Cross-Layer Optimization of Big Data Transfer Throughput and Energy Consumption
Cross-Layer Optimization of Big Data Transfer Throughput and Energy Consumption
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DOI：
10.1109/cloud.2019.00017
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Luigi Di Tacchio;M. S. Q. Z. Nine;T. Kosar;Muhammed Fatih Bulut;Jinho Hwang
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文献类型：
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作者：
Luigi Di Tacchio;M. S. Q. Z. Nine;T. Kosar;Muhammed Fatih Bulut;Jinho Hwang
随着数据洪流的出现，全球数据移动的能源足迹已超过100太瓦时，给世界经济造成了超过200亿美元的损失。在数据活跃传输期间，根据源端和目的端之间的跳数，网络基础设施消耗的能量占总能量的10% - 75%，其余部分由终端系统消耗。尽管在降低网络基础设施的功耗方面已经进行了大量研究，但专注于在终端系统节能的工作仅限于对一些应用层参数的调整。在本文中，我们引入了一种新颖的跨层优化框架，该框架联合考虑应用层和内核层参数，以在不牺牲传输吞吐量的情况下将能耗降至最低。我们提出了三种不同的算法，它们可以动态调整CPU频率级别、活动CPU核心数量、活动传输线程数量、并行TCP流数量以及传输命令流水线级别，以实现不同的用户设定目标。实验结果表明，我们提出的算法优于现有解决方案，在能耗降低48%的同时，吞吐量提高了80%。
With the emergence of data deluge, the energy footprint of global data movement has surpassed 100 terawatt hours, costing more than 20 billion US dollars to the world economy. During an active data transfer, depending on the number of hops between the source and destination, the networking infrastructure consumes between 10% - 75% of the total energy, and the rest is consumed by the end systems. Even though there has been extensive research on reducing the power consumption at the networking infrastructure, the work focusing on saving energy at the end systems has been limited to the tuning of a few application-level parameters. In this paper, we introduce a novel cross-layer optimization framework which jointly considers application-level and kernel-level parameters to minimize the energy consumption without sacrificing from the transfer throughput. We present three different algorithms which can dynamically tune the CPU frequency level, number of active CPU cores, number of active transfer threads, number of parallel TCP streams, and the level of transfer command pipelining to achieve different user-set goals. Experimental results show that our proposed algorithms outperform the state-of-the-art solutions, achieving up to 80% higher throughput while consuming 48% less energy.