## 6 Discussion  
Our proposed mechanism Adaptive TrimTree is aimed at reducing power consumption in data center networks while keeping the associated costs acceptable. The results in Figure 7 show power consumption reduction when Adaptive TrimTree is used as compared to ElasticTree [11]. We observe that Adaptive TrimTree reduces power consumption by almost 20% as compared to the ElasticTree protocol. Generally in network energy consumption reduction schemes, there is a tradeoff between power-related metrics such as network power consumption, network energy efficiency, network power usage effectiveness, etc. and performance-related metrics such as network latency, bandwidth oversubscription ratio, network losses, average link utilization ratio, etc. and the goal of the designer is to keep this tradeoff acceptable [32]. In the analysis of our proposed scheme, we selected power consumption as the power-related metric and link utilization, packet loss percentage and average delay as the performance-related metrics which affect network performance in terms of Quality of Service (QoS). We discussed link utilization in Section 5.1 and observed that the link utilization is an indicator of the utilization of links which must not be under-utilized or over-utilized. For link utilization of 55%, packet loss percentage is 1% which if exceeded beyond this limit results in unacceptable losses due to queue buildup. In order to observe the impact on packet loss percentage and delay, we carried out simulations and obtained results shown in Figures 8 and 9. Figure 8 shows that the average packet loss percentage is slightly higher in Adaptive TrimTree as compared to ElasticTree, however it is much less than 1%. This is due to the fact that when the packet loss exceeds a threshold, links are made to operate at their full rated capacity and if it still exceeds this limit, selective connectedness triggers passive switches to ON mode thus reducing loss at a cost of lower power reduction. We also observe that the nature of packet loss in Adaptive TrimTree is not bursty while burst losses are more common in ElasticTree. The impact of Adaptive TrimTree on network delay is observed in Figure 9 which shows that there is a slight increase in delay which is almost negligibly higher than ElasticTree. Overall our results show that at the cost of slightly higher packet loss percentage and delay, Adaptive TrimTree achieves significant power consumption improvements. The active and passive network subsets are computed after fixed time intervals which can be controlled. Based on QoS requirements for various applications, if traffic patterns are expected to vary too often, the network operator can minimize this time for minimizing the tradeoffs while saving power consumption. In order to study particular behavior of data center network applications, network profiling can be used. One such profiler is proposed and discussed in [34] which assist operators and developers in diagnosing performance problems for different data center applications which are becoming complex and more distributed. This type of application profiler can be employed by the network operator in order to best adjust the tradeoffs between power reduction and QoS. The results of our mathematical analysis show that the average power saved by Adaptive TrimTree as compared to ElasticTree is approximate 9%, while our simulation results are more promising and show an improvement of 19.6%. This is due to the fact that in our model, we assumed that around 25% of ports operate at reduced data rates of 100 Mbps while the remaining switches operate at the full data rate of 1 Gbps which can be termed as the worst case scenario because [PERSON] et al. [9], based on 5-day measurements, report that 90% of links can be set at 10 or 100 Mbps and less than 5% of links need to be set at 1 Gbps. These settings would result in a much higher percentage of improvement.