# Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization
## 4 CMRBC: System Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Cost** & **Time** & **Broker RAM** & **Speed** & **BDW** & **Server** \\ \hline CURA & 1588 & 946 & 10,564 & 256 & 500 & 1 GB \\ CMRBC & 1042 & 855 & 8555 & 174 & 300 & 2 GB \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of CURA and CMRBC models. Figure 5: Number of servers required in CURA and CMRBC model. 2. Number of Servers Versus Broker Cost

The comparison between the CURA and CMRBC models is shown in Figure 6. The \(x\)-axis represents the number of servers, and the \(y\)-axis represents broker cost. In CURA, to execute each job, one virtual machine is created. The user has to bear the cost for both the creation and termination of the VM, and thus, the cost is on the higher side, and so is the brokerage. In the CMRBC model, for every four jobs, one VM is created, which would automatically reduce the cost of their creation and termination. Hence, the creation ratio is 4, which is towards the cost of VMs. In CURA, four VMs have to be created, ultimately making users pay higher costs. However, in CMRBC, only a one-time cost is levied on the user, thereby reducing the cost to be borne, which amounts to a 4:1 ratio between CURA and CMRBC. 3. Number of Jobs Versus Resource Cost

In Figure 7, the cost and number of jobs are dealt with. The \(x\)-axis represents jobs, and the \(y\)-axis represents resource costs. Here, it is shown how the cost would be reduced by three-fourths. Figure 7 indicates the overall cost involved in executing a bunch of jobs. The CURA model shows a 65% higher cost than the actual cost incurred by a user under the CMRBC model. This is because of the usage of a lower number of VMs. Figure 6: Broker cost comparison between CURA and CMRBC models. Figure 7: Estimation of cost in CURA and CMRBC models. 4. Number of Jobs Versus Response Time

In Figure 8, emphasis is laid upon the time consumed for completing jobs, and response time is taken into account. The \(x\)-axis represents the number of jobs, and the \(y\)-axis represents response time. A comparison between the CURA model and the CMRBC model is shown. The time consumed for the creation of VMs in the CURA model is double when compared to the CMRBC model. This is due to the creation of fewer VMs. This also automatically reduces the time taken. ## 7 Conclusions

The proposed mechanism, the CMRBC model, was designed to reduce cloud brokerage cost and the overall resource cost using a classification and merging algorithm to optimize the resource cost to satisfy the clients and avoiding either underutilization or overutilization. This provided the efficient utilization of resources to the service provider. It was evaluated using a Poisson distribution in terms of the arrival of jobs for VM requests. The classification setting was completed using threshold values. The experiment was conducted considering the request of 1000 jobs and the request of VMs for four types of instances: small, medium, large, and extra-large. The creation of VMs for each job was avoided. This would automatically reduce brokerage due to the allocation, creation, or selection of VM resources. The CMRBC model was designed to overcome this problem. The results emerged show that the CMRBC model helps to minimize brokerage and maximize effective resource utilization in the above algorithm, to give good results, and to cater to the satisfaction of the clients. The proposed model optimizes two parameters of response, i.e., time and cost, by using multi-objective optimization. By deploying a broker approach, CMRBC does not consider large jobs and extra-large jobs to make resource utilization effective. D.B.K. and S.K.C. ; methodology, G.H.L. and F.F. ; software, G.H.L. and F.F. ; validation, D.B.K. and S.K.C. ; D.B.K. and S.K.C. ; writing--original draft preparation, D.B.K. and S.K.C. ; writing--review and editing, G.H.L. and F.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding.