Data Transfer Tools Research Articles

Design a Simple Portable Electrocardiograph Based on the IOT Concept

Abstract There are many cases of heart disease with death associated. The symptoms are not always clearly visible and require examination using an electrocardiogram (ECG) device to be detected. Recording human electrical activity can be done in various organs of the human body, one of which is in the heart. An ECG is a medical test to detect electrical activity signals produced by the heart by exchanging the signals on a monitor or graph on paper. This study aimed to design, build, test, and analyze a portable ECG device that is compact and easy to use. This portable electrocardiogram helps monitor human heart rate to check heart rate periodically. Tool design is done by making tools equipped with a monitoring system. The proposed system uses the AD8232 sensor to detect a person’s cardiac activity. Arduino Nano is a microcontroller connected to ESP32 with a WiFi feature. Heart activity monitors can be done anywhere by applying the Internet of Things concept, namely with the Ubidots application. A portable tool using Li-Ion batteries as a tool voltage source and BMS 3S as a charger, this tool is also equipped with a 3.2-inch TFT screen as a graph viewer of the results of monitoring heart activity. The results showed that the system could monitor the user’s heart for 5 minutes when sitting, walking, and running. The system created has been able to send heart signal data to be processed by Arduino Nano and ESP32 as a data transfer tool so that the data generated in the form of electrocardiogram graphs can be displayed on the Ubidots application. TFT LCDs can display electrocardiogram graphs generated by the electrical activity of the human heart and beats per minute values. The AD8232 sensor can measure beat per minute (bpm) values with an average increase in bpm values for walking conditions of 17% and running conditions of 19.17%. The AD8232 Sensor Performance can detect the user’s heart rate with a percentage of data difference between walking and running conditions of 2.88% with an accuracy rate of 97.12%.

A Comparative Analysis and Mathematical Modelling of Data Migration Tools to Evaluate Performance Parameters using Machine Learning Methods

Abstract: Transferring data between storage systems, platforms, or places is a common reason why data movement is an important part of modern computing. The effectiveness and dependability of these tasks can be greatly affected by how well data transfer tools work. Using machine learning techniques, we compare and contrast different data transfer tools in this work. First, we will talk about some important performance metrics that are needed to properly evaluate data transfer tools. Such elements include the type of data, its size, the network's speed, and other important factors like delay and flow. Next, we get data from different data transfer tools and prepare it before reviewing their performance in a range of situations and setups. The data is analyzed using machine learning techniques like classification, regression, and grouping. Finding groups of tools that work similarly is easier with clustering. This gives you information about which tools are best for different data transfer jobs. We can describe the link between performance factors and tool performance under different situations using regression analysis. The performance traits of tools help with classification, which makes it easier to choose the right tool for a transfer job. Our findings demonstrate that machine learning techniques can correctly evaluate and contrast data transfer tools by looking at how well they do certain tasks. The paper study shows how these approaches can help companies pick the best tool for their data transfer needs by giving them useful information about tool choices. Furthermore, our research shows how important it is to use machine learning when assessing data transfer tools and sets the stage for more research to be done in this area in the future.

Effective Strategies for Managing Multi-Cloud Salesforce Solutions

Businesses are using multi-cloud solutions to improve flexibility, scalability, and efficiency in today's dynamic environment. Salesforce, a prominent CRM platform, is crucial to many organizations' multi-cloud ecosystems. To achieve seamless integration, maximum performance, and safe operations, administering Salesforce products across numerous cloud environments requires distinct methodologies. This paper discusses best practices, essential concerns, and practical solutions for managing multi-cloud Salesforce applications. The research starts with multi-cloud advantages and difficulties. By using various cloud providers, companies may prevent vendor lock-in, improve resilience, and customize solutions. These benefits complicate data management, integration, and security. The article begins with multi-cloud Salesforce integration techniques. It emphasizes the need for a smooth integration architecture between Salesforce and other cloud services. API-based integrations, middleware platforms, and cloud-based integration services are tested for real-time data synchronization and latency. The study also examines data integration technologies and their influence on data consistency and accuracy across numerous cloud environments. The second segment handles multi-cloud Salesforce data management issues. It addresses data governance, quality control, and storage optimization. Strong data management practices that meet industry and regulatory standards are prioritized. The study examines data transfer, backup, and recovery tools and methods that enable effective data management across cloud platforms.

