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Sledge: Towards Efficient Live Migration of Docker Containers

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Sledge: Towards Efficient Live Migration of Docker Container...

IEEE International Conference on Cloud computing, CLOUD

作者： Bo Xu Song Wu Jiang Xiao Hai Jin Yingxi Zhang Guoqiang Shi Tingyu Lin Jia Rao Li Yi Jizhong Jiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China The University of Texas at Arlington Arlington TX USA Alibaba Cloud Computing Co. Ltd. Hangzhou China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Modern large-scale cloud platforms require live migration technique on Docker containers with stateful workload to support load balancing, host maintenance, and Quality of Service (QoS) improvement. Efficient and scalable Docker live migration is expected to guarantee the component-integrity (image, runtime, and management context) with negligible downtime. In this paper, we present a highly efficient live migration system called Sledge, which ensures the component-integrity by integrating both images and management context during runtime migration. The key insight is that the layered image can be leveraged to reduce the migration overhead, and appropriately selective migration of management context will effectively improve QoS with negligible downtime. To achieve good scalability, a lightweight container registry mechanism for end-to-end image migration is designed to avoid the redundant layers transmission. In addition, a dynamic context loading scheme is proposed to precisely load the management context into the running daemon, which can significantly reduce downtime. Experiments show that, compared with the state-of-the-art, Sledge reduces 57% of total migration time, 55% of image migration time, and 70% downtime.

关键词： Cloud computing Runtime Scalability Loading Quality of service Containers Maintenance engineering

Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission

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arXiv 2022年

作者： Sun, Lunan Yang, Yang Chen, Mingzhe Guo, Caili Saad, Walid Poor, H. Vincent The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Net works School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA United States The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Economic and social effects

A mixed approach for fetal QRS complex detection

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A mixed approach for fetal QRS complex detection

Chinese Intelligent Systems Conference, CISC 2018

作者： Liao, Lijuan Zhong, Wei Guo, Xuemei Wang, Guoli School of Electronics and Information Technology Sun Yat-Sen University Guangzhou510006 China School of Data and Computer Science Sun Yat-Sen University Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Beijing China

ISBN: (纸本)9789811322877

Non-invasive fetal electrocardiogram (NI-FECG) plays an important role in detecting and diagnosing fetal diseases. Fetal electrocardiogram (FECG) is used to know the information of the fetal health. In this paper, we propose a mixed approach for extracting FECG from maternal abdominal ECG (AECG) recording. The proposed method is based on a combination of the wavelet transform and Support Vector Machines (SVM). As a first tier, the wavelet transform is used to detect maternal QRS complex from abdominal ECG recording. Then, a coherent averaging method was using to construct MECG and remove MECG from AECG recording. After removing MECG, SVM is used to locate fetal QRA complex from residual signal. The accuracy (84.53%) and Positive predictive value (PPV) (89.6%) in this study are much higher than other method. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Support vector machines

Environment Scan of Generative AI Infrastructure for Clinical and Translational science

