With the proliferation of data movement across the Internet, global data traffic per year has already exceeded the Zettabyte scale. The network infrastructure and end-systems facilitating the vast data movement consume an extensive amount of electricity, measured in terawatt-hours per year. This massive energy footprint costs the world economy billions of dollars partially due to energy consumed at the network end-systems. Although extensive research has been done on managing power consumption within the core networking infrastructure, there is little research on reducing the power consumption at the end-systems during active data transfers. This paper presents a novel cross-layer optimization framework, called Cross-LayerHLA, to minimize energy consumption at the end-systems by applying machine learning techniques to historical transfer logs and extracting the hidden relationships between different parameters affecting both the performance and resource utilization. It utilizes offline analysis to improve online learning and dynamic tuning of application-level and kernel-level parameters with minimal overhead. This approach minimizes end-system energy consumption and maximizes data transfer throughput. Our experimental results show that Cross-LayerHLA outperforms other state-of-the-art solutions in this area.
翻译:随着数据在互联网上扩散,全球每年的数据流量已经超过了Zettabyte规模,因此超过了Zettabyte规模。网络基础设施和终端系统促进数据大规模流动消耗了大量电力,每年以兆瓦时计。这种巨大的能源足迹使世界经济花费了数十亿美元,部分是由于网络终端系统的能源消耗。虽然在核心网络基础设施内部对管理电力消耗进行了广泛的研究,但在主动数据传输过程中,对减少终端系统电力消耗的研究却很少。本文展示了一个新的跨层优化框架,称为跨LayerHLA,通过应用机器学习技术来历史传输记录和提取影响业绩和资源利用的不同参数之间的隐藏关系,从而最大限度地减少终端系统的能源消耗。它利用离线分析来改进在线学习和动态调整应用水平和内核层面参数,并尽量减少间接费用。这个方法最大限度地减少终端系统的能源消耗和数据传输。我们的实验结果表明,跨层HLA在这一地区超越了其他状态解决方案。