# CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario
## 3 Results
### CoKnowMe: Architectural and Analytical Model
#### 3.3.8 Performance Evaluation in a Complex Networked Scenario  
Taking into consideration the complex approach used for the evaluation scheme of IoMT microservices and in order to present a performance evaluation to support our viewpoint, we have performed simulations, as shown in Figure 7, to display how a networked set of heterogeneous attributes that characterize the module of evaluation scheme proposed can be represented as \(N\), an interconnected population of node. We consider as a sample of a complex networked graph that enables us to detect what kind of attribute in structural terms can have a key role more than another. As explained in Section 2.6 we proceed through a WSM model, weighing up the contributions of each attribute and, as indicated in Figure 3, we assume that the attributes can be interconnected through weighted links. Thus, we consider a sample graph, having unit link weights, by varying the size of the set of attributes jointly with the complex topology that interconnect the attributes in the graph. We take into consideration three topologies: Scale-Free networks (SF), Erdos-Renyi networks (ER) and Small-World networks (SW). The ER is the classical random graph model, suitable in the case of networks governed only by stochasticity, characterized by a statistical homogeneity of the nodes. The SW network structure displays a high clustering coefficient and it is a network with a small diameter that generates homogeneous networks where the average of each metric is a typical value shared, with little variations, by all nodes of the network. SF is inherently heterogeneous, strictly resembling real-world networks displaying a skewed statistical distribution deriving from the preferential attachment rule. Many real-world networks exhibit levels of heterogeneity, structured in a hierarchy of nodes with a few nodes having very large connectivity (the hubs), while the vast majority of nodes have much smaller degrees. As shown in the Figure 7, varying the population size N, we can underline that, with a low value of N, both ER and SF display a skewed distribution. By increasing the size of the attributes, we detect a more homogenized distribution in ER and SW, while the higher heterogeneous distribution is in the SF topology. These findings can drive the choice of suitable topology, under specific assumptions, and the detection of certain attributes that exhibit a role of hub in the network structure, and therefore under conditions of particular requirements at edge level (e.g., load balancing issue); this suggests that certain attributes cannot be excluded from the assessment. Moreover, by considering a heterogeneous, dynamic and complex approach, in line with the trend of the forthcoming 6G, the more realistic network structure SF is able to unveil hidden properties and roles of nodes that can change dynamically based on use context and environment and requirement conditions. In addition, we conduct a performance evaluation on the quality-of-service measure, in function of the size of the attributes' population jointly with the variation of the clustering coefficient of the networked structure topology. To this aim, we include Figure 8 where we show that the QoS value increases with the increasing of the attributes' population size. The trend of QoS is shown in the case of two different topologies, the scale-free and the small-world structural network, assumed for both graphs, microservice's graph and evaluation's graph. The QoS depends on theoretical distribution of the values and weights of each attribute considered in the assumed sample. We shed light also on the clustering coefficient by varying the population size in both cases. The clustering coefficient is a complex metric, representing a measure of the degree to which nodes in a graph tend to cluster together and quantifying the abundance of connected triangles in a network.