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arXiv 2024年

作者： Idnay, Betina Xu, Zihan Adams, William G. Adibuzzaman, Mohammad Anderson, Nicholas R. Bahroos, Neil Bell, Douglas S. Bumgardner, Cody Campion, Thomas Castro, Mario Cimino, James J. Cohen, I. Glenn Dorr, David Elkin, Peter L. Fan, Jungwei W. Ferris, Todd Foran, David J. Hanauer, David Hogarth, Mike Huang, Kun Kalpathy-Cramer, Jayashree Kandpal, Manoj Karnik, Niranjan S. Katoch, Avnish Lai, Albert M. Lambert, Christophe G. Li, Lang Lindsell, Christopher Liu, Jinze Lu, Zhiyong Luo, Yuan McGarvey, Peter Mendonca, Eneida A. Mirhaji, Parsa Murphy, Shawn Osborne, John D. Paschalidis, Ioannis C. Harris, Paul A. Prior, Fred Shaheen, Nicholas J. Shara, Nawar Sim, Ida Tachinardi, Umberto Waitman, Lemuel R. Wright, Rosalind J. Zai, Adrian H. Zheng, Kai Lee, Sandra Soo-Jin Malin, Bradley A. Natarajan, Karthik Price, W. Nicholson Zhang, Rui Zhang, Yiye Xu, Hua Bian, Jiang Weng, Chunhua Peng, Yifan Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States Department of Pediatrics Boston Medical Center BostonMA United States Chobanian & Avedisian School of Medicine Boston University BostonMA United States Oregon Clinical and Translational Research Institute Oregon Health and Science University PortlandOR United States Department of Public Health Sciences University of California Davis DavisCA United States Keck School of Medicine University of Southern California Los AngelesCA United States Department of Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine University of Kentucky College of Medicine LexingtonKY United States Division of Pulmonary Critical Care and Sleep Medicine University of Kansas School of Medicine Kansas CityKS United States Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama BirminghamAL United States Harvard Law School Petrie-Flom Center for Health Law Policy Biotechnology and Bioethics Harvard University CambridgeMA United States Department of Biomedical Informatics University at Buffalo BuffaloNY United States Center for Clinical and Translational Science Mayo Clinic RochesterMN United States Technology and Digital Solutions Stanford Medicine Stanford University StanfordCA United States Center for Biomedical Informatics Rutgers Cancer Institute New BrunswickNJ United States Department of Learning Health Sciences University of Michigan Medical School Ann ArborMI United States University of California San Diego La Jolla CA United States Department of Biostatistics and Health Data Science School of Medicine Indiana University IndianapolisIN United States Division of Artificial Medical Intelligence in Ophthalmology Univ

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational science Award (CTSA) Program led by the National Center for Advancing Translational sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency. © 2024, CC BY.

关键词： Electronic health record

Cooperation dynamics under pandemic risks and heterogeneous economic interdependence

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arXiv 2021年

作者： Serrano, Manuel Chica Hernández, Juan M. Santos, Francisco C. Andalusian Research Institute DaSCI "Data Science and Computational Intelligence" University of Granada Granada18071 Spain School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia University of Las Palmas de Gran Canaria Las Palmas35017 Spain INESC-ID Instituto Superior Técnico Universidade de Lisboa Porto Salvo2744-016 Portugal

The spread of COVID-19 and ensuing containment measures have accentuated the profound interdependence among nations or regions. This has been particularly evident in tourism, one of the sectors most affected by uncoordinated mobility restrictions. The impact of this interdependence on the tendency to adopt less or more restrictive measures is hard to evaluate, more so if diversity in economic exposures to citizens' mobility are considered. Here, we address this problem by developing an analytical and computational game-theoretical model encompassing the conflicts arising from the need to control the economic effects of global risks, such as in the COVID-19 pandemic. The model includes the individual costs derived from severe restrictions imposed by governments, including the resulting economic interdependence among all the parties involved in the game. By using tourism-based data, the model is enriched with actual heterogeneous income losses, such that every player has a different economic cost when applying restrictions. We show that economic interdependence enhances cooperation because of the decline in the expected payoffs by free-riding parties (i.e., those neglecting the application of mobility restrictions). Furthermore, we show (analytically and through numerical simulations) that these cross-exposures can transform the nature of the cooperation dilemma each region or country faces, modifying the position of the fixed points and the size of the basins of attraction that characterize this class of games. Finally, our results suggest that heterogeneity among regions may be used to leverage the impact of intervention policies by ensuring an agreement among the most relevant initial set of cooperators. © 2021, CC BY-NC-ND.

关键词： Game theory

Adaptive Secure Control of Uncertain Second-order Nonlinear Cyber-physical System Against Intermittent DoS Attacks

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Adaptive Secure Control of Uncertain Second-order Nonlinear ...

International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Mengze Yu Wei Wang Changyun Wen School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Electrical and Electronic Engineering Nanyang Technological University Nanyang Avenue Singapore Singapore

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Cyber-physical systems (CPSs) are often complexly nonlinear and uncertain. This paper investigates the adaptive output-feedback control problem for CPSs subject to intermittent denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of uncertain second-order strict-feedback nonlinear systems. When a DoS attack is active, the output signal becomes unavailable. To overcome this diffculty, an adaptive observer is constructed. Based on an average-dwell-time (ADT) method incorporated by frequency and duration properties of DoS attacks, convex design conditions of controller parameters are derived by solving a set of linear matrix inequalities (LMI). The proposed controller guarantees that all closed-loop signals remain globally bounded. An illustrative example is included to validate the theoretical results.

