# A Dynamic Decision Support System for Selection of Cloud Storage Provider
## 2 Related Work  
This section provides a basic detail on DSSs, including factors that are involved in decision making and summarizes specific DSSs that assist in decision making when shifting data to the cloud computing environment. A DSS helps users to make decisions based on the intelligence, knowledge, and user requirements. A lot of considerations are required to evaluate a decision like the consequences of making the correct decision, costs, and risks [5]. In this research, DSSs are divided into five categories, and each has its own algorithm. These categories are knowledge-driven, model-driven, data-driven, communication-driven, and document-driven. The research in [6; 7] emphasized the main issues related to cloud security. An organized review of the risks in cloud computing from a cloud services perspective and client side, is proposed. In addition to the above-mentioned research models, researchers have focused on business issues through several methodologies that focus on the application side. The main issue is the focus on migrating applications without considering enterprise issues. Their research is conceptual and does not provide implementation descriptions to build a prototype. To account for the overall performance in cloud computing, we need to consider many diverse factors. If the consumer considers migration of a legacy application to the cloud, a DSS can be used to assess the cloud computing services. This system considers the reduction of risk and the relationship between computation performance and cost. A service called Service Measurement Index (SMI) [8] is an example of the service from such a system. A virtual machine (VM) and its services are characterized by principles that are calculated by proficiency parameters. The final rating is from the possible combinations of VM and service. Key performance indicators (KPI) are defined in order to measure and compare cloud services. An example is assurances from KPIs, like availability and stability of service [9]. The SMI model summarizes the most significant attributes for the service quality such as assurances of usability, cost, agility, performance, scalability, security, accountability, and privacy. The metamodeling technique [10] was adopted to develop a generic process model. The technique is evaluated and refined by the reviews from the domain experts and three users. It benefits organizations for migration to the cloud according to their circumstances. The research in [11] has investigated the major issues in developing an efficient cloud storage service for the personal health record (PHR) domain where the patient's data are stored on the cloud and doctors can access it from the cloud server for diagnoses. The performance of the cloud storage services is discussed with their strengths and weaknesses. A qualitative and quantitative analysis was performed on the PHR data. The features considered in the qualitative study chunking, bunding, de-duplication and compression; and the parameters considered in the quantitative study include control data overhead, impact of data size on number of packets as well as transmission rate, synchronization initialization time, and protocol overhead. After testing the cloud storage services, the average transmission achieved was 93%, 3%, and 4% for the application, control, and other data, respectively. [PERSON] et al. [12] suggested a methodology for understanding the architecture and benchmarking the personal storage services on the cloud. In this study, a comparison of eleven (11) storage services was provided through a case study. Their methodology suggested design choices and performance factors for the organizations. Their proposed work was tested on various workloads and the implemented tool is released on the public website ([[http://www.simpleweb.org/wiki/Cloudbenchmarks](http://www.simpleweb.org/wiki/Cloudbenchmarks)]([http://www.simpleweb.org/wiki/Cloudbenchmarks](http://www.simpleweb.org/wiki/Cloudbenchmarks))). A comparison tool \"CloudCmp\" was introduced by [PERSON] et al.