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IEEE Conference on Network Softwarization (NetSoft)

作者： Shoaib Basu Deepak Nadig Dept. of Computer and Information Technology Purdue University West Lafayette IN

ISBN: (数字)9798350369588

ISBN: (纸本)9798350369595

The landscape of cloud deployments has surged, evolving into a cornerstone for modern infrastructures. However, with this advancement, a key challenge remains: general-purpose CPUs perform infrastructure functions of the cloud deployments, i.e., storage, security, and networking. SmartNICs have emerged as a transformative solution, integrating specialized processing capabilities with the help of processing cores and accelerators directly on the NIC hardware. These intelligent adapters offload and accelerate infrastructure functions, enhancing throughput, reducing latency, and alleviating the burdens on host CPUs. Their ability to handle complex networking, storage, and security tasks at line speed revolutionizes cloud deployments, optimizing resource utilization and enabling seamless scalability while mitigating the performance bottlenecks encountered with conventional NICs. In this paper, we explore offloading NVMe over Fabrics (NVMe-oF) to SmartNIC. We present an experimental design on the FABRIC testbed to perform NVMe-oF by offloading it to a SmartNIC. Our overall goal is to compare CPU performance with and without offloading, benchmark SmartNIC over a regular NIC, and estimate CPU cycle savings.

关键词： Scalability Benchmark testing Throughput Fabrics Hardware Security Resource management

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Shopping While Watching: An Updated Forensic Analysis of TikTok on Android and iOS

Shopping While Watching: An Updated Forensic Analysis of Tik...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Xiao Hu Umit Karabiyik Computer and Information Technology Purdue University West Lafayette United States

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

TikTok is one of the most popular social media platforms in the world. Despite its extensive use, mobile forensics research on TikTok has seen little progress in the past two years, possibly due to limitations faced by some researchers using this app. This paper presents a forensic analysis of TikTok, focusing on recent inadequacies in mobile forensic research on it, especially with the advent of features like the TikTok Shop. This study not only describes the forensic value of the new shopping feature, but also updates artifacts from previous studies on general information and media sections, providing investigators with information about TikTok-related folder structures and important data paths. For general information, we extracted data about user accounts and apps, and for media information, we found video content uploaded and viewed by users, and proposed possible methods to distinguish what users actually watched. This work improves the understanding of the digital environment and offers valuable forensic insights crucial to keeping up with rapid technological advancements in social media platforms for digital forensic investigations.

关键词： computers Video on demand Social networking (online) Forensics Digital forensics Focusing Media Feature extraction Web sites Data mining

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Enhancing Workload Predictions Using Service Interactions in Cloud-Native Microservices

Enhancing Workload Predictions Using Service Interactions in...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Isham Mahajan Deepak Nadig Dept. of Computer and Information Technology Purdue University West Lafayette IN

ISBN: (数字)9798350376562

ISBN: (纸本)9798350376579

Container orchestration systems, such as Kuber-netes, often rely on manual resource allocation to manage re-sources, which can be inefficient and inflexible due to the frequent over-provisioning or underprovisioning of resources. Kubernetes horizontal pod autoscaler (HPA), vertical pod autoscaler (VPA), and Google Kubernetes Engine (GKE) Autopilot are primarily threshold-based, making them reactive rather than proactive as they adjust resources after exceeding utilization thresholds, leading to temporary degradation in quality of service (QoS). While some solutions utilize calls per minute (CPM) counts for requests to microservices to estimate resource consumption dynamically, they do not fully exploit distributed traces or associated microservices' interdependencies. We hypothesize that we can gain deeper insights into future workload patterns by exploiting microservices' interaction and the CPM counts for each pair of communicating microservices. In this paper, we propose a comprehensive machine learning workflow to assess whether factoring in the interdependencies between microservices results in improved workload prediction. Our findings indicate that an LSTM model performs well, with average mean absolute error (MAE) and root mean square error (RMSE) values of 7.02 and 10.54, respectively. The highest $R^{2}$ score observed was 0.07. This suggests that although incorporating distributed traces and inter-microservice CPM counts provides valuable insights, the models fail to capture the full complexity of workload dynamics. These results highlight the potential for enhancing workload prediction accuracy and underscore the need to refine these methods further to achieve more proactive and efficient resource allocation in container orchestration systems.

