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RITA: Efficient Memory Allocation Scheme for Containerized parallel Systems to Improve Data processing Latency 13

RITA: Efficient Memory Allocation Scheme for Containerized P...

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IEEE 13th International conference on Cloud Computing (CLOUD)

作者： Jia, Danlin Bayati, Mahsa Lee, Ron Mi, Ningfang Northeastern Univ Boston MA 02115 USA Samsung Semicond San Jose CA USA

ISBN: (纸本)9781728187808

The emerging resource-sharing container-based virtualization is prevalent in IT, as it is a much lighter deployment in the cloud environment compared to VM-based virtualization. distributed data-processing workloads executing in parallel take advantages of resource sharing, fast delivery, and excellent portability of containerization, but also suffer from resource competition and performance interference. Especially for memory virtualization, data-processing frameworks allocate physical memory (i.e., RAM) and swap to applications specified by users, without considering cache-characteristics and parallelism of applications, which induces performance degradation and significantly protracted latency which is worse given over-provisioning. We design an efficient memory allocation scheme (RITA) for containerized parallel systems to improve data processing latency. RITA monitors memory usage and cache characteristics of applications, and dynamically re-allocates memory resources. We implement RITA in a real-world system, which can easily migrate to other container-based virtualization environments. Our experimental results show that RITA provides remarkable latency improvement for memory intensive distributed data-processing workloads.

关键词： Containerization distributed Data-processing Latency Memory Allocation

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Resource management method of distributed parallel database based on directed graph 6

Resource management method of distributed parallel database ...

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6th Annual International conference on network and Information Systems for Computers, ICNISC 2020

作者： Shi, Yingjie Zhang, Huayun Wang, Xiang Zhou, Yuchao Jiang, Shusong China Realtime Database Co.Ltd. Building 6 Big Data Industrial Park No.180 software Avenue Yuhuatai District Nanjing China

A distributed parallel database resource management method based on directed graph is proposed. By using content distributor based on distributed unstructured P2P network association, the high performance and stability of distributed parallel database system in dynamic changing environment are guaranteed. The problem of network congestion caused by too many redundant messages and the "barrel effect" caused by resource imbalance due to differential configuration is solved, through resource search algorithm based on directed graph lookahead, and the query node cached the resource information of two-level neighbor nodes. By adopting the Linux cgroups resource management mechanism, fully considering the multi tenant and multi factor based resource scheduling strategy, reduce resource fragments and better meet the problem of distributed parallel database storage or hot spot processing. © 2020 Journal of Physics: conference Series.

关键词： Directed graphs

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Gradient Forward-Propagation for Large-Scale Temporal Video Modelling

Gradient Forward-Propagation for Large-Scale Temporal Video ...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Malinowski, Mateusz Vytiniotis, Dimitrios Swirszcz, Grzegorz Patraucean, Viorica Carreira, Joao DeepMind London England

ISBN: (纸本)9781665445092

How can neural networks be trained on large-volume temporal data efficiently? To compute the gradients required to update parameters, backpropagation blocks computations until the forward and backward passes are completed. For temporal signals, this introduces high latency and hinders real-time learning. It also creates a coupling between consecutive layers, which limits model parallelism and increases memory consumption. In this paper, we build upon Sideways, which avoids blocking by propagating approximate gradients forward in time, and we propose mechanisms for temporal integration of information based on different variants of skip connections. We also show how to decouple computation and delegate individual neural modules to different devices, allowing distributed and parallel training. The proposed Skip-Sideways achieves low latency training, model parallelism, and, importantly, is capable of extracting temporal features, leading to more stable training and improved performance on real-world action recognition video datasets such as HMDB51, UCF101, and the large-scale Kinetics-600. Finally, we also show that models trained with Skip-Sideways generate better future frames than Sideways models, and hence they can better utilize motion cues.

关键词： Training Couplings Three-dimensional displays Computational modeling parallel processing Streaming media Feature extraction

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Analysis of Genome Architecture Mapping Data with a Machine Learning and Polymer-Physics-based Tool

Analysis of Genome Architecture Mapping Data with a Machine ...

