## 1 Introduction
RAS is organized into 3 functions--Discovery of resources, monitoring of resources and dynamic allocation. The main goal is to achieve high utilization. The total resource allocation cost is not taken into account and the VM having minimum resource requirements suffer lower delay. In case of similar requirements, the VM have a random, equal waiting time. Two strategies focusing on the total execution time are found in Reference [12; 13]. [PERSON] et al. present a resource allocation scheme, which is based on the job features. The jobs are assigned priorities and high priority jobs may well take the place of jobs with low priorities. In Reference [13], the authors use the concept of \"skewness\" to measure the unevenness in the multidimensional resource utilization of a server. Different types of workloads are combined by minimizing the skewness and the strategy aims at achieving low execution times by balancing the load distributed over time. Table 1 summarizes the discussion so far, by indicating the metrics considered by the papers described. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper Reference** & **Cost** & **Execution Time** & **Utilization** \\ \hline
[6] & Yes & No & No \\ \hline
[7] & Yes & No & Yes \\ \hline
[8] & Yes & Yes & No \\ \hline
[10] & Yes & No & No \\ \hline
[11] & No & No & Yes \\ \hline
[9] & Yes & Yes & No \\ \hline
[12] & No & Yes & Yes \\ \hline
[13] & No & Yes & Yes \\ \hline Our work & No & No & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related papers, based on the metrics used to evaluate resource allocation. Generally, the simulators have an important role in the development of every software application. When a researcher tries to design a resource allocation strategy for the data centers of a cloud, he focuses on the aspects mentioned above, that is, the minimization of the resource allocation cost, the overall system utilization and the job execution time. A well-developed simulator will help the researcher to grasp the main issues and challenges of the problem like the resource allocation strategy to be used, the choice of the basic cloud resources (in an initial stage, it is necessary to keep the most important resources, so that the simulator can be tested easily), and the other factors that are important (for example, an input rate regulation policy, as will be discussed in the next section). The simulator will provide answers to questions involving the effectiveness of resource utilization, or the \"fair\" distribution among the competitive user jobs. Also, it may prove that an allocation strategy performs better under certain job input rates, which are regulated based on the service time of the resource allocations over the cloud. In the remaining of this section, we briefly discuss a few of the typical cloud simulators found in the literature, which include modules for resource allocation based on a certain policy. A detailed presentation of the existing cloud simulators can be found in a very recent comprehensive work presented by [PERSON] et al. [14]. One of the first cloud simulators, was introduced by [PERSON] et al. [15] and was named CloudSim. This simulator was the basis for the development for a number of other simulating tools (examples include References [16; 17; 18; 19; 20]). CloudSim supports allocation provisioning at two levels--(1) host level and (2), at the Virtual Machine level. At the host level, decisions are taken regarding the percentage of the processing power of each cort that will be allocated to each VM. At the VM level, the VM assigns a constant percentage of the processing power to the individual jobs within the VM. [PERSON] et al. [21] presented UCloud, which was developed for usage in a university environment. The model combines public and private clouds and the resource allocation policy is based on information like performance monitoring or security management.