N.B: I am posting by revising something I wrote for academic purposes. So it doesn't look like a blog article.
Description of IEEE standard
EEE concept is described in figure (1) as seen in the Reviriego et al. 2009. The link is made active when a packet arrives and when no packets are left link enters into a low power mode. Periodically link is made active for a short time to make sure that it is working. This is denoted as a refresh cycle in the figure. Table (1) shows the proposed mean sleep time, wake-up time from Reviriego et al 2009.
Simulation experiments
Simulation of EEE was done using the CSIM. CSIM is a simulation library from Mesquisite software. It provides the facility for simulating queues. It is believed that the arrival of packets in Ethernet networks follows an exponential distribution and therefore EEE can be simulated as a Poisson process. Coincidentally packet arrivals and leaving on Ethernet links can be modeled as an M/M/1 queue. The service center would be link and queue will be packets arrived on the link. The simulation was done with 5000 packets each for 100Mbps, 1Gbps, and 10 Gbps. Energy consumption for the link was calculated using the following known formula.
P = Plow power · (1 − load) + Pactive · load
Plow power is the power consumed in low power mode and Pactive is the power consumed when the link is active. The load was increased from 0 to 100 at intervals of 5. It is assumed that both forward and backward packet transfer between the links are independent of each other. I also neglected the refresh cycle since it has little effect on performance. Further bulk arrivals were simulated using a geometric distribution with their means equivalent to the desired burst length. In this experiment, I used mean burst lengths of 2, 5, and 10 to find its effect on power consumption. Finally, I did experiments on real-world packet traces collected from a personal computer. Trace files used in this experiment were collected from a 100 Mbps Ethernet link. Trace files were collected when the user did typical browsing, downloading, and surfing on the internet.
Simulation Results
Results of the simulation on the regular EEE model are plotted in the graphs shown in figures (2), (3), and (5).
Results for the simulation on regular EEE were as expected. For burst arrivals, we notice that the performance is improved when the burst length is increased. Table II shows results for real-world packet trace analysis.
Results Analysis
Graphs for regular EEE at various loads show that performance is as expected. Energy consumption is proportional to the traffic of the packets as inferred from results. In 100Mbps, results are close to the proportional (ideal) case. However, in 1Gbps and 10 Gbps links, results are not good especially when traffic is high. It appears that the link is spending more time sleeping and waking up at higher traffic.
When packets appear as burst, performance is improved as expected. This is because the link is made to wake up and sleep only for the whole burst packet length. More the burst arrivals, more the performance getting close to the ideal case.
For real-world data, power consumption in EEE looks very low when compared to that of normal Ethernet. This might be because the packet arrival rate in the real world is higher than that of simulation models and hence link is in sleep most of the time.
Enhancing EEE
Improving EEE using predictive packet arrival
From our results, we see that the performance is affected in high-speed links when the packets arrive at high speed because the link would be spending more time sleeping and waking up. To overcome this issue I propose to make use of a predictive sleep method in which we sleep only if the queue is empty and mean arrival delay till the last packet is greater than sleep time. By doing so we could overcome the overhead issue due to continuous sleep and wake up in high-speed networks; however, we would be incurring a penalty if the prediction was wrong. If a packet did not arrive within the mean delay time we would need to sleep. Therefore penalty for a failed prediction, in that case, would be
Penalty = Mean packet arrival delay + TS
However, before making any judgments it is better to analyze the proposed model using simulation.
Simulation experiments
Experiments were conducted for the proposed enhancement method using CSIM Software. When each packet is leaving the system we check if the queue is empty. If the queue is empty we next verify that the mean delay for past packet arrivals is less than sleep time. If that is the case then we continue without sleeping, otherwise, we would sleep. If a packet arrives before the predicted mean delay we gain a power loss for the time equivalent to the difference between sleep time and mean packet delay time. If a packet did not arrive before the predicted mean delay time we incur the penalty described above.
Simulation Results
The result of the proposed simulation model is shown in Figures (8), (9) & (10).
Result Analysis
The simulation results of the proposed model show that it could improve the performance without any packet loss. It also shows that the proposed method would work better for 100Mbps. The graph gets skewed toward the ideal case. Notice that a slight variation could produce increased cost benefits, and therefore this method looks better than the original proposal theoretically. There is no packet loss in this suggested improvement.
Summary
In this post, we reproduced the results of the IEEE simulation. We found that when burst arrivals are high performance in EEE is enhanced. It is also noteworthy that given the round trip time on typical internet servers is in milliseconds, the small delay in terms of microseconds in the Ethernet links is negligible when compared with the significant economic benefit. We also proposed a method to improve the performance of EEE without packet loss. However, we need more testing of the proposed system using real data before assuring its enhanced performance.