Cross-border data sharing through the lens of research ethics committee members in sub-Saharan Africa.

Several factors thwart successful data sharing-ambiguous or fragmented regulatory landscapes, conflicting institutional/researcher interests and varying levels of data science-related expertise are among these. Traditional ethics oversight mechanisms and practices may not be well placed to guarantee adequate research oversight given the unique challenges presented by digital technologies and artificial intelligence (AI). Data-intensive research has raised new, contextual ethics and legal challenges that are particularly relevant in an African research setting. Yet, no empirical research has been conducted to explore these challenges. We explored REC members' views and experiences on data sharing by conducting 20 semi-structured interviews online between June 2022 and February 2023. Using purposive sampling and snowballing, we recruited representatives across sub-Saharan Africa (SSA). We transcribed verbatim and thematically analysed the data with Atlas.ti V22. Three dominant themes were identified: (i) experiences in reviewing data sharing protocols, (ii) perceptions of data transfer tools and (iii) ethical, legal and social challenges of data sharing. Several sub-themes emerged as: (i.a) frequency of and approaches used in reviewing data sharing protocols, (i.b) practical/technical challenges, (i.c) training, (ii.a) ideal structure of data transfer tools, (ii.b) key elements of data transfer tools, (ii.c) implementation level, (ii.d) key stakeholders in developing and reviewing a data transfer agreement (DTA), (iii.a) confidentiality and anonymity, (iii.b) consent, (iii.c) regulatory frameworks, and (iii.d) stigmatisation and discrimination. Our results indicated variability in REC members' perceptions, suboptimal awareness of the existence of data protection laws and a unanimously expressed need for REC member training. To promote efficient data sharing within and across SSA, guidelines that incorporate ethical, legal and social elements need to be developed in consultation with relevant stakeholders and field experts, along with the training accreditation of REC members in the review of data-intensive protocols.

Fastensor: Optimise the Tensor I/O Path from SSD to GPU for Deep Learning Training

In recent years, benefiting from the increase in model size and complexity, deep learning has achieved tremendous success in computer vision (CV) and (NLP). Training deep learning models using accelerators such as GPUs often requires much iterative data to be transferred from NVMe SSD to GPU memory. Much recent work has focused on data transfer during the pre-processing phase and has introduced techniques such as multiprocessing and GPU Direct Storage (GDS) to accelerate it. However, tensor data during training (such as Checkpoints, logs, and intermediate feature maps), which is also time-consuming, is often transferred using traditional serial, long-I/O-path transfer methods. In this article, based on GDS technology, we built Fastensor, an efficient tool for tensor data transfer between the NVMe SSDs and GPUs. To achieve higher tensor data I/O throughput, we optimized the traditional data I/O process. We also proposed a data and runtime context-aware tensor I/O algorithm. Fastensor can select the most suitable data transfer tool for the current tensor from a candidate set of tools during model training. The optimal tool is derived from a dictionary generated by our adaptive exploration algorithm in the first few training iterations. We used Fastensor’s unified interface to test the read/write bandwidth and energy consumption of different transfer tools for different sizes of tensor blocks. We found that the execution efficiency of different tensor transfer tools is related to both the tensor block size and the runtime context. We then deployed Fastensor in the widely applicable Pytorch deep learning framework. We showed that Fastensor could perform superior in typical scenarios of model parameter saving and intermediate feature map transfer with the same hardware configuration. Fastensor achieves a 5.37x read performance improvement compared to torch.save () when used for model parameter saving. When used for intermediate feature map transfer, Fastensor can increase the supported training batch size by 20x, while the total read and write speed is increased by 2.96x compared to the torch I/O API.

