دانلود رایگان مقاله یک روش HMM فضایی برای شبکه های سبز

abstract

Green networking becomes more and more important, especially with the rapid development of data centers in recent years. In this paper, in order to minimize the energy consumption of a network, we present a novel energy saving approach, called Predictive Green Networking Approach (PGNA), based on a spatial Hidden Markov Model (sHMM). The sHMM is proposed to describe both the topology and the traffic distribution of the network. Loads of links in the network can be predicted based on the sHMM, and the links that most likely become near idle can be put into sleep mode to save energy. A deepsleep method is proposed to maximize the energy saving while the network satisfies the connectivity and the maximum utilization constraints. We test the performance of PGNA with two real ISP backbone topologies and real traffic demands. The results show that our approach is effective and works better than related approaches.

8. Conclusion

In this paper, we solve the energy-consumption problem by proposing PGNA to predict the network traffic and then try to shut off the links as long as the network constraints are satisfied. In PGNA, a sHMM is used to model the network, and a deep-sleep method is proposed to shut off the near idle links as many as possible. In order to evaluate the performance of our solution, we used two real ISP topology from the SNDlib and the Rocketfuel, respec- tively. Two typical sets of traffic matrices were generated and real traffic demands from SNDlib were used to test the PGNA. The re- sults show that our solution are effective for energy saving, es- pecially when the network is during off-peak time. Comparing to anther energy-saving approach, namely HESA, PGNA works much better in both topology. For instance, when the network is in idle time, the energy PGNA saves is about twice as much as HESA in GERMANY50.