关键词： Microservice architectures Quality of service Manuals Machine learning Containers Internet Resource management Root mean square Long short term memory Engines

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Oh No, Not Another Trolley! on the Need for a Co-Liberative Consciousness in CS Pedagogy

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第1期3卷 67-74页

作者： Williams, Rua M. Smarr, Simone Prioleau, Diandra Gilbert, Juan E. Computer Graphics and Technology Department Purdue University West LafayetteIN47907 United States Computer and Information Sciences and Engineering Department University of Florida GainesvilleFL32611 United States

Due to growing concerns for the disproportionate dangers, artificial intelligence (AI) advances pose to marginalized groups, proposals for procedural solutions to ethics in AI abound. It is time to consider that some systems may be inherently unethical, even violent, whether or not they are fair. In this article, we deploy a feminist critical discourse analysis of long-format responses to ethical scenarios from computing science undergraduate students. We find that even among students that had a strong understanding of social justice and the power of AI to exacerbate existing inequities, most students contextualize these problems as the product of biased datasets and human mis/trust factors, rather than as problems of design and purpose. Further, while many students recognized racism and classism at play in the potential negative impacts of AI systems, most students failed to recognize ableism as a driving social force for inequity. As computing science faculty, we must recognize that our students graduate to become the researchers and developers of future technosocial systems. Pedagogically, we need more than procedural fixes to systemic inequities. We are not going to program our way into justice. We must learn to say no to building violent things. © 2022 IEEE.

关键词： Students

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Data Processing Unit (DPU) Based Network Process Offloading for Efficient Service Meshes

Data Processing Unit (DPU) Based Network Process Offloading ...

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IEEE Conference on Network Softwarization (NetSoft)

作者： Karumuri Meher Hasanth Shoaib Basu Deepak Nadig Dept. of Computer and Information Technology Purdue University West Lafayette IN

ISBN: (数字)9798350369588

ISBN: (纸本)9798350369595

As the software development landscape transitions from monolithic applications to agile, cloud-native microservices, new challenges in communication have emerged, necessitating the evolution of network service meshes to effectively manage these complexities. While addressing these communication challenges, implementing service mesh solutions introduces higher resource utilization, primarily due to deploying an additional per-microservice infrastructure known as the “sidecar” container. A sidecarless approach, as exemplified by Cilium, leverages extended Berkeley packet filter (eBPF) technology to minimize operational overhead and optimize resource utilization. The advent of data processing units (DPU) represents a crucial evolution in cloud accelerators, designed to offload and accelerate networking, security, and storage tasks traditionally handled by server CPUs. This demonstration integrates DPU technology with Cilium’s sidecarless architecture and proposes to offload Cilium’s control plane to DPUs. Our proposed approach improves system efficiency by deploying Cilium’s control plane components on DPUs in a separate host mode. Further, our approach conserves CPU cycles on the primary hosts and shifts network processing tasks to the DPU. This strategic offload aims to enhance throughput, reduce latency, and unburden CPUs from network processing tasks to improve the performance of service mesh architectures for cloud-native ecosystems.

关键词： Microservice architectures computer architecture Data processing Throughput Resource management Servers Security

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Fuzzy Q-Table Reinforcement Learning for continues State Spaces: A Case Study on Bitcoin Futures Trading

Fuzzy Q-Table Reinforcement Learning for continues State Spa...

