More Like this

1. Energy-saving Cross-layer Optimization of Big Data Transfer Based on Historical Log Analysis

   https://doi.org/10.1109/ICC42927.2021.9500693  

   Rodolph, Lavone; Zulkar Nine, MD S; Di Tacchio, Luigi; Kosar, Tevfik (June 2021, ICC 2021 - IEEE International Conference on Communications)

   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
2. Cross-Layer Optimization of Big Data Transfer Throughput and Energy Consumption

   https://doi.org/10.1109/CLOUD.2019.00017  

   Di Tacchio, Luigi; Nine, MD S; Kosar, Tevfik; Bulut, Muhammed Fatih; Hwang, Jinho (July 2019, 2019 IEEE 12th International Conference on Cloud Computing (CLOUD))

   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. 

   more » « less
3. GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement

   https://doi.org/10.1109/BigData.2018.8622570  

   Zulkar Nine, MD S; Di Tacchio, Luigi; Imran, Asif; Kosar, Tevfik; Bulut, M. Fatih; Hwang, Jinho (January 2018, 2018 IEEE International Conference on Big Data (Big Data))

   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. 

   more » « less
4. FastHLA: Energy-Efficient Mobile Data Transfer Optimization Based on Historical Log Analysis

   https://doi.org/10.1145/3265863.3265871  

   Guner, Kemal; Nine, MD S; Bulut, M. Fatih; Kosar, Tevfik (October 2018, MobiWac'18 Proceedings of the 16th ACM International Symposium on Mobility Management and Wireless Access)

   Mobile data traffic will exceed PC Internet traffic by 2020. As the number of smartphone users and the amount of data transferred per smartphone grow exponentially, limited battery power is becoming an increasingly critical problem for mobile devices which depend on the network I/O. Despite the growing body of research in power management techniques for the mobile devices at the hardware layer as well as the lower layers of the networking stack, there has been little work focusing on saving energy at the application layer for the mobile systems during network I/O. In this paper, we propose a novel technique, called FastHLA, that can achieve significant energy savings at the application layer during mobile network I/O without sacrificing the performance. FastHLA is based on historical log analysis and real-time dynamic tuning of mobile data transfers to achieve the optimization goal. FastHLA can increase the data transfer throughout by up to 10X and decrease the energy consumption by up to 5X compared to state-of-the-art HTTP/2.0 transfers. 

   more » « less
5. Big data transfer optimization based on offline knowledge discovery and adaptive sampling

   https://doi.org/10.1109/BigData.2017.8257959  

   Nine, Md S.; Guner, Kemal; Huang, Ziyun; Wang, Xiangyu; Xu, Jinhui; Kosar, Tevfik (December 2017, 2017 IEEE International Conference on Big Data)

   The amount of data moved over dedicated and non-dedicated network links increases much faster than the increase in the network capacity, but the current solutions fail to guarantee even the promised achievable transfer throughputs. In this paper, we propose a novel dynamic throughput optimization model based on mathematical modeling with offline knowledge discovery/analysis and adaptive online decision making. In offline analysis, we mine historical transfer logs to perform knowledge discovery about the transfer characteristics. Online phase uses the discovered knowledge from the offline analysis along with real-time investigation of the network condition to optimize the protocol parameters. As real-time investigation is expensive and provides partial knowledge about the current network status, our model uses historical knowledge about the network and data to reduce the real-time investigation overhead while ensuring near optimal throughput for each transfer. Our novel approach is tested over different networks with different datasets and outperformed its closest competitor by 1.7× and the default case by 5×. It also achieved up to 93% accuracy compared with the optimal achievable throughput possible on those networks. 

   more » « less