# An SDN Focused Approach for Energy Aware Traffic Engineering in Data Centers
## 4 Emulation Results
The next steps are according to power consumption data sheet from switch manufacturers [34]. Network sizes varied from 12 switches (4 server racks equivalent in real deployments) to 74 switches (32 server racks) were emulated. For each topology the exact figures for switches per network layer are listed in Table 2. Traffic profile (we evaluated only east-west communications) and total traffic volume were considered constant for each execution of the experiment. Figure 2 demonstrates the results for various network sizes and different network topologies. Even though there is a different level of redundancy, starting from limited resiliency in a 3-tier architecture up to the highest level of resiliency in leaf and spine topology, we recorded a power reduction of at least 65% in the worst case. By adding redundant paths, especially on the spine layer, the power savings increased up to 90%. Leaf and spine topology by design includes the higher number of redundant links compared to the other two topologies. Adding switches on the spine layer increases the number of redundant paths without serving more hosts and thus increases the power savings potential. There is no similar improvement in the other topologies although savings are around 70%. The classic 3-tier architecture has less redundant links. Energy savings derive from partially loaded link power states and are independent of the number of switches in each layer. In the fat tree architecture, where the number of alternate paths depends on the switches at the aggregation level, power consumption is affected only by the number of devices at this layer. Figure 1: Process followed during the simulation. On the leaf and spine topology, the most promising in terms of power savings potential, we examined the impact of various workloads. To compare different traffic volumes we introduce the concept of traffic amplifier, being a percentage of the same maximum traffic across all topologies. The maximum traffic is calculated assuming that hosts load the network as much as possible without violating the link capacity on a given topology. Traffic amplifier is the factor used to multiply the reference flows between the hosts (the ones resulting in full core link utilization without traffic engineering algorithm in place). Thus, a traffic factor of 10% corresponds to a random generation of flows between the hosts that lead to a maximum 10% utilization on the core links. Figure 3 depicts the impact of increasing traffic volumes on the energy savings in conjunction with the number of interfaces that operate on specific power states. The power state is derived from the traffic level at each interface, whereas on the 1 st state the load is 25% of the nominal and the 4 th state corresponds to the maximum capacity. As depicted in Figure 3, even for high load utilization, where a number of links have to operate on their maximum capacity, we still recorded energy savings up to 52%. Notice that, since the maximum gains are achieved when a link is at sleep state, the MIP solver tends to reroute traffic on selected links causing them to operate at higher rate, instead of distributing the load to multiple links which is the current mode of operation in existing DC deployments. As traffic increases, the margins for energy savings via traffic engineering are narrower as shown in Figure 3a where energy savings decrease as link utilization increases from 85% savings to nearly 50%. Figure 3: Analysis for average size leaf and spine topology under various traffic loads. (**a**) Energy savings from optimization algorithm. (**b**) Number of interfaces per state after the execution of the algorithm. Figure 2: Energy savings for various network topologies and sizes. Next, we evaluate the gains from considering enhanced power scaling capabilities of interfaces compared to only disabling unused interfaces and devices. In Figure 4a, we plot the energy savings gain versus traffic volume achieved in a leaf and spine topology.