## 4 Experiments  
We evaluated the proposed mechanism with experiments conducted on a real 29-node testbed set up in a topology that mimics the CESNET2 network [32] as shown in Figure 2. In the experiments, as our focus is on the supporting wired network infrastructure, we do not differentiate based on how the users connect to it. For example, it could be a mobile user connecting through a base station to the network or connecting directly. Our focus is only on the duration that each user (mobile or not) is connected to the backbone network and the impact that an energy saving strategy of turning equipment OFF could have on the user's QoS. Our testbed consists of 29 PC-based physical routers running Ubuntu Linux 2.6.32-25. Their BIOS was configured to accept wake-on-LAN packets, and have the S3 suspend power state enabled so that they may be remotely put to sleep and waken up. The capacity of the links is 10 Mbps. Each user is represented by a source/destination pair generating continuous Poisson UDP traffic of 50 Kbps, to avoid congestion situations. The sources and destinations are always kept ON, while the intermediate nodes are put to sleep intermittently. The delay and packet loss of the traffic were measured as end-to-end values and the power consumption of the intermediate nodes was measured by using the _Watts up?.Net_ power meter [30]. Each experiment lasted 200 seconds and was conducted 5 times. The results presented here are the average values of those runs. Figure 1: Description of the queuing mechanism. The network uses shortest path routing implemented as a Linux kernel module, and the queuing mechanism we have described is incorporated in the same module. The architecture used is not one of an interactive network with adaptive routing, but more of a store and forward network. We have chosen to use a predefined shortest-path routing and to store the packets until a path is re-established and not attempt to change paths when a node is OFF. This is because our aim is to study only the effect that turning nodes OFF has on the power consumption of a network, regardless of the routing protocol used. This also avoids cases such as the overloading of alternative paths and additional packet losses. Likewise, we have chosen to send UDP instead of TCP traffic so that the results will not be affected by the retransmission of packets. Of course mechanisms similar to AODV [33] could be proved useful for future research. Such an on-demand approach for finding paths could be appropriate in cases where some of the nodes on the shortest path are asleep and alternative paths exist. Flood packets could be used to explore possible routes so that the most up-to-date information regarding the state of the nodes would be used. Experiments are run with the auto-hibernation mechanism following a random pattern of turning OFF and ON the nodes. The duration that any node stays ON \(T_{on}\) and the duration that it stays in sleep mode \(T_{off}\) are selected randomly between two values \(T_{on}^{min}\),\(T_{on}^{max}\) and \(T_{off}^{min}\),\(T_{off}^{max}\) respectively. The average cycle time \(T_{on}^{avg}+T_{off}^{avg}=T_{cycle}\) is kept constant for comparison. We report the results of seven experiments with the parameters shown in Table 1. During each experiment we measured the power consumption of the intermediate nodes, the packet loss and the average end-to-end delay experienced by the packets. Figure 2: Experimental topology. Grey nodes are sources/destinations and are always ON. Intermediate nodes can be put to sleep and their power consumptions are measured. In the first experiment the nodes are kept always ON, while in the last experiment the nodes are always OFF and are turned ON at the \(200\)-th second. The minimum time that a node remains OFF (\(T_{off}^{min}\)) was chosen to be \(5\) seconds because this was the time it takes for a node to switch from normal operation into the suspended state. In Figure 3 we examine further the sixth experiment and plot the measured power consumption over time.