ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (7): 1347-1368.doi: 10.7544/issn1000-1239.2020.20190776

• 网络技术 •    下一篇

一种面向主干网的器件级动态功率感知节能机制

张金宏1,王兴伟1,易波1,黄敏2   

  1. 1(东北大学计算机科学与工程学院 沈阳 110169);2(东北大学信息科学与工程学院 沈阳 110819) (neuzjh@aliyun.com)
  • 出版日期: 2020-07-01
  • 基金资助: 
    国家重点研发计划项目(2017YFB0801701);国家自然科学基金项目(61872073)

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).

摘要: 随着互联网流量逐年递增,网络功耗正以惊人的速度攀升,由此激增的碳足迹导致的温室效应等环境问题也引起了全球范围内的持续关注,尤其是在流量汇聚之后的主干网,这些问题更为突出.传统互联网资源的过供给原则进一步加剧了这种严峻的状况.鉴于此,面向主干网提出了一种器件级动态功率感知节能机制.该机制首先对节点入流量大小进行动态短期预测,进而采用细粒度的端口数转换算法确定需要调整的端口数目,之后依据休眠唤醒规则和速率调节规则控制相应的端口进行功率状态的转换,最后采用层次调度算法进行分组的调度.在实现方面,基于3个典型主干网中的真实流量分布轨迹,确定了预测参数,测试了功效随负载变化的比例性,探索了采用不同的预测时隙序列以及不同的流量负载计数器数目对负载预测准确度的影响,分析了可能出现的流量负载预测过估计误差和低估计误差对功耗和性能产生的影响,讨论了在不同应用场景下功效与实际性能之间的权衡.结果表明:提出的器件级功控机制能够动态、细粒度和比例性地控制各网元功耗,具有显著的节能收益.

关键词: 器件级节能, 动态功率感知, 细粒度控制, 负载预测, 层次调度

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

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