Large-scale data transport for data-intensive sciences is a complex multidimensional challenge. The challenge includes optimizing the end-to-end Big Data movement performance in real-time, supporting direct remote data access using NVMe over Fabrics (NVMeoF) and deploying to existing research platforms. AIDTN is the first effort to provide a unique AI system designed to incorporate NVMe over Fabrics (NVMeoF) and optimize coordination among multiple components supporting large-scale, multi-domain Wide Area Network (WAN) data-intensive science. AIDTN's research objective is to integrate next-generation storage architecture using NVMeoF, specialized network design using high-performance network appliances, Data Transfer Nodes (DTNs), catalysts in driving data transport, and a unique AI system explicitly designed for high-performance data movement challenges. AIDTN is the first system that uses network and system features to predict the end-to-end performance of high-performance data movement and further extends the model with NVMe-specific features for NVMeoF remote data access. As a result, AIDTN improves data movement performance by up to 284% while minimizing packet loss compared to other heuristics approaches. It also has a prediction error rate as low as 0.16 compared to AI models with the only network (error rate = 0.29) or network and system features (error rate = 0.19).

Mobile devices are equipped with limited processing power and battery charge. A mobile computation offloading framework is a software that provides better user experience in terms of computation time and energy consumption, also taking profit from edge computing facilities. This article presents User-Level Online Offloading Framework (ULOOF), a lightweight and efficient framework for mobile computation offloading. ULOOF is equipped with a decision engine that minimizes remote execution overhead, while not requiring any modification in the device’s operating system. By means of real experiments with Android systems and simulations using large-scale data from a major cellular network provider, we show that ULOOF can offload up to 73 percent of computations, and improve the execution time by 50 percent while at the same time significantly reducing the energy consumption of mobile devices.

Mobile computation offloading has recently attracted much interest and first offloading solutions have been developed. However, the relevant technical challenge of how to automatically determine offloadable sections of Android applications has not been adequately investigated so far. This paper proposes an innovative task selection algorithm that can parse an Android application autonomously and classify all the methods based on their offloadability by adopting a fine grained and multi-steps analyzer. The reported experimental results show the effectiveness of our solution when applied to the top 25 most downloaded Android apps on the Google Play store, by showing its accuracy in identifying off loadable methods and demonstrating the potential benefits of automated mobile computation offloading.

Measuring quality of Internet access is important because it provides rich information on capability of Internet services and user experience. As Internet services actively evolve, users tend to spend more time and require more network capacity over time. To meet the needs of consumers, there is a wide mix of ISPs and access technologies offered in New Zealand. Benchmarking ISPs in New Zealand for their quality of service enables us to predict what users may experience among different ISPs. In this paper, we present a study of broadband service performance in New Zealand ISPs. Our results will provide accurate information on the quality of service users experience from New Zealand ISPs and helps both ISPs and Government to understand current Internet service market.

Large-scale scientific installations generate voluminous amounts of data (or big data) every day. These data often need to be transferred using high-speed links (typically with 10 Gb/s or more link capacity) to researchers located around the globe for storage and analysis. Efficiently transferring big data across countries or continents requires specialized big data transfer protocols. Several big data transfer protocols have been proposed in the literature, however, a comparative analysis of these protocols over a long distance international network is lacking in the literature. We present a comparative performance and fairness study of three open-source big data transfer protocols, namely, GridFTP, FDT, and UDT, using a 10 Gb/s high-speed link between New Zealand and Sweden. We find that there is limited performance difference between GridFTP and FDT. GridFTP is stable in terms of handling file system and TCP socket buffer. UDT has an implementation issue that limits its performance. FDT has issues with small buffer size limiting its performance, however, this problem is overcome by using multiple flows. Our work indicates that faster file systems and larger TCP socket buffers in both the operating system and application are useful in improving data transfer rates.

This paper presents a characterization study of big data transfer protocols on a long-haul network. We analyzed the performance and fairness of three well-known open-source protocols, namely, GridFTP, FDT, and UDT. Using a real-world 10 Gb/s network link between New Zealand and Sweden, we studied data transfer rates (in terms of goodput) and fairness (in terms of impact on round trip time) of the protocols. We performed extensive experiments using single and multiple data flows to comprehend how these protocols behave in real-world situations. We found that GridFTP has the fastest data transfer rates when using a single flow. UDT suffered from poor performance due to implementation issues. A small buffer size limited FDT's performance, however, this drawback can be overcome by using multiple flows in lieu of fairness.

We know that students solve problems in different ways, but we know little about the kinds of variation, or the degree of variation between these student generated solutions. In this paper, we propose a taxonomy that classifies the variation between correct student solutions in objective terms, and we show how the application of the taxonomy provides instructors with additional insight about the differences between student solutions. This taxonomy may be used to inform instructors in selecting examples of code for teaching purposes, and provides the possibility of automatically applying the taxonomy to existing solution sets.

Researchers working in diverse fields such as astronomy, experimental physics, genomics, and meteorology have to frequently deal with analyzing voluminous amounts of complex data. Such data is often referred to as big data. These researchers work in teams and have to transfer this data over long distances. Efficiently transferring big data over long distances requires the use of appropriate transfer protocols. Several TCP-based and UDP-based protocols have been proposed in the literature, however, a comparative analysis of such protocols is lacking in the literature. This paper presents a comparative performance analysis of four well-known high-speed data transfer protocols for long fat networks, namely, GridFTP, FDT, UDT, and Tsunami. We performed extensive experiments to measure the effectiveness of each protocol in terms of its throughput for various roundtrip times, and against increasing levels of congestion inducing TCP or UDP background traffic on a 10 Gb/s network. Our results show that without much tuning, TCP based protocols are able to achieve throughputs of more than 2 Gb/s. In presence of background traffic, UDP protocols perform better.