3DLightBeam+. Design, simulation, and testing of carbon-efficient reinforced 3D concrete printed beams

The paper presents a workflow for the design, material testing and numerical simulation of shape-optimised reinforced 3D Concrete Printed (3DCP) beams tested structurally in three-point bending. Building upon previous research on structurally optimised toolpath planning for 3DCP, the presented work suggests a method for the computational design optimisation process based on material efficiency principles and the parallel development of a lossless data transfer tool from the 3D modelling environment to a Finite Element Analysis (FEA) workspace taking into account the layered process characteristic of 3DCP and the consequent weak interactions generated between adjacent printed concrete filaments. The proposed FEA model was calibrated through cored cylindrical specimens and then applied to the design, fabrication and experimental testing of a proof-of-concept shape- and infill-optimised 3-metre simply supported beam. The comparative analysis of simulation and structural testing proves the reliability of the numerical model, which correctly estimated peak load and damage patterns for 3DCP. The tests highlight how, with a shear failure mode, the dominant cracks start from the support region and reach the area of load application. The presented work advances the research in 3DCP by developing 3DLightBeam+, a carbon-efficient reinforced concrete beam design with a 200% higher flexural strength-to-weight ratio than a full comparable beam. The beam is conceived through a seamless workflow for its FE analysis and structural verification that accurately predicts structural capacity and behaviour without destructive testing methods.

CRBF: Cross-Referencing Bloom-Filter-Based Data Integrity Verification Framework for Object-Based Big Data Transfer Systems

Various components are involved in the end-to-end path of data transfer. Protecting data integrity from failures in these intermediate components is a key feature of big data transfer tools. Although most of these components provide some degree of data integrity, they are either too expensive or inefficient in recovering corrupted data. This problem highlights the need for application-level end-to-end integrity verification during data transfer. However, the computational, memory, and storage overhead of big data transfer tools can be a significant bottleneck for ensuring data integrity due to the large size of the data. This paper proposes a novel framework for data integrity verification in big data transfer systems using a cross-referencing Bloom filter. This framework has three advantages over state-of-the-art data integrity techniques: lower computation and memory overhead and zero false-positive errors for a limited number of elements. This study evaluates the computation, memory, recovery time, and false-positive overhead for the proposed framework and compares them with state-of-the-art solutions. The evaluation results indicate that the proposed framework is efficient in detecting and recovering from integrity errors while eliminating false positives in the Bloom filter data structure. In addition, we observe negligible computation, memory, and recovery overheads for all workloads.

Rancang Bangun Alat Transfer Data Teks Nirkabel Bawah Air Berbasis Mikrokontroler

Communication is a very important factor to avoid misunderstanding, with communication it will be easy to understand what is meant between the object of introduction and the recipient of information. However, communication is still an obstacle for those who carry out underwater activities such as diving. Many ordinary divers do not understand the diver's signal code which will be a problem because of the difficulty in communicating if something happens that requires them to carry out information sharing activities underwater. Innovation is needed to be implemented, namely research that is able to facilitate divers to communicate underwater the same as using diver sign language. The purpose of this research is to design a wireless data transfer tool that can be used underwater by utilizing the Arduino microcontroller control board as a data processing component supported by an infrared sensor as a transmitting medium which aims to capture infrared light waves as a wireless communication medium. The marker components such as LEDs and LCDs indicate and display information from the processing results when data is successfully received and sent. The power distribution uses low voltage currents to carry out the performance of wireless data transfers underwater with the results of safe test criteria while the device is working in water.

BIM-integrated LCA to automate embodied carbon assessment of prefabricated buildings

Building information modeling (BIM) has been leveraged to reduce manual efforts in the life cycle assessment (LCA) of buildings. However, data interoperability between BIM and LCA tools remains a significant challenge because of disparate data structures, leading to a deficiency in automatic data mapping methods. Few studies have adopted professional BIM and LCA tools to evaluate the embodied carbon of prefabricated buildings at multiple spatial levels. Thus, this paper developed a BIM-integrated LCA solution to automate the embodied carbon assessment of prefabricated buildings based on a five-level framework: material, component, assembly, flat, and building. The solution framework involved three modules: (1) BIM data preparation, (2) data extraction and integration, and (3) embodied carbon assessment. An Industry Foundation Classes (IFC)-enabled data transfer tool was developed to adapt BIM data to the LCA data structure automatically. A real-life prefabricated building in Hong Kong was used to validate the feasibility of the developed solution by comparing it with the traditional LCA method. Embodied carbon was reported in five levels. The cradle-to-end-of-construction embodied carbon of the case building was calculated as 561 kg CO2/m2, showing an acceptable 1% discrepancy with traditional LCA results owing to inevitable quantity differences between design and construction. The results also indicate a dramatical time reduction in the LCA modeling process from 729 min to 62 min, achieving a 91.5% efficiency improvement. It can thus practically increase practitioners’ willingness to conduct LCA at an early design stage with quick feedback. The developed solution also extends the knowledge on automatic BIM-integrated LCA methodologically by addressing data interoperability using the IFC standard. Further, it provides systematic insights into the embodied carbon emissions of prefabricated buildings based on the five-level theoretical framework, guiding low-carbon designs for prefabricated buildings.

