## 3 Results and Discussion  
### Cloud Connectivity Scheduling  
Clustering is one technique that is widely used in analyzing data; its use is widely applied in the fields of computer science, statistics, biology and big data. Moreover, clustering refers to a group of datasets that are divided into different classes in the same cluster. Here, datasets are differentiated by their level of similiarity, such as high level degree and minimal level degree of similarity. Clustering is the process of partitioning a set of data objects into subsets called clusters, where the clustering process is accomplished through applying a computer algorithm. According to [PERSON] [10], the conditions and challenges that must be met by a clustering algorithm are: Scalability, Ability to Analyze Various Forms of Data, Find Clusters with Unexpected Shapes, Ability to Handle Noise, Sensitivity to input changes, Ability to do clustering for high dimensionality data, and Ability to do clustering for high dimensional data Interpretation and usability. This study refers to the implementation of the high-possibilistic c-means (HOPCM) algorithm approach for clustering a set of data. The HOPCM algorithm was developed to strengthen a previous clustering algorithm namely the possibilistic c-means (PCM) algorithm, to cover the issues of big data  
Figure 3: Example of application workflow. clustering with heterogeneous data. HOPCM can work well for clustering heterogeneous data that has many data attributes, a large number of data samples and diverse data sources. [PERSON] et al. (2018) developed HOPCM algorithm by defining the Objective Function as follows:  
\[J_{m}(U,V)=\sum_{i=0}^{c}\ \cdot\sum_{j=1}^{n}U_{ij}^{m}\ \sum_{i_{1}\ldots i_{N}}^{ l_{1}\ldots l_{N}}\Big{(}x_{ji_{1}\ldots i_{N}}\ -\ V_{ii_{1}\ldots i_{N}}\ \Big{)}^{2}\ +\ \sum_{i=1}^{c}\eta_{i}\sum_{j=1}^{n} \Big{(}1-\ U_{ij}\Big{)}^{2} \tag{6}\]  
where \(V=\{V1,\ V2,\...,\ Vc\}\) are a set of cluster centers and \(u_{ij}\) is membership value. By simplifying the objective function above, two equations will be produced, namely the membership matrix function and clustering center functions, represented by the following equations:  
\[U_{ij}=\frac{1}{\left(1+\left(\frac{d_{(T)ij}^{2}}{\eta_{i}}\right)^{1/(m-1)} \right)} \tag{7}\]  
\[V_{ii_{1}i_{2}\.....\ i_{N}}=\frac{\sum_{j=1}^{n}U_{ij}^{m}X_{ji_{1}i_{2}\ldots i_{N}} }{\sum_{j=1}^{n}U_{ij}^{m}} \tag{8}\]  
where \(d_{(T)ij}^{2}\) is the distance between \(X_{\text{j}}\) and \(V_{\text{i}}\) given by the following equation:  
\[d_{(T)ij}^{2}=\sum_{i_{1}\ldots i_{N}}^{l_{1}\ldots l_{N}}\Big{(}x_{ji_{1} \ldots i_{N}}\ -\ V_{ii_{1}\ldots i_{N}}\ \Big{)}^{2} \tag{9}\]  
The result of a joint test of the HOPCM clustering algorithm with scheduling FDHEFT as tested by [PERSON] et al. (2015) is shown in Tables 1 and 2 for five series. It is very promising to be implemented within cloud applications in the time of COVID19. After a while, the results were compared with other schematic algorithms, namely \(\varepsilon\)-fuzzy PSO, NSPSO, SPEA2*, and MOHEFT related to monetary cost and workflow execution time. The result is a combination of the HOPCM clustering algorithm with FDHEFT scheduling algorithm that produces the best performance related to the reduction of monetary cost and time of excess workflow in the cloud environment compared with: (1) \(\varepsilon\)-fuzzy PSO algorithm ([PERSON] et al., 2018), (2) NSPSO ([PERSON] et al., 2018), (3) SPEA2* ([PERSON] et al., 2018), and (4) MOHEFT ([PERSON] et al., 2018).