# An Intelligent Approach to Allocating Resources within an Agent-Based Cloud Computing Platform
## 5 Evaluation of the Proposed Model
Figure 6: Exchange of messages in the infrastructure distribution during the experiment. provided by each of the CBR agents from each physical server with available resources is then sent to the _Service Supervisor_ agent that initiated the process because of its own performance difficulties (step 3, Figure 6). Once it has received the set of proposed solutions from the different CBR-BDI agents, the agent overseeing the service sends an acceptance message to the _GMA_ that offers the greatest amount of resources for the new node that must be instantiated (step 4, Figure 6). Finally, the _GMA_ that receives the request asks the _LMA_ (step 5, Figure 6) from its machine to instantiate a new virtual node on the level of the VM associated with the service, according to the proposed problem solution. Once the new execution node has been launched, it is then necessary to evaluate the proposed solution. The _LMA_ evaluates the solution according to the degree of underused resources in the node that has just been instantiated. If a new resource distribution process must be conducted at the macro level, the SSA completes the evaluation performed by the _LMA_ in order to penalize the solution. In both cases, the efficiency of the proposed solution is evaluated according to the amount of underused resources of the new instantiated node. Results in terms of QoS are represented in Figures 7 and 8, showing also the increase in quality once the adaptation is finished. Subsequently, another set of experiments has been conducted for the distribution of infrastructure resources. In these experiments, we intended to perform numerous consecutive executions in the adaptation process, because a single execution of the proposed algorithm to perform the adaptation automatically could not satisfy the demand for the services. The result of the tests carried out from the point of view of the adaptation model was positive since it showed that it performed correctly, always within the limits of the case study and keeping the SLA level within the established limits. Figure 7: Experiment 1: Readjustment of the infrastructure resources for the method “GetFolderContent” (**(top left)** increase in request; **(top right)** time response of request; **(bottom)** the whole adaptation process). It is not possible to make an empirical comparison of the proposed model with other existing approaches in the state-of-the-art systems, as it is difficult to recreate the computer and/or simulation environments in which they have been evaluated. However, it is possible to make a theoretical comparison of the proposed approach with respect to other existing works in the state-of-the-art approaches. Firstly, it is observed that the proposed model follows a distributed approach to solving the problem, which totally differentiates it from existing works in the state-of-the-art approaches [16, 38]. This approach, which has been shown to be valid for the distribution of computational resources in this type of CC environment, has advantages with respect to their availability, since there is no single component in charge of the distribution of resources, but rather the system (society) itself is reorganized as a whole through the individual adaptation of its components (agents). In the state-of-the-art approaches, as detailed in particular by [PERSON] and [PERSON] [45], the execution of allocation algorithms is a complex task that requires a large amount of computational power and time. However, the proposed model simplifies the search for an adequate solution to the problem, as (i) the computational needs are distributed among different nodes; (ii) the value space to be considered is smaller since each node must only consider the data regarding its own resources and, furthermore, it is not necessary to have global knowledge of the platform; and, finally, (iii) each node can autonomously apply a partial solution to the problem, eliminating the coordination needs at the global level of the platform.