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Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness
IntroductionThis is the dataset for the paper titled ‘Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness’ that has been accepted for publication at IEEE INFOCOM 2024. The paper’s accepted version will be available following publication in May 2024 at https://sussex.figshare.com/articles/conference_contribution/Reinforcement_learningbased_congestion_control_a_systematic_evaluation_of_fairness_efficiency_and_responsiveness/24711033. The dataset is meant to be used in conjunction with the codebase that is also made available at https://doi.org/10.25377/sussex.24978162.However, the dataset itself is of value to researchers as it contains an extensive set of metrics captured during experimentation with Reinforcement Learning-based Congestion control as discussed in the ‘Experimental Evaluation’ section of the paper. Our study is the result of a 160-hour long experimentation during which 1950 Orca, Aurora and TCP Cubic flows were measured. We have collected approximately 500GB of data encompassing diverse metrics related to network interfaces (e.g., utilisation, retransmissions, packet drops), CPU and memory parameters (such as CPU load and memory usage), as well as the data transport layer (e.g., congestion window, round trip time). Reinforcement learning (RL)-based congestion control (CC) promises efficient CC in a fast-changing networking landscape, where evolving communication technologies, applications and traffic workloads pose severe challenges to human-derived, static CC algorithms. RL-based CC is in its early days and substantial research is required to understand existing limitations, identify research challenges and, eventually, yield deployable solutions for real-world networks. In this paper we present the first reproducible and systematic study of RL-based CC with the aim to highlight strengths and uncover fundamental limitations of the state-of-the-art. We identify challenges in evaluating RL-based CC, establish a methodology for studying said approaches and perform large-scale experimentation with RL-based CC approaches that are publicly available. We show that existing approaches can acquire all available bandwidth swiftly and are resistant to non-congestive loss, however, this is commonly at the cost of excessive packet loss in normal operation. We show that, as fairness is not embedded directly into reward functions, existing approaches exhibit unfairness in almost all tested network setups. Finally, we provide evidence that existing RL-based CC approaches under-perform when the available bandwidth and end-to-end latency dynamically change. Our experimentation codebase and datasets are publicly available with the aim to galvanise the community towards transparency and reproducibility, which have been recognised as crucial for researching and evaluating machine-generated policies.The datasetThe dataset contains an extensive set of metrics captured during experimentation. It is composed of eight different experiments. Please refer to the paper for a detailed explanation of the experimental set-up. Note that some experiments are the source of more than one plot in the paper. Refer to the codebase for the relationship between plots and experiments.The data from each experiment is organised into multiple folders and files. Just under the root folder, the data is divided by the type of packet scheduling adopted by the bottleneck queue, i.e. fifo, codel, fq, fq-codel.The next folder contains all variations of bottleneck bandwidth, propagation delay, and buffer size for the same experiment. Each of the folders is named following the same pattern:{topology}_{bottleneck_bandwidth}mbps_{one_way_delay}ms_{buffer_size}pkts_{loss_rate}loss_{number_of_flows}flows_22tcpbuf_{protocol}For example: Dumbell_100mbit_80ms_279pkts_0loss_2flows_22tcpbuf_orca contains all the runs of two flows in a dumbbell topology with 100mbps bottlenck bandwidth, 160ms rtt and a buffer size of 279 MSS. Note that the semantic meaning of {protocol} depends on the specific experimental setup. In the intra-protocol fairness experiment, all flows will be {protocol}, whereas in the friendliness experiment, one flow will always be cubic and one {protocol}.Under the experiment variation folder, multiple folders contain different runs of the same variation. Depending on the experiment, a different seed may be used. However, in most of the cases, all runs are identical.Finally, each run folder contains all the raw data captured during the experiment, plus some processed data.The files and folders contained in each run folder are:tcp_probe.txt: The raw output of tcp_probe modulex[N]_output.txt: The raw output of the Nth server applicationc[N]_output.txt: The raw output of the Nth client application.emulation_info.json: JSON representation of all flows in the experiment. Each flow is represented by the source and destination IP addresses, the protocol used, and the starting time.queues (folder): Contains a text file for each of the bottleneck queue. Each file contains the output of ‘tc -s qdisc show dev’ applied to the dev in the filename.sysstat (folder): Contains the raw binary files (datafile_*.log) generated with sysstat. It also contains some log files generated using sadf processing utility on the binary datafiles. ]csvs (folder): contains processed data in csv format.</p
Intergenerational Discipleship at St. Paul\u27s Smoke Church
Intergenerational ministry through discipleship is found in the archetype of Christ and His disciples. St. Paul’s Smoke Church in Windsor Township, Pennsylvania, has impacted and influenced the area through many centuries in the generational call to follow Christ. The church has seen its ebbs and flows. In the past decade, membership has tremendously decreased, but the heart of love for God and its community is lasting. The pandemic of COVID-19 had limited ministry in the traditional sense. Still, the unorthodox ways of ministry were sought out and employed to continue St. Paul’s Smoke Church in its operation and functionality while hoping for growth. Sunday service was the strong anchor while prayer, encouragement, and practical solutions were the substance of ministry. Research methods include observation, first-hand knowledge, demographic data, interviews, surveys, and a Bible study on discipleship. The study shows that intergenerational ministry in love toward one another cannot be stopped by rules and regulations and in fact, causes many to walk closer with the Lord. The project does open a door to many other subtopics of research including successes, showing what worked, and failures, showing what was not working, in ministry during the pandemic. Further research should branch into church preparedness and prevention for the future existence of the church in power and authority outreaching with love into the world
Passive Electric Field Sensing for Ubiquitous and Environmental Perception
Electric Field Sensing plays an important role in the research branches of Environmental Perception as well as in Ubiquitous Computing. Environmental Perception aims to collect data of the surroundings, while Ubiquitous Computing has the objective of making computing available at any time. This includes the integration of sensors to perceive environmental influences in an unobtrusive way.