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International Conference on Control, Decision and information Technologies (CoDIT)

作者： Zahra Ghorrati Kourosh Shahnazari Ahmad Esmaeili Eric T. Matson Department of Computer and Information Technology Purdue University West Lafayette IN USA Department of Computer Engineering and Information Technology Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

One of the simplest approach in Reinforcement Learning (RL) is updating Q-table using Bellman operator. While theoretical expectations hint at the potential convergence achieved by modeling the discrete Q-table with the Bellman operator, practical limitations surface in real-world scenarios. The main challenges associated with it include the exponential growth of the Q-table size with an increasing number of state dimensions and the inability to use the Q-table in continuous state spaces. Alternative approaches, such as employing neural networks to approximate the parameterized Q-function, may not necessarily result in *** response to these challenges, this paper introduces an simple innovative methodology inspired by the Bellman method updating. The proposed method utilizes fuzzy rules to discretize the state space, leading to the direct use of the Bellman operator for updating the fuzzy neural network weights, effectively acting as the Fuzzy Q-table. Instead of approximating the Q-function utilizing neural network/deep neural network based on gradient approaches, the proposed method establishes a Fuzzy Q-table and updates it using the Bellman equation. This strategic decision helps to solve the convergence problem in addition to prevent entrapment in local minima problems, a common challenge faced by conventional gradient methods. The efficacy of the proposed approach is demonstrated through its application to trading in the Bitcoin Futures Market, showcasing its ability to navigate complexities and uncertainties. Beyond financial markets, this methodology presents a versatile solution applicable to a diverse range of reinforcement learning problems, addressing limitations faced by traditional Q-tables or DQN.

关键词： Fuzzy logic Training Uncertainty Q-learning Navigation Neural networks Bitcoin Fuzzy neural networks Mathematical models Convergence

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Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People’s Cough Detection Models 12th

Transfer Learning to Detect COVID-19 Coughs with Increment...

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12th International Conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Vhaduri, Sudip Paik, Seungyeon Huber, Jessica E. Computer and Information Technology Department Purdue University West LafayetteIN47907 United States Speech Language and Hearing Sciences Department Purdue University West LafayetteIN47907 United States Communicative Disorders and Sciences Department University at Buffalo BuffaloNY14214 United States

ISBN: (纸本)9783031606649

Millions of people have died worldwide from COVID-19. In addition to its high death toll, COVID-19 has led to unbearable suffering for individuals and a huge global burden to the healthcare sector. Therefore, researchers have been trying to develop tools to detect symptoms of this human-transmissible disease remotely to control its rapid spread. Coughing is one of the common symptoms that researchers have been trying to detect objectively from smartphone microphone-sensing. While most of the approaches to detect and track cough symptoms rely on machine learning models developed from a large amount of patient data, this is not possible at the early stage of an outbreak. In this work, we present an incremental transfer learning approach that leverages the relationship between healthy peoples’ coughs and COVID-19 patients’ coughs to detect COVID-19 coughs with reasonable accuracy using a pre-trained healthy cough detection model and a relatively small set of patient coughs, reducing the need for large patient dataset to train the model. This type of model can be a game changer in detecting the onset of a novel respiratory virus. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： COVID-19

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Fairness in survival analysis with distributionally robust optimization

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 11887-11971页

作者： Shu Hu George H. Chen Department of Computer and Information Technology Purdue University Indianapolis IN Heinz College of Information Systems and Public Policy Carnegie Mellon University Pittsburgh PA

