ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (7): 1347-1368.doi: 10.7544/issn1000-1239.2020.20190776

    Next Articles

A Component-Level Dynamic Power-Aware Energy-Saving Mechanism for Backbone Networks

Zhang Jinhong1, Wang Xingwei1, Yi Bo1, Huang Min2   

  1. 1(School of Computer Science and Engineering, Northeastern University, Shenyang 110169);2(School of Information Science and Engineering, Northeastern University, Shenyang 110819)
  • Online:2020-07-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2017YFB0801701) and the National Natural Science Foundation of China (61872073).

Abstract: With a progressive increase of Internet traffic year by year, power consumption in the Internet is rising at an alarming rate, and the consequent environmental problems, e.g. the greenhouse effect caused by the surging carbon footprint and so on, have also aroused continuous concerns on a global scale, which are more serious especially in the backbone network where the aggregated traffic is transmitted. The oversupply principle for traditional Internet resources further aggravates these severe situations. With regard to this situation, a component-level dynamic power-aware energy-saving mechanism is devised over the backbone network in this paper. In the proposed mechanism, firstly, the incoming traffic size of nodes is dynamically predicted for a short term; then the fine-grained port number conversion algorithm is adopted to determine the number of ports to be regulated; then the corresponding ports convert their power states according to the sleeping and awakening rules; finally a novel hierarchical scheduling algorithm is devised to schedule the packets. In the simulation, based on the real traffic distribution traces over three typical backbone networks, we determine prediction parameters, test the proportionality of tracing load by power efficiency, explore the impacts of adopting different prediction time slot series and the different number of traffic load counters on the accuracy of load prediction, analyze the impacts of overestimation error and underestimation error of traffic load prediction that might appear on power consumption and discuss the tradeoff between power efficiency and actual performance in different application scenarios. Results demonstrate that the component-level power control mechanism proposed in the paper can control the power consumption of each network component dynamically and proportionally with a fine granularity and has a significantly energy-saving benefit.

Key words: component-level energy saving, dynamic power awareness, fine-grained control, load prediction, hierarchical scheduling

CLC Number: