## 4 Experimental Results and Discussion  
In the beginning, the criteria for evaluating SPs, cloud resources, and tasks should be determined. As stated in the introduction, the hierarchical SMI framework proposed by the CSMIC includes seven categories, and each category consists of several sub-attributes. Based on the SMI framework, criteria such as provider business stability, provider certifications, provider personnel requirements, profit or cost sharing, reputation, and trust can be used to evaluate various CSPs. In addition, criteria such as adaptability, portability, recoverability, reliability, performance, and cost can be used to evaluate cloud resources. However, in this study, four criteria were selected to measure the tendency of each task. These criteria were reputation, trust, performance, and cost. In experiment 1 and 2, the AHP was applied to rank a set of SPs, resources, and tasks. The AHP method was introduced by [PERSON] [39] as a theory of measurement to solve complex decision problems. A pairwise comparison matrix was used to derive the ratio scale in the AHP approach. The structure of the AHP consists of three levels, which are the goal, criterion, and alternative levels. At the goal level, the objective of applying the AHP or the decision problem that needs to be solved is identified. The criterion level contains all possible criteria related to the decision problem, and this affects the alternatives. In addition, the criterion level may include sub-criteria if they are needed during the structuring of the decision problem. The alternative level includes all possible solutions that are evaluated with respect to the selected criteria to find the optimal solution. However, in order to maintain consistency of judgment, the number of alternatives should be reasonably small; therefore, the AHP was adopted in Experiments 1 and 2 to consider a suitable number of alternatives. After structuring the decision problem, consumers provide their preferences for each AHP level and assess each element in the model. The preferences are entered into the pairwise comparisons in the AHP using the fundamental scale of comparisons. The Super Decision software (Version 2.10) [40] was used to count the aggregation judgments for the AHP and ANP. In Experiment 3, the ANP was applied; this is a generalized version of the AHP. The ANP's structure is based on a network that contains several nodes or clusters. Each node consists of several elements that are needed for the decision problem. These nodes represent various criteria and their sub-criteria. In addition, the ANP network should include an alternative node that contains the proposed solutions. The main difference between the ANP and the AHP is that in the ANP, the interdependence among the network elements is taken into consideration and feedback is allowed within the network structure. In the ANP, feedback means that high-node elements can be evaluated with respect to the low-node elements, and this cannot be achieved in a hierarchical structure. Similarly to the AHP, consumers' preferences are entered into the pairwise comparisons of the ANP using the fundamental scale of comparisons. The ANP allows decision makers to maintain more consistency with a large number of elements in the network with respect to its structure; therefore, it was adopted with large numbers in Experiment 3; however, the ANP method consumes more time than the AHP due to its complicated structure. The BWM was applied in Experiment 4 to rank several SPs, resources, and tasks. The BWM was introduced by [PERSON] [41] to overcome the heavy calculations of the AHP, which makes the BWM a light pairwise decision method. The BWM proved its power and reliability in structuring decision problems and achieving consistent results when compared with other MCDM techniques. In comparison with the AHP and ANP, the BWM allows decision makers to include large numbers of decision elements while maintaining consistency and simplicity by reducing the number of computations needed.