We propose a general approach for encouraging fairness in survival analysis models that is based on minimizing a worst-case error across all subpopulations that are "large enough" (occurring with at least a user-specified probability threshold). This approach can be used to convert a wide variety of existing survival analysis models into ones that simultaneously encourage fairness, without requiring the user to specify which attributes or features to treat as sensitive in the training loss function. From a technical standpoint, our approach applies recent methodological developments of distributionally robust optimization (DRO) to survival analysis. The complication is that existing DRO theory uses a training loss function that decomposes across contributions of individual data points, i.e., any term that shows up in the loss function depends only on a single training point. This decomposition does not hold for commonly used survival loss functions, including for the standard Cox proportional hazards model, its deep neural network variants, and many other recently developed survival analysis models that use loss functions involving ranking or similarity score calculations. We address this technical hurdle using a sample splitting strategy. We demonstrate our sample splitting DRO approach by using it to create fair versions of a diverse set of existing survival analysis models including the classical Cox model (and its deep neural network variant DeepSurv), the discrete-time model DeepHit, and the neural ODE model SODEN. We also establish a finite-sample theoretical guarantee to show what our sample splitting DRO loss converges to. Specifically for the Cox model, we further derive an exact DRO approach that does not use sample splitting. For all the survival models that we convert into DRO variants, we show that the DRO variants often score better on recently established fairness metrics (without incurring a significant drop in accuracy) compared to existing survival

关键词： survival analysis fairness distributionally robust optimization

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Comparison of goTenna and ClusterDuck Protocol: Evaluation of the Performance under Various Conditions 14

Comparison of goTenna and ClusterDuck Protocol: Evaluation o...

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14th International Conference on information and Communication technology Convergence, ICTC 2023

作者： Lee, Jinmyeong Bae, Seonghyeon Lim, Geonu Melenchuk, Ryan Lee, Sihyeong Smith, Anthony Environmental Engineering Chungnam National University Daejeon Korea Republic of Computer Science and Engineering Chungnam National University Daejeon Korea Republic of Computer and Information Technology Purdue University West LafayetteIN United States

ISBN: (纸本)9798350313277

Natural catastrophes cause widespread damage, including the destruction of communication infrastructure;When communication infrastructure goes down, people can no longer send SOS signals or messages for help. Furthermore, with previous problems, the frequency of natural disasters and the necessity for communication systems in emergencies is rising every year. For these reasons, companies and organizations have developed innumerable technologies focused on disaster relief. However, there is a lack of comparative research on the practical application of communication technology in this emergency environment. Therefore, two technologies (goTenna and ClusterDuck Protocol) had changes in their Received Signal Strength Indicator (RSSI) and Message Delivery Ratio (MDR) compared over a variety of environments and differing ranges. This study aims to compare the transmission range of two technologies under various conditions and provide guidance for selecting the appropriate technology based on specific use cases. By analyzing the results, this study seeks to identify the strengths and weaknesses of each technology and provide insights into their optimal use cases. © 2023 IEEE.

关键词： Aspen Grove ClusterDuck Protocol(CDP) goTenna LoRa Mesh Network

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Deploying a Sustainable Deep Learning Pipeline for Poison Ivy Image Classification 1

Deploying a Sustainable Deep Learning Pipeline for Poison Iv...

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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Park, Wonjun Kim, Subin Cho, Sumin Lee, Jiyeon Mahedy, Jack Gebresalssie, Nebey Lee, Minji Computer Science and Engineering Konkuk University Seoul Korea Republic of Computer Software Engineering Soonchunhyang University Asan Korea Republic of Human Centered Artificial Intelligence Sangmyung University Seoul Korea Republic of Computer Science and Engineering Kyonggi University Suwon Korea Republic of Computer and Information Technology Purdue University West LafayetteIN United States

ISBN: (纸本)9798350371987

Poison ivy, causing irritated skin in humans due to the urushiol oil it contains, is a well-known plant mainly distinguishable by the traits of its leaves. Nevertheless, according to the statistical data, a significant number of people are still affected by the plant. This research postulates that this notorious result stemmed from the scarce analysis about the plant. The paper proposes an automated pipeline, functioning Continuous Integration, Deployment, and Training on Kubeflow so that the harmful impact of the plant decreases, leading the public health outcomes to enhance. The pipeline contains a dataset composed of both a set of leaves and a stem images which are manually collected from a local farm and a devised model which considers both features to classify Western poison ivy. With the pipeline, the research contributes to decrease the number of suffering people from the plant as promptly reacting to seasonal changes. ©2024 IEEE.

关键词： Pipelines

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