# Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization
## 4 CMRBC: System Model  
The system architecture consists of the following components: users, classifier, merger, mapper, profiler and analyzer, cloudlet, broker, and data center, as shown in Figure 2.  
* Users: There are umpteen users who submit their work in the form of jobs to the cloud service provider, and in turn, these jobs are sent to the classifier. * Classifier: The classifier classifies the jobs into small, medium, large or extra-large based on the threshold value, and classified small jobs are submitted to the merger. * Merger: The merger segregates the small jobs based on their types. It reads small jobs in each group one by one and starts finding _larger_jobs_ by merging small jobs under consideration if _larger_jobs_' size is less than or equal to VM capacity. Once the _larger_job_'s size reaches VM capacity, it stops merging, and the _larger_job_ is submitted to the profiler and starts creating a new _larger_job_ for the next small job in the group. * Profiler and analyzer: The main goal of profiling is to reduce time and error. The profiler and analyzer analyze the job if it arrives for the first time and provides a set of the requirements such as _Memory_, _CPU_, _RAM_, _Bandwidth_, _Start-time_, _and_ End-timeto the mapper for the job to be executed, and these requirements are saved against the job. If the same type of job arrives, its requirements are retrieved and sent to the mapper. * Mapper: The mapping process makes sure not to violate the SLA constraints, and it is a process of mapping jobs with its requirement to cloudlet by interacting with the local schedulers of each data center after determining the availability of CPU, free time slots, and the expected number of resources to be utilized. * Cloudlet: The cloudlet is a mobility-enhanced, small-scale cloud data center that is located at the edge of the internet. When a _larger_job_ is submitted to a broker by the mapper, it converts the jobs into tasks with the attributes of job length, type, and time. * Broker: The broker can act as a negotiator or intermediator between user and service provider; the allocation of a job is performed by the broker to reduce time and effort by assigning the task to available resources in a data center based on users' Quality of Services (QoS). The broker interacts with different CSPs to provide effective resource utilization and decides which task should be executed in which virtual machine. Effective resource assignment strategies and allocation policies are required to satisfy users' needs to maximize the profit for cloud service providers and minimize the cost to cloud users. * Data Center: In cloud computing, data are not stored on computer hardware nor local machines, but they must be housed on physical drivers somewhere in a data center. A data center is a repository that contains servers, physical machines, virtual machines, and so on, and it can be homogeneous or heterogeneous according to its equipment designs. From Table 2, consider a set '\(J\)' of '\(n\)' jobs, where \(J=\{j_{1},j_{2},j_{3},\ldots j_{n}\}\), and set '\(VM\)' of '\(m\)' virtual machines in the system, where \(VM=\{vm_{1},vm_{2},vm_{3},\ldots\ vm_{m}\}\). When the request arrives from the cloud user, it is considered as a job, and for job sizes less than or equal to 50, 100, 150, and 200, the jobs are classified as small, medium, large, and extra-large, respectively. Since the size of the jobs varies, it is difficult to allocate virtual machines dynamically because the configuration of the virtual machine is the same for all types of jobs and it consumes the same number of resources, for example, the small job does not require a higher-configuration virtual machine. For small jobs, the less resources are required, and for large jobs, more are required. As such, the utilization of resources is not efficient. Hence, to avoid this, the 'classification and merging' method is proposed. The classification is completed using a classifier.