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.
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GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement
The global data movement over Internet has an estimated energy footprint of 100 terawatt hours per year, costing the world economy billions of dollars. The networking infrastructure together with source and destination nodes involved in the data transfer contribute to overall energy consumption. Although considerable amount of research has rendered power management techniques for the networking infrastructure, there has not been much prior work focusing on energy-aware data transfer solutions for minimizing the power consumed at the end-systems. In this paper, we introduce a novel application-layer solution based on historical analysis and real-time tuning called GreenDataFlow, which aims to achieve high data transfer throughput while keeping the energy consumption at the minimal levels. GreenDataFlow supports service level agreements (SLAs) which give the service providers and the consumers the ability to fine tune their goals and priorities in this optimization process. Our experimental results show that GreenDataFlow outperforms the closest competing state-of-the art solution in this area 50% for energy saving and 2.5× for the achieved end-to-end performance.
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- NSF-PAR ID:
- 10113312
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Big Data (Big Data)
- Page Range / eLocation ID:
- 335 to 342
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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Abstract The use of information and communication technology (ICT) has resulted in significant impacts on social welfare, economic growth, transparency, and good governance in developing countries like Nepal. Due to the diverse geographic and economic situations, ICT network and service expansions throughout Nepal have been becoming quite challenging. Private network operators mostly have confined their services to urban areas. Nepal Telecommunications Authority (NTA) collects 2% royalty form Internet Service Providers (ISPs) and Telecom Operators as Rural Telecommunications Disbursement Fund (RTDF) to enhance ICT services to rural Nepal. Broadband expansion projects initiated by utilizing RTDF to expand ICT infrastructure throughout Nepal are expected to have considerable societal and economical transformations in the rural communities of Nepal. This paper not only presents the current ICT deployment scenario of Nepal but also studies design, analysis, and evaluation of green networking that leverages both software defined networking (SDN), and Internet Protocol version 6 (IPv6)—aka Software Defined IPv6 (SoDIP6)—for energy efficient networking, robust services, and sustainable ICT ecosystem for developing nations like Nepal. We evaluate the SoDIP6 network by considering a typical ISP with end access networks and present benefits and recommendations. Experimental results show that the proposed SoDIP6 network help significantly reduce the energy consumption and carbon footprint leading to overall economic benefits to service providers and the society. Furthermore, energy‐saving practices through SoDIP6 networks and some policy directions to the government to focus on green networking considering sensitivity of climate change and global warming impact in the mountainous and developing countries like Nepal are presented.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData.2018.8622570
