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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. 

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2. Energy-Efficient Data Transfer Optimization via Decision-Tree Based Uncertainty Reduction

   https://doi.org/10.1109/ICCCN54977.2022.9868866  

   Jamil, Hasibul; Rodolph, Lavone; Goldverg, Jacob; Kosar, Tevfik (July 2022, 2022 International Conference on Computer Communications and Networks (ICCCN))

   The increase and rapid growth of data produced by scientific instruments, the Internet of Things (IoT), and social media is causing data transfer performance and resource consumption to garner much attention in the research community. The network infrastructure and end systems that enable this extensive data movement use a substantial amount of electricity, measured in terawatt-hours per year. Managing energy consumption within the core networking infrastructure is an active research area, but there is a limited amount of work on reducing power consumption at the end systems during active data transfers. This paper presents a novel two-phase dynamic throughput and energy optimization model that utilizes an offline decision-search-tree based clustering technique to encapsulate and categorize historical data transfer log information and an online search optimization algorithm to find the best application and kernel layer parameter combination to maximize the achieved data transfer throughput while minimizing the energy consumption. Our model also incorporates an ensemble method to reduce aleatoric uncertainty in finding optimal application and kernel layer parameters during the offline analysis phase. The experimental evaluation results show that our decision-tree based model outperforms the state-of-the-art solutions in this area by achieving 117% higher throughput on average and also consuming 19% less energy at the end systems during active data transfers. 

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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. 

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4. GreenNFV: Energy-Efficient Network Function Virtualization with Service Level Agreement Constraints

   https://doi.org/10.1145/3581784.3607090  

   Nine, Md_S_Q Zulkar; Kosar, Tevfik; Bulut, Muhammed Fatih; Hwang, Jinho (November 2023, ACM)

   Network Function Virtualization (NFV) platforms consume significant energy, introducing high operational costs in edge and data centers. This paper presents a novel framework called GreenNFV that optimizes resource usage for network function chains using deep reinforcement learning. GreenNFV optimizes resource parameters such as CPU sharing ratio, CPU frequency scaling, last-level cache (LLC) allocation, DMA buffer size, and packet batch size. GreenNFV learns the resource scheduling model from the benchmark experiments and takes Service Level Agreements (SLAs) into account to optimize resource usage models based on the different throughput and energy consumption requirements. Our evaluation shows that GreenNFV models achieve high transfer throughput and low energy consumption while satisfying various SLA constraints. Specifically, GreenNFV with Throughput SLA can achieve 4.4× higher throughput and 1.5× better energy efficiency over the baseline settings, whereas GreenNFV with Energy SLA can achieve 3× higher throughput while reducing energy consumption by 50%. 

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5. Accurately Simulating Energy Consumption of I/O-intensive Scientific Workflows

   https://doi.org/10.1007/978-3-030-22734-0_11  

   Ferreira da Silva, Rafael; Orgerie, Anne-Cecile; Casanova, Henri; Tanaka, Ryan; Deelman, Ewa; Suter, Frederic (July 2019, International Conference on Computational Science (ICCS))

   While distributed computing infrastructures can provide infrastructure-level techniques for managing energy consumption, application-level energy consumption models have also been developed to support energy-efficient scheduling and resource provisioning algorithms. In this work, we analyze the accuracy of a widely-used application-level model that have been developed and used in the context of scientific workflow executions. To this end, we profile two production scientific workflows on a distributed platform instrumented with power meters. We then conduct an analysis of power and energy consumption measurements. This analysis shows that power consumption is not linearly related to CPU utilization and that I/O operations significantly impact power, and thus energy, consumption. We then propose a power consumption model that accounts for I/O operations, including the impact of waiting for these operations to complete, and for concurrent task executions on multi-socket, multi-core compute nodes. We implement our proposed model as part of a simulator that allows us to draw direct comparisons between real-world and modeled power and energy consumption. We find that our model has high accuracy when compared to real-world executions. Furthermore, our model improves accuracy by about two orders of magnitude when compared to the traditional models used in the energy-efficient workflow scheduling literature. 

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