# A Dynamic Decision Support System for Selection of Cloud Storage Provider
## 2 Related Work
An acceptable cost is an important parameter, so the client can easily shift the application to the cloud service. In this research, the basic decision process focuses on changing the determination of appropriate cloud vendors in relation to charges. This framework introduces three-layered engineering (client, server, software), which consolidates the front end for client communications. A learning base with data assembled from cloud providers on their services offers a back end that contains the vendor choice and cost-figuring rationale. A cloud service provider's information base is enhanced by adding more offerings that include information on different attributes like cost, number of CPU cores, storage size, synchronization, sharing, etc. Besides gathering cloud provider information (e.g., offers), it will also predict and prioritize a list of cloud storage and services that are suitable for customers. Main modules of MDSS are the user interface, an offerings matcher, a cost calculator, and a provider knowledge base (PKB). The customer interacts with the system through the user interface. A web form consists of different controls for gathering user requirements. Users enter their requirements as numerical or non-numerical values, where numerical values include the number of CPU cores, storage capacities, trial period availability, etc., whereas non-numerical values consist of attributes like file synchronization and scalability. Output from the user interface are the parameters entered by the users and input for the next module, i.e., the Offerings Matcher. In this module, the user requirements are checked against the providers' data and will produce the matched offerings. The next module is a cost calculator that takes input from both offer matching and knowledge provider modules, and calculates the cost of each matched offer. The PKB is the brain of MDSS where all cloud provider data reside. A model of this framework (shown in Figure 3) was executed as an internet application written in C# and Microsoft Sql Server on the.NET platform. The model was checked against an online apparatus, affirming the determination criteria, and clarifying the accuracy of cost estimation. The web form created in MDSS for taking user requirements is not user friendly, because some users are not aware of technical terms like _cores_, _GB_, _recurring_, etc. Secondly, the web form and controls used in this research do not provide selective options (i.e., user has to type data instead of selection). Another issue is the static database that may not reflect the latest offerings. The Reuse and Migration of Legacy Systems to Interoperable Cloud Service (REMICS) project by [PERSON] et al. [22] proposes a way to transfer legacy systems into the cloud service paradigm. It first understands the architecture and functionality of the current system; develops a new application to replace the current system, and finally uploads the new application to the cloud. Their project is based on the methods and model-driven engineering (MDE) tools. Only two cloud providers are showing offers (Google and Amazon), and have some common offerings with varying costs. In almost all the frameworks as discussed above,  
Figure 3: The Migration DSS Framework. none of them is a complete system that covers all aspects of a DSS for application migration to the cloud. The majority of the described systems emphasize only the vendor selection and calculation of costs. Besides these aspects, a decision-making system specific to application migration to the cloud needs to consider more dimensions. Along with calculating costs, other tasks such as performance forecast and countermeasures against security threats are critical.