Cloud Computing
The advent of the Internet of Things has increased the interest in automating mission-critical processes from domains such as smart cities. These applications' stringent Quality of Service (QoS) requirements motivate their deployment through the Cloud-IoT Continuum, which requires solving the NP-hard problem of placing the application's services onto the infrastructure's devices. Moreover, as the infrastructure and application change over time, the placement needs to continuously adapt to these changes to maintain an acceptable QoS.
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The Transport-level pAcket RouTing ANalysis Tool for Cloud-native Applications (TARTAN) Dataset contains TARTAN/HiPerConTracer Traceroute runs between an endpoint in Oslo, Norway and the public Comprehensive TeX Archive Network (CTAN, https://www.ctan.org) and Comprehensive R Archive Network (CRAN, https://cran.r-project.org) mirror we
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381 Views
When designing task scheduling algorithms in mobile edge computing (MEC), the mobile device (MD)'s mobility becomes an important concern, since the change in MD's location would affect the data transmission rate, leading to fluctuations in task transmission duration and completion time. In this paper, we study a mobility-aware task off-and-downloading scheduling problem in MEC, considering both the communication delay and energy consumption caused by the data offloading and the result downloading.
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860 Views
This quantitative correlational research study aimed to investigate the factors affecting the implementation of zero-trust security and multifactor authentication (MFA) in a fog computing environment. Fog computing is an emerging decentralized technology that extends cloud computing capabilities near the user. A fog computing environment helps in faster communication with the internet of things (IoT) devices and reduces data transmission overheads.
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561 Views
Predicting the data transfer throughput of cloud networks plays an important role in several resource optimization applications, such as auto-scaling, replica selection, and load balancing. However, constant short-term variations in cloud networks make the prediction of end-to-end data transfer throughput a very challenging task.
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330 Views
This data collection focuses on capturing user-generated content from the popular social network Reddit during the year 2023. This dataset comprises 29 user-friendly CSV files collected from Reddit, containing textual data associated with various emotions and related concepts.
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2075 Views
Goal
The goal of this project is to leverage Amazon Web Service's machine learning services to create a dataset that automatically adds and updates files on IEEE DataPort's S3 storage. Through this process, we sought to learn and demonstrate how an ongoing data collection script can create a shared living dataset by streaming data to our IEEE DataPort dataset storage. In the process, we also hoped to gain further insights into areas including:
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211 Views
This dataset contains .pcap files collected during the execution of variant calling on large number of human genomes using a cluster. The GATK4 variant calling pipeline was executed using AVAH in two testbeds, CloudLab and FABRIC. A 16-node cluster was used on CloudLab, and an 8-node cluster was used on FABRIC. The files were collected by running tcpdump on the network interfaces of the nodes.
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893 Views
Currently, Internet applications running on mobile devices generate a massive amount of data that can be transmitted to a Cloud for processing. However, one fundamental limitation of a Cloud is the connectivity with end devices. Fog computing overcomes this limitation and supports the requirements of time-sensitive applications by distributing computation, communication, and storage services along the Cloud to Things (C2T) continuum, empowering potential new applications, such as smart cities, augmented reality (AR), and virtual reality (VR).
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1369 Views
The main objective of this project is to design and develop a collaborative framework which facilitates real-time tracking of a target person even when GPS signal is not available, while collecting motion data to infer his or her lifestyle and health status. The framework orchestrates a wide range of technologies such as localization technologies, machine learning and AI, sensor data analytics and cloud computing. The overall framework design also takes into consideration the culture, lifestyles, behaviours and infrastructures of ASEAN countries.
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555 Views