RapidXfer - data transfer framework for square kilometre array

Data Management of Astronomy Data is often a laborious task and it is even more challenging for the extraordinary amounts of data expected from the world’s largest radio telescope, Square Kilometre Array. There are overt issues in transferring the voluminous SKA data and the traditional data transfer methods are fragile especially for the data transfer between two continents. To address this, a new data transfer framework RapidXfer is proposed and the data transfer is achieved using two steps: international and local transfers. The efficiency of different end-to-end data transfer tools used in RapidXfer is evaluated on different dataset sizes. Further, a comparative study of two IRIS grid data transfer methods is made to understand each methods’ advantages and disadvantages. This study can be used as a reference for the development of future SKA’s data transfer operations.

Transmedia project: strategy of communicative interactive (based on the material from the «RBC Pro» website)

The article is devoted to the analysis of the transmedia project of the «RBC Pro» website as an interactive platform on which a media product is formed that has a certain value for the target audience. Traditional methods of obtaining information are not attractive to audiences today. New consumer demands have influenced the emergence of the transmedia format. A new cultural practice of perceiving media content was to emerge. Modern communication technologies give journalism the opportunity to present information through not only traditional means, but also using other digital data transfer tools. The use of transmedia tools was the answer of journalism to the demands of the audience. This study identifies the types of communication strategies used to create the «RBC Pro» transmedia project, systematizes the transmedia principles and technologies for promoting a media product that is attractive for interactive interaction with the audience. The novelty of the research is the disclosure of the multiplication effect of the organizing function of the media, which creatively implements modern technologies for creating and promoting media content.

Retracted: Synchrophasor data transfer tool IEEE C37.118.2—Implementation and study of measurement standard for a futuristic substation automation

Sasikala R, Meenakumari R. Synchrophasor data transfer tool IEEE C37.118.2–Implementation and study of measurement standard for a futuristic substation automation, International Journal of Communication Systems 2021, (https://doi.org/10.1002/dac.4759). The above article published online on 05 May 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor and John Wiley and Sons Ltd. Following publication, it has come to our attention that the people named as the Guest Editors of the Special Issue were being impersonated by a fraudulent entity and the articles were not reviewed in line with the journal's peer review standards. The Editor has determined that the articles do not meet the required scholarly standards of the journal, and therefore has taken the decision to retract the article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.

NBFTP: a dedicated data transfer system for remote astronomical observation at Dome A* *The source code has been uploaded on gitee ( https://gitee.com/AstroTJU/NBFTP ) and china-vo ( https://code.china-vo.org/ast3/nbftp ).

Dome A, Antarctica, has been thought to be one of the best astronomical sites on the Earth for decades. Since it was first visited by astronomers in 2008, dozens of facilities for astronomical observation and site testing were deployed. Due to its special geographical location, the data and message exchange between Dome A and the domestic control center could only depend on Iridium. Because the link bandwidth of Iridium is extremely limited, the network traffic cost is quite expensive and the network is rather unstable, the commonly used data transfer tools, such as rsync and scp, are not suitable in this case. In this paper, we design and implement a data transfer tool called NBFTP (narrow bandwidth file transfer protocol) for the astronomical observation of Dome A. NBFTP uses a uniform interface to arrange all types of data and matches specific transmission schemes for different data types according to rules. Break-point resuming and extensibility functions are also implemented. Our experimental results show that NBFTP consumes 60% less network traffic than rsync when detecting the data pending to be transferred. When transferring small files of 1KB, the network traffic consumption of NBFTP is 40% less than rsync. However, as the file size increases, the network traffic consumption of NBFTP tends to approach rsync, but it is still smaller than rsync.