Electric Field Sensing, also referenced as Capacitive Sensing, is an often used sensing modality in these research fields, for example, to detect the presence of persons or to locate touches and interactions on user interfaces. Electric Field Sensing has a number of advantages over other technologies, such as the fact that Capacitive Sensing does not require direct line-of-sight contact with the object being sensed and that the sensing system can be compact in design. These advantages facilitate high integrability and allow the collection of data as required in Environmental Perception, as well as the invisible incorporation into a user's environment, needed in Ubiquitous Computing.
However, disadvantages are often attributed to Capacitive Sensing principles, such as a low sensing range of only a few centimeters and the generation of electric fields, which wastes energy and has several more problems concerning the implementation. As shown in this thesis, this only affects a subset of this sensing technology, namely the subcategory of active capacitive measurements. Therefore, this thesis focuses on the mainly open area of Passive Electric Field Sensing in the context of Ubiquitous Computing and Environmental Perception, as active Capacitive Sensing is an open research field which already gains a lot of attention. The thesis is divided into three main research questions.
First, I address the question of whether and how Passive Electric Field Sensing can be made available in a cost-effective and simple manner. To this end, I present various techniques for reducing installation costs and simplifying the handling of these sensor systems.
After the question of low-cost applicability, I examine for which applications passive electric field sensor technology is suitable at all. Therefore I present several fields of application where Passive Electric Field Sensing data can be collected.
Taking into account the possible fields of application, this work is finally dedicated to the optimization of Passive Electric Field Sensing in these cases of application. For this purpose, different, already known signal processing methods are investigated for their application for Passive Electric Field sensor data. Furthermore, besides these software optimizations, hardware optimizations for the improved use of the technology are presented
Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation
The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics
Advancing security in IoT-driven critical infrastructure: a focus on smart transportation system.
As new technological platforms such as the Internet of Things (IoT), blockchain, Artificial Intelligence (AI) and Machine Learning (ML) are gradually emerging and being integrated into critical infrastructures which are subjected to digital attacks. i.e., the critical systems are vulnerable to new cybersecurity threatsand thus requires corresponding security approach to challenge the threats.It is therefore imperative to identify the various types of possible cyber-attacks on the systems and develop a security framework to manage the associated security risks. IoT-based critical infrastructure systemslike smart healthcare, smart transportation and smart manufacturing are prone to attacks such as Denial of Service (DoS) attacks, brute-force attacks, Man-in-the-Middle attacks (MiTM), Stuxnet computer virus etc. This paper focuses on a detailed study of the smart transportation system and its security issues; various threat vectors used by the attackers are examinedalongsidecorresponding countermeasures. Additionally,an in-depth analysis on how an identified malicious attack on smart transportationcould be achieved was carried out by using an open-source vehicular network tool called Vehicle in Network Simulation (Veins). A detailed evaluation of the impact of MiTM attack was then carried out based on the evaluation metrics. Results from the simulation results indicate that attacks on the built STSthesis vehicular network have a higher influence on the network. Also, although the STSthesis was a basic network that was run with considerable node, limited time and injected malicious node, the impact of the MiTM attack was still visible. Furthermore, implementing the elliptic-curve Diffie-Hellman (ECDH) with the Advanced Encryption Standard (AES) in the early stage of design and implementation will prevent the MiTM attacks from intercepting messages between legitimate nodes
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