# v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers +
## Data Center Design and the v-Mapper System Model
### VM Placement and the Data Center Model
Our technique minimizes the path length between two intercommunicating VMs. This leads to an efficient and better usage of the link and switches resources. It also results in an increase in

Figure 5: Topologies used in the performance evaluation. the blocking ratio of groups of VMs with a high intensive traffic rate. Therefore, the performance improvement of vmap is more significant in topologies with better connectivity and path diversity. ### Performance Cost

Performance can be assessed under numerous aspects. Here, we investigated scenarios where the hardware resources could be fully used. To evaluate the performance cost, we evaluated the performance of all the algorithms using a real workload obtained from our computing clusters. The details of 2500 jobs were extracted to evaluate their arrival times and flow duration statistics. These details were then catered for the Fat-Tree and B-Cube topologies. Figures 7 and 8 illustrate the detailed performance cost for all the compared algorithms with static and varying data traffic between VMs, respectively. From the results, it is clearly observed that as we increase the intensity of traffic between VMs, the overall performance cost increases. This increase results in congestion among communicating VMs and the slowing down of the networks' overall performance. Due to this, the performance of stp-bp declines. This can be understood as the stp-bp strategy is dependent on hop counts; therefore, if the traffic intensity among VMs is high, it adversely affects the algorithm's performance. On the other hand, vmap works independent of these constraints and has therefore the minimum cost among all the tested algorithms in the experimental set-up. Figure 6: Performance evaluation of the algorithms with the different data center topologies. Figure 7: Performance evaluation of the algorithms using the Fat-Tree (128 node) data center topology. ### VM Resource Occupancy

The distribution of VM image data should be based on the unit of chunks, instead of on entire VM image files. Herein, we first studied the distribution of the number of instances created from individual VMs. The VM size distribution is the amount of resources required by each VM instance to carry out discrete events. This categorization provides the basis for understanding how VM images are constructed and how similar they are. Different data center applications have their own patterns of resource consumption, e.g., CPU-intensive applications or memory-intensive applications, etc. To represent a clean and simple representation of a proposed scheme, our study defines only one type of resource consumption pattern. The evaluations were performed on 125-host B-Cube architecture with a synthesized traffic pattern. The application requests were categorized in four major categories i.e., uniform small, uniform large, random and bimodel demand. Further detail of the application request ranges employed in our experiments is given in \[\mathrm{p(q)}=\sum_{\mathrm{a.av=n}}\frac{\alpha_{1}^{\mathrm{a_{1}}}}{\alpha _{1}!},\ldots,\,\frac{\alpha_{1}^{\mathrm{a_{n}}}}{\alpha_{n}!}\mathrm{n}=0,1, 2,\ldots,\,\mathrm{N} \tag{8}\]0 The results in Figure 9 demonstrate that vmap outperformed the other algorithms in the uniform small and random schemes; whereas in the uniform large and bimodal demand schemes, its efficiency showed a 30 percent improvement compared to the other algorithms. This makes vmap (v-Mapper) a simple, feasible, yet practical VM management and resource consolidation solution. \begin{table}
\begin{tabular}{c c} \hline \hline
**Size** & **Request Range** \\ \hline Uniform small & 30 percent \\ Uniform large & 100 percent \\ Random & 0 to 100 percent \\ Bimodel demand & Normal distributions \(N\) (0:3:0:1) and \(N\) (0:7:0:1) with equal probability \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification of VM resources. Figure 8: Performance evaluation of the algorithms by varying the traffic intensity between VMs.