关键词： Adaptive systems Observers Denial-of-service attack Backstepping Adaptation models Stability analysis Closed loop systems

The energy revolution: Cyber physical advances and opportunities for smart local energy systems

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arXiv 2021年

作者： Verba, Nandor Gaura, Elena McArthur, Stephen Konstantopoulos, George Wu, Jianzhoug Fan, Zhong Athanasiadis, Dimitrios Monasterios, Pablo Rodolfo Baldivieso Morris, Euan Hardy, Jeffrey Centre for Data Science Coventry University United Kingdom Department of Electronic and Electrical Engineering University of Strathclyde United Kingdom Department of Automatic Control & Systems Engineering University of Sheffield United Kingdom Department of Electrical and Electronic Engineering Cardiff University United Kingdom School of Computing and Mathematics Keele University United Kingdom Grantham Institute Imperial College London United Kingdom

Accuracy-Guaranteed Event Detection via Collaborative Mobile Crowdsensing with Unreliable Users 15th

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Accuracy-Guaranteed Event Detection via Collaborative Mobile...

15th EAI International Conference on Collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Liu, Tong Wu, Wenbin Zhu, Yanmin Tong, Weiqin School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Scalable Computing and Systems Shanghai China

ISBN: (纸本)9783030301453

Recently, mobile crowdsensing has become a promising paradigm to collect rich spatial sensing data, by taking advantage of widely distributed sensing devices like smartphones. Based on sensing data, event detection can be conducted in urban areas, to monitor abnormal incidents like traffic jam. However, how to guarantee the detection accuracy is still an open issue, especially when unreliable users who may report wrong observations are considered. In this work, we focus on the problem of user recruitment in collaborative mobile crowdsensing, aiming to optimize the fine-grained detection accuracy in a large urban area. Unfortunately, the problem is proved to be NP-hard, which means there is no polynomial-time algorithm to achieve the optimal solution unless P NP. To meet the challenge, we first employ a probabilistic model to characterize the unreliability of users, and measure the uncertainty of inferring event occurrences given collected observations by Shannon entropy. Then, by leveraging the properties of adaptive monotonicity and adaptive submodularity, we propose an adaptive greedy algorithm for user recruitment, which is theoretically proved to achieve a constant approximation ratio guarantee. Extensive simulations are conducted, which show our proposed algorithm outperforms baselines under different settings. © 2019, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Polynomial approximation

Predicting Large-scale Protein-protein Interactions by Extracting Coevolutionary Patterns with MapReduce Paradigm

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Predicting Large-scale Protein-protein Interactions by Extra...

IEEE International Conference on Systems, Man and Cybernetics

作者： Lun Hu Bo-Wei Zhao Shicheng Yang Xin Luo MengChu Zhou Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China School of Computer Science and Technology Wuhan University of Technology Wuhan China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781665442084

Protein-protein interactions are of great significance for us to understand the functional mechanisms of proteins. With the rapid development of high-throughput genomic technology, the amount of protein-protein interaction data has become so big that most of existing prediction algorithms are no longer applicable. To address this problem, we develop a distributed framework by reimplementing one of state-of-the-art algorithms, i.e., CoFex, by using MapReduce. In particular, we adopt a novel tree-based data structure to reduce the heavy memory consumption cased by the huge sequence information of proteins. After that, the procedure of CoFex is modified by following the paradigm of MapReduce such that the prediction task can be completed in a distributed manner, thus fulfilling the demanding requirements of large-scale protein-protein interaction prediction. A series of experiments have been conducted to evaluate the performance of the proposed distributed framework in terms of both efficiency and effectiveness. Experimental results demonstrate that the proposed framework can considerably improve the efficiency of CoFex by achieving more than two-orders-of-magnitude improvement in computational efficiency while retaining a comparable level of accuracy.

关键词： Proteins Memory management Genomics Predictive models Prediction algorithms data structures Computational efficiency