# Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization
## 4 CMRBC: System Model
The CMRBC model enables a reduction in cost for 500 servers because the broker does not necessarily create a VM for every job that is submitted. After all, jobs are already classified and merged to reduce the workload of the broker and to avoid brokerage by creating virtual machines per job. ## 6 Performance Evaluation

The performance of CMRBC is evaluated based on parameters such as the number of servers, resource cost, response time, and resource utilization. * Several servers: Servers are super-computers that are equipped with programs or hardware of specified configuration which meet certain prerequisites to enable the server to offer services to the computers on its network. Methods that require a relatively lower number of VMs to successfully meet the service quality requirements are not cost-effective. The capital cost is high if a greater number of VMs are used. To overcome this, Table 4 demonstrates the minimum number of servers required to execute the job in the CMRBC model compared to the previously used model, which utilizes a classification and merging technique. * Response Time: Response time is the total amount of time it takes to respond to a request for service, and it is a sum of service time and waiting time. Systems that have higher response times give poor service quality. Table 4 and Figure 5 demonstrate that the time required to execute a job is less in CMRBC compared to CURA. * Resource Cost: Resource cost is worked out based on the number of tasks and the number of VMs. In the CURA model, for every 10 tasks, an equal number of VMs are required, and for the CMRBC model, for the same number of tasks, half of the number of VMs are required, i.e., 5 VMs for 10 tasks are required, as shown in Figure 5 and Table 4.

Figure 4: Internal structure of job allocation to virtual machines. * Effective Utilization: Here, the effect of distinctive and decided-upon distributions of the deadline for the execution of CMRBC for various values is considered. It was found that both Poisson and uniform distribution require a comparatively fewer number of servers through exponential distribution. This requires 30 percent more servers, as there are more jobs with shorter deadlines in the exponential distribution. Table 4 shows the comparison results of the CURA and CMRBC models with parameters of cost, time, brokerage, RAM speed, and bandwidth. Considering the cost execution of various classification and merging methodologies, as shown in Figure 4, when requests fluctuate from clients without stable requests, this bends the cost contrast between the online calculations and Heuristic methods. In CMRBC, it is more noticeable when clients have stable requests, but it is difficult to precisely assess their future requests. 1. Number of Jobs Versus Number of servers

The experiments evaluated the performance of the techniques for various job sizes based on the quantum of data input. Small jobs process 50 MB of text data, medium jobs process 100 MB, and large and extra-large jobs process 150 and more than 200 GB, respectively. In addition, small, medium, and large jobs have a mean deadline of 50 s, and extra-large jobs have a mean deadline of 180 s, as it takes a bit more running time. It is seen that the performance, in terms of the number of servers required for processing a job under CMRBC, is much lower than the CURA model, as shown in Figure 5.

Improvements were made at a 1:4 ratio for the smaller jobs. A sincere attempt was made to prove that smaller jobs can be executed with minimum time consumption. Hence, it was proved that the CMRBC model requires significantly fewer resources even with peak work-load utilization, as the CURA model demands more resources. The CMRBC model consumes fewer resources, and the output would be at a much more optimal speed, with less consumption of time, and without any hindrance whatsoever. Further, this model will not create virtual machines (thereby reducing their usage) for every small job, because classification and merging techniques are used.