for the customer. For example, predictive analytical model reassess the transition when the dedicated hosting environment becomes unstable or the cost of using the cloud computing platform becomes prohibitive. A fluid decision model may be utilized by the customer to dynamically measure the ROI of each alternative. In an embodiment, the predictive analytical model may decide on the transition based on customer preferences. For example, a customer may require certain parts of resources in a cloud computing platform and certain parts in a dedicated hosting environment. Thus, the predictive analytical model considers various factors before performing the transition.

Fig 1 illustrates an embodiment where a feasibility report is generated based on different factors using the predictive analytical model. A historical database...