# Towards Rack Utilization in Internet Datacenters: An Approach Based on Dynamic Programming
## 5 Experiment
### Experiments Based on Real Data  
In this section, \(C_{unit}\) is used to measure the leasing cost of a rack, and it can be calculated by Formula (7). First, the branch and bound based server placement algorithm is implemented and evaluated on the experiment platform. Since the time complexity is \(O(n2^{n})\), where \(n\) is the number of servers, and the time spent to accomplish the optimal server arrangement is rather unreasonable.  
Then, we adopt the dynamic programming based server placement algorithm (\(DPS\)) to obtain the solution of server placement. The leasing cost and rack utilization obtained by \(DPS\) are compared with that of current server placement strategy from Tencent, and the experimental results are shown in Table 4. The current server placement strategy of Tencent is marked as \"no mechanism\", and the leasing cost is 4.12 \(C_{unit}\). Two groups of experiments are separately conducted at the modes with \"no power capping\" and \"no performance loss\", and they both use our algorithm based on dynamic  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Rack** & **Utilization** & **Model** & **Quantity** & **Power Rating** \\ \hline
0301-B01 & 62.2\% & DELL DCS6100 & 16(4 Nodes) & 629/4 \\ \hline
0301-B02 & 33\% & DELL DCS6100 & 4(4 Nodes) & 629/4 \\  & & HUAWEI XH320 & 4(4 Nodes) & 707.7/4 \\ \hline
0301-B03 & 11.6\% & IBM X3650M3 & 2 & 234.2 \\ \hline
0301-B04 & 28.6\% & HUAWEI RH2285 & 1 & 216.7 \\  & & DELL FS12-TY & 5 & 188 \\  & & IBM X3650M3 & 7 & 234.2 \\ \hline
0301-B05 & 64.9\% & DELL FS12-TY & 9 & 188 \\  & & IBM X3650M3 & 4 & 234.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information about servers and racks.  
programming. Specifically, \"no power capping\" indicates that all servers took _serverMaxPower_ as _serverBudget_, and \"no performance loss\" represents that the _serverBudget_ of servers is reduced to the level that would not cause performance loss through power capping.  
Table 4 shows that the current server placement strategy with no mechanism on the power of server and rack has a very low utilization, and its leasing cost is about 1.7 times than that of the solution optimized by the proposed algorithm without power capping.  
Figure 5 shows the power utilization of each rack at different server placement modes. When using no mechanism, the lowest power utilization of rack is only 11.6% and the highest one just reaches 64.9%. After adopting two solutions based on the algorithm proposed in this paper, the first \((n_{u}-1)\) racks (suppose there are \(n_{u}\) racks in total) all reach or approach the power utilization of 100%. Therefore, our algorithm is capable of fully reducing the rack fragment to enhance the overall rack utilization. Through experimental results, it can be seen that the leasing cost can be substantially saved by implementing power capping and server arrangement based on our algorithm for the racks and servers in the Tencent server room. In addition, as for different levels of power capping, our algorithm can provide approximately optimal solutions corresponding to different leasing costs.