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1. Sample Transfer Optimization with Adaptive Deep Neural Network

   https://doi.org/10.1109/INDIS49552.2019.00013  

   Sapkota, Hemanta; Arifuzzaman, Md; Arslan, Engin (November 2019, IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS))

   Application-layer transfer configurations play a crucial role in achieving desirable performance in high-speed networks. However, finding the optimal configuration for a given transfer task is a difficult problem as it depends on various factors including dataset characteristics, network settings, and background traffic. The state-of-the-art transfer tuning solutions rely on real-time sample transfers to evaluate various configurations and estimate the optimal one. However, existing approaches to run sample transfers incur high delay and measurement errors, thus significantly limit the efficiency of the transfer tuning algorithms. In this paper, we introduce adaptive feed forward deep neural network (DNN) to minimize the error rate of sample transfers without increasing their execution time. We ran 115K file transfers in four different high-speed networks and used their logs to train an adaptive DNN that can quickly and accurately predict the throughput of sample transfers by analyzing instantaneous throughput values. The results gathered in various networks with rich set of transfer configurations indicate that the proposed model reduces error rate by up to 50% compared to the state-of-the-art solutions while keeping the execution time low. We also show that one can further reduce delay or error rate by tuning hyperparameters of the model to meet specific needs of user or application. Finally, transfer learning analysis reveals that the model developed in one network would yield accurate results in other networks with similar transfer convergence characteristics, alleviating the needs to run an extensive data collection and model derivation efforts for each network. 

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2. Mining Workflows for Anomalous Data Transfers

   https://doi.org/10.1109/MSR52588.2021.00013  

   Tu, Huy; Papadimitriou, George; Kiran, Mariam; Wang, Cong; Mandal, Anirban; Deelman, Ewa; Menzies, Tim (May 2021, MSR '21)

   Modern scientific workflows are data-driven and are often executed on distributed, heterogeneous, high-performance computing infrastructures. Anomalies and failures in the work- flow execution cause loss of scientific productivity and inefficient use of the infrastructure. Hence, detecting, diagnosing, and mitigating these anomalies are immensely important for reliable and performant scientific workflows. Since these workflows rely heavily on high-performance network transfers that require strict QoS constraints, accurately detecting anomalous network perfor- mance is crucial to ensure reliable and efficient workflow execu- tion. To address this challenge, we have developed X-FLASH, a network anomaly detection tool for faulty TCP workflow transfers. X-FLASH incorporates novel hyperparameter tuning and data mining approaches for improving the performance of the machine learning algorithms to accurately classify the anoma- lous TCP packets. X-FLASH leverages XGBoost as an ensemble model and couples XGBoost with a sequential optimizer, FLASH, borrowed from search-based Software Engineering to learn the optimal model parameters. X-FLASH found configurations that outperformed the existing approach up to 28%, 29%, and 40% relatively for F-measure, G-score, and recall in less than 30 evaluations. From (1) large improvement and (2) simple tuning, we recommend future research to have additional tuning study as a new standard, at least in the area of scientific workflow anomaly detection. 

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3. 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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4. A Comprehensive Tutorial on Science DMZ

   https://doi.org/10.1109/COMST.2018.2876086  

   Crichigno, Jorge; Bou-Harb, Elias; Ghani, Nasir (May 2019, IEEE Communications surveys and tutorials)

   Science and engineering applications are now generating data at an unprecedented rate. From large facilities such as the Large Hadron Collider to portable DNA sequencing devices, these instruments can produce hundreds of terabytes in short periods of time. Researchers and other professionals rely on networks to transfer data between sensing locations, instruments, data storage devices, and computing systems. While general-purpose networks, also referred to as enterprise networks, are capable of transporting basic data, such as e-mails and Web content, they face numerous challenges when transferring terabyte- and petabyte-scale data. At best, transfers of science data on these networks may last days or even weeks. In response to this challenge, the Science Demilitarized Zone (Science DMZ) has been proposed. The Science DMZ is a network or a portion of a network designed to facilitate the transfer of big science data. The main elements of the Science DMZ include: 1) specialized end devices, referred to as data transfer nodes (DTNs), built for sending/receiving data at a high speed over wide area networks; 2) high-throughput, friction-free paths connecting DTNs, instruments, storage devices, and computing systems; 3) performance measurement devices to monitor end-to-end paths over multiple domains; and 4) security policies and enforcement mechanisms tailored for high-performance environments. Despite the increasingly important role of Science DMZs, the literature is still missing a guideline to provide researchers and other professionals with the knowledge to broaden the understanding and development of Science DMZs. This paper addresses this gap by presenting a comprehensive tutorial on Science DMZs. The tutorial reviews fundamental network concepts that have a large impact on Science DMZs, such as router architecture, TCP attributes, and operational security. Then, the tutorial delves into protocols and devices at different layers, from the physical cyberinfrastructure to application-layer tools and security appliances, that must be carefully considered for the optimal operation of Science DMZs. This paper also contrasts Science DMZs with general-purpose networks, and presents empirical results and use cases applicable to current and future Science DMZs. 

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5. Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

   https://doi.org/10.3389/frai.2022.780405  

   Ardalan, Zaniar; Subbian, Vignesh (February 2022, Frontiers in Artificial Intelligence)

   Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling annotated data. It utilizes and transfers knowledge learned from a source domain to target domain even when the dataset is small. There are multiple approaches to transfer learning that result in a range of performance estimates in diagnosis, detection, and classification of clinical problems. Therefore, in this paper, we reviewed transfer learning approaches, their design attributes, and their applications to neuroimaging problems. We reviewed two main literature databases and included the most relevant studies using predefined inclusion criteria. Among 50 reviewed studies, more than half of them are on transfer learning for Alzheimer's disease. Brain mapping and brain tumor detection were second and third most discussed research problems, respectively. The most common source dataset for transfer learning was ImageNet, which is not a neuroimaging dataset. This suggests that the majority of studies preferred pre-trained models instead of training their own model on a neuroimaging dataset. Although, about one third of studies designed their own architecture, most studies used existing Convolutional Neural Network architectures. Magnetic Resonance Imaging was the most common imaging modality. In almost all studies, transfer learning contributed to better performance in diagnosis, classification, segmentation of different neuroimaging diseases and problems, than methods without transfer learning. Among different transfer learning approaches, fine-tuning all convolutional and fully-connected layers approach and freezing convolutional layers and fine-tuning fully-connected layers approach demonstrated superior performance in terms of accuracy. These recent transfer learning approaches not only show great performance but also require less computational resources and time. 

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