Method of developing machine learning architecture for IoT­devices based on serverless architecture

With the advent of IoT and microservice architectures, a multitude of intelligent distributed applications have emerged in which IoT devices collect, transform, and analyze data in large volumes and at high speed. A large number of these programs require robust, predictive analytics in real time, which require threading workflows closer to the data source, as well as dynamic resource management decisions. Moreover, predictive analytics requires the developer to create robust deep learning models, which, in turn, requires them to develop functions, find and configure parameters, and select machine learning models, which takes not only time, but also requires high experience level. The proliferation of machine learning libraries and frameworks, data ingestion tools, streaming and batch processing engines, rendering techniques, and the myriad of available hardware platforms further exacerbate these challenges. To overcome these complex challenges faced by developers of intelligent IoT applications, this article proposes a method for deploying machine learning architecture for IoT devices based on a serverless architecture called MLAbosa. The MLAbosa method can deploy, plan and dynamically manage data transfer tools, streaming software, batch analytics tools, and visualization tools across the cloud spectrum. This article describes the architecture of the MLAbosa method, highlighting the problems it solves.

An open source data transfer tool kit for research data

We present the prototype of an open source data transfer tool kit. It provides an easy to use ‘drag-and-drop’ web interface for users to transfer files between institutions that do not have a grid infrastructure in place. The underlying technology leverages standard grid technologies. e.g. automatic generation of X.509 certificates, but remains completely hidden from the user.

FTLADS: Object-Logging Based Fault-Tolerant Big Data Transfer System Using Layout Aware Data Scheduling

Layout-Aware Data Scheduler (LADS) data transfer tool, identifies and addresses the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environments. It exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS can avoid congested storage elements within the shared storage resource, improving input/output bandwidth, and hence the data transfer rates across the high speed networks. However, absence of FT (fault tolerance) support in LADS results in data re-transmission overhead along with the possible integrity issues upon errors. In this paper, we propose object based logging methods to avoid transmitting the objects which are successfully written to Parallel File System (PFS) at the sink end. Depending on the number of logger files created, for the whole dataset, we classified our fault tolerance mechanisms into three different categories: File logger, Transaction logger and Universal logger. Also, to address space overhead of these object based logging mechanisms, we have proposed different methods of populating logger files with the information of the completed objects. We have evaluated the data transfer performance and recovery time overhead of the proposed object based logging fault tolerant mechanisms on LADS data transfer tool. Our experimental results show that, LADS in conjunction with proposed object based fault tolerance mechanisms exhibit an overhead of less than 1% with respect to data transfer time and total recovery time overhead is around 10% of total data transfer time at any fault point.

NUMA-Aware Thread Scheduling for Big Data Transfers over Terabits Network Infrastructure

The evergrowing trend of big data has led scientists to share and transfer the simulation and analytical data across the geodistributed research and computing facilities. However, the existing data transfer frameworks used for data sharing lack the capability to adopt the attributes of the underlying parallel file systems (PFS). LADS (Layout-Aware Data Scheduling) is an end-to-end data transfer tool optimized for terabit network using a layout-aware data scheduling via PFS. However, it does not consider the NUMA (Nonuniform Memory Access) architecture. In this paper, we propose a NUMA-aware thread and resource scheduling for optimized data transfer in terabit network. First, we propose distributed RMA buffers to reduce memory controller contention in CPU sockets and then schedule the threads based on CPU socket and NUMA nodes inside CPU socket to reduce memory access latency. We design and implement the proposed resource and thread scheduling in the existing LADS framework. Experimental results showed from 21.7% to 44% improvement with memory-level optimizations in the LADS framework as compared to the baseline without any optimization.

