# An SDN Focused Approach for Energy Aware Traffic Engineering in Data Centers
## 4 Emulation Results
Even for high traffic volumes, there is at least 28% benefit when taking into account the interface's power state behavior. Similar results can be achieved regardless of the selected topology as demonstrated in Figure 4b. The benefits of interface power scaling decrease as traffic increases since more interfaces operate closer to their maximum capacity consuming the maximum power. Power scaling is far more beneficial in the classic 3-tier topology due to the absence of redundant paths on core links and the fact that only a small portion of the links can be disabled. In the fat tree and leaf and spine topology, benefits are still significant compared to the benefits of disabling only unused interfaces even though a large number of redundant links can be switched off. Figure 4 demonstrates that a combined strategy of traffic engineering (to disable the highest number of interfaces) and exploiting interface power states is required so as to achieve the maximum benefits in power savings since greater values of power savings can be achieved regardless of the traffic volumes and topology size. We further compare our results to existing studies that do not consider hardware capabilities and only try to suspend unused interfaces. We selected the studies based on the relevance to our study and the methodology followed. Existing literature in this domain can be classified into two main categories: (a) studies that can be applied to all types of networks [35], (b) studies that focus only on DC topologies. In both cases full knowledge of the topology and the relevant traffic matrix is required. The optimal solution is applied either via SDN methods or following legacy approaches, i.e., by modifying the routing information. Since there is no direct comparison on topology level with the studies in the first category, we can only examine power savings achieved for equivalent average link utilizations of 10%, 50% and 90%. Compared to the results in Figure 6 of [35], our method can provide five times more energy savings in the worst-case scenario of 90% link utilization, achieving 53% power savings instead of only 10%. Considering 50% link utilization, we achieve more than triple savings namely 68% in our case compared to 22%. For low link utilizations of 10% the savings we get are more than double, 85% savings in our study compared to 35%. Compared to studies in the second category, we can consider the same traffic volumes and network topology. In particular, we compared our method towards the fat-tree network topology examined in [8] under mid-traffic profile (50% on near nodes, 50% on far nodes) for 1024 nodes and maximum link utilization of 20%. In this case, our approach can produce around 45% more savings, i.e., 65% instead of 45% power savings. For lower number of hosts and similar link utilization, we observe 55% more savings when our approach is applied. Based on the aforementioned comparison with DC  
Figure 4: Comparison between approaches based on disabling interfaces and our interface power scale proposal. Difference under (**a**) various workloads on leaf and spine topology, and (**b**) various topologies and number of devices for the same workload. focused methods, the energy savings potential of our method is far greater compared to approaches that try to optimize power consumption in DCs without considering hardware characteristics.