Abstract 378: The Cancer Genome Collaboratory

Abstract The Cancer Genome Collaboratory is an academic compute cloud designed to enable computational research on the world’s largest and most comprehensive cancer genome dataset, the International Cancer Genome Consortium (ICGC). The ICGC is on target to categorize the genomes of 25,000 tumors by 2018. A subproject of ICGC, the PanCancer Analysis of Whole Genomes (PCAWG) alone has generated over 800TB of harmonized sequence alignments, variants and interpreted data from over 2,800 cancer patients. A dataset of this size requires months to download and significant resources to store and process. By making the ICGC data available in cloud compute form in the Collaboratory, researchers can bring their analysis methods to the cloud, yielding benefits from the high availability, scalability and economy offered by cloud services, avoiding a large investment in static compute resources and essentially eliminating the time needed to download the data. To facilitate the computational analysis on the ICGC data, the Collaboratory has developed software solutions that are optimized for typical cancer genomics workloads, including well tested and accurate genome aligners and somatic variant calling pipelines. We have developed a simple to use, but fast and secure, data transfer tool that imports genomic data from cloud object storage into the user’s compute instances. Because a growing number of cancer datasets have restrictions on their storage locations, it is important to have software solutions that are interoperable across multiple cloud environments. We have successfully demonstrated interoperability across The Cancer Genome Atlas (TCGA) dataset hosted at University of Chicago’s Bionimbus Protected Data Cloud, the ICGC dataset hosted at the Collaboratory, and ICGC datasets stored in the Amazon Web Services (AWS) S3 storage. Lastly, we have developed a non-intrusive user authorization system that allows the Collaboratory to authenticate against the ICGC Data Access Compliance Office (DACO) when researchers require access to controlled tier data. We anticipate that our software solutions will be implemented on additional commercial and academic clouds. The Collaboratory is actively growing, with a target hardware infrastructure of over 3000 CPU cores and 15 petabytes of raw storage. As of November 2016, the Collaboratory holds information on 2,000 ICGC PCAWG donors (500TB total). We anticipate expanding the Collaboratory to host the entire ICGC dataset of 25,000 donors (approximately 5PB) and to extend its data management and analysis facilities across multiple clouds. During the current closed beta phase, the Collaboratory has been successfully utilized by multiple research groups, most notably PCAWG project researchers who analyzed thousands of genomes at scale over a few weeks’ time. The Collaboratory will open to the public during the second quarter of 2017. We invite cancer researchers to learn more about our cloud resources at cancercollaboratory.org, and apply for access to the Collaboratory. Citation Format: Christina K. Yung, George L. Mihaiescu, Bob Tiernay, Junjun Zhang, Francois Gerthoffert, Andy Yang, Jared Baker, Guillaume Bourque, Paul C. Boutros, Bartha M. Knoppers, BF Francis Ouellette, Cenk Sahinalp, Sohrab P. Shah, Vincent Ferretti, Lincoln D. Stein. The Cancer Genome Collaboratory [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 378. doi:10.1158/1538-7445.AM2017-378

Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature

The importance of data science and big data analytics is growing very fast as organizations are gearing up to leverage their information assets to gain competitive advantage. The flexibility offered through big data analytics empowers functional as well as firm-level performance. In the first phase of the study, we attempt to analyze the research on big data published in high-quality business management journals. The analysis was visualized using tools for big data and text mining to understand the dominant themes and how they are connected. Subsequently, an industry-specific categorization of the studies was done to understand the key use cases. It was found that most of the existing research focuses majorly on consumer discretionary, followed by public administration. Methodologically, a major focus in such exploration is in social media analytics, text mining and machine learning applications for meeting objectives in marketing and supply chain management. However, it was found that not much focus was highlighted in these studies to demonstrate the tools used for the analysis. To address this gap, this study also discusses the evolution, types and usage of big data tools. The brief overview of big data technologies grouped by the services they enable and some of their applications are presented. The study categorizes these tools into big data analysis platforms, databases and data warehouses, programming languages, search tools, and data aggregation and transfer tools. Finally, based on the review, future directions for exploration in big data has been provided for academic and practice.

MdtmFTP and its evaluation on ESNET SDN testbed

To address the high-performance challenges of data transfer in the big data era, we are developing and implementing mdtmFTP: a high-performance data transfer tool for big data. mdtmFTP has four salient features. First, it adopts an I/O centric architecture to execute data transfer tasks. Second, it more efficiently utilizes the underlying multicore platform through optimized thread scheduling. Third, it implements a large virtual file mechanism to address the lots-of-small-files (LOSF) problem. Finally, mdtmFTP integrates multiple optimization mechanisms, including–zero copy, asynchronous I/O, pipelining, batch processing, and pre-allocated buffer pools–to enhance performance. mdtmFTP has been extensively tested and evaluated within the ESNET 100G testbed. Evaluations show that mdtmFTP can achieve higher performance than existing data transfer tools, such as GridFTP, FDT, and BBCP.