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26th International conference on parallel and distributed Computing (Euro-Par)

作者： Fiorillo, Luca Conte, Mattia Esposito, Andrea Musella, Francesco Flora, Francesco Chiariello, Andrea M. Bianco, Simona Univ Napoli Federico II Dipartimento Fis I-80126 Naples Italy Complesso Univ Monte St Angelo INFN Napoli I-80126 Naples Italy

ISBN: (纸本)9783030715939;9783030715922

Understanding the mechanisms driving the folding of chromosomes in nuclei is a major goal of modern Molecular Biology. Recent technological advances in microscopy (FISH, STORM) and sequencing approaches (Hi-C, GAM, SPRITE) enabled to collect quantitative data about chromatin 3D architecture, revealing a non-random and highly specific organization. To transform such tremendous amount of data into valuable insights on genome folding, heavy computational analyses are required. Here, we study the performances of PRISMR, a computational tool based on Machine Learning strategies and Polymer Physics principles, to explore genome 3D structure from Genome Architecture Mapping (GAM) data. Using such data, we show that PRISMR can successfully reconstruct the 3D structure of real genomic regions at various length scales, from mega-base sized loci to whole chromosomes. Importantly, the inferred structures are validated against independent Hi-C data. Finally, we show how PRISMR can be effectively employed to explore differences between experimental methods.

关键词： Chromatin organization Machine learning GAM

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Trusted Auditing of Data Operation Behaviors in Cloud based on Blockchain and TEE

Trusted Auditing of Data Operation Behaviors in Cloud based ...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Shujiang Xu Fansheng Wang Lianhai Wang Miodrag J. Mihaljević Shuhui Zhang Wei Shao Qihao Huang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Mathematical Institute the Serbian Academy of Sciences and Arts Belgrade Serbia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the Internet of Things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. The third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users’ identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. The data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. The security analysis and experiment results indicate that this scheme is secure and feasible.

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2nd International conference on Computing, Communication, and Learning, CoCoLe 2023

2nd International Conference on Computing, Communication, an...

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2nd International conference on Computing, Communication, and Learning, CoCoLe 2023

ISBN: (纸本)9783031569975

The proceedings contain 25 papers. The special focus in this conference is on Computing, Communication, and Learning. The topics include: Yolo and RetinaNet Ensemble Transfer Learning Detector: Application in Pavement Distress;stock Market Prediction Performance Analysis by Using Machine Learning Regressor Techniques;nearest Neighbor and Decision Tree based Cloud Service QoS Classification;object Recognition Through Content based Feature Extraction and Classification of Sounds in IoT Environment;detection of Malicious Activity on Credit Cards Using Machine Learning;agent-based Modeling for Optimum Location Selection: Enhancing Day-Ahead Market Analysis for Microgrid Integration;a Comparative Approach for Skin Cancer Detection Using Artificial Bee Colony;a Hybrid Approach to Enhance Software Quality by Sentiment Analysis of Developer;unsupervised Sentiment Analysis of Amazon Fine Food Reviews Using Fuzzy Logic;sentiment Analysis of Crypto Currency Trading Applications in India Using Machine Learning;Graph Similarity Join (GSJ) Approach to Detect Near Duplicate Text Documents;three Factor Authentication Scheme for Telecare Medical Information System;an Effective Threshold based Technique for Retinal Image Blood Vessel Segmentation on Fundus Image Using Average and Gaussian Filters;substantiation System Using Facial Recognition and Principal Component Analysis;impact of Stress on Faculties’ Attitudes: A Study of Higher Education Institutes;dynamic Cross-Layer Communication Design for Multi-objective Optimization in Wireless Sensor networks;Implementation of Weighted parallel Interference Cancellation Technique for Multi User Detection in UWA Communication;analysis and Comparison of Firefly Algorithm for Measuring Convergence Rate in distributed Environment;A Comparative Study on Social Behavior of Tribes and Non Tribes in ITDA Paderu by Using network Science;raster Big Data processing Using Spark with GeoTrellis.

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Design and Implementation of SFC Monitoring and Alerting based on YANG Data Model

Design and Implementation of SFC Monitoring and Alerting Bas...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Yaru Ding Yintan Ai Xiaomin Zhang Hua Li Xianrong Wang Hongwei Ruan College of Computer Science Inner Mongolia University Hohhot China

Service Function Chaining (SFC) is an emerging technology that provides flexible service management and traffic control through an ordered set of service functions (SF). However, the variable deployment of service functions makes network architecture more complex and introduces uncertainty in the distribution of available resources. Additionally, the likelihood of service interruption rises with network configuration complexity. So, it is essential to monitor the SFC and alert the network operator when SFC problems are encountered. The SFC monitoring and alerting method based on the YANG data model is proposed. Each SF /SFF on the service function chaining is monitored, mainly for CPU utilization, memory utilization, bandwidth utilization, latency, average load and other SF metrics, and throughput and latency of SFF. Thresholds are set for each indicator. When a component exceeds the preset threshold, an abnormal information alarm will be triggered, and then the monitoring and alerting module will be modeled using the YANG data modeling language. Finally, the monitoring and alerting module in OpenDaylight (ODL) is implemented, and the experimental verification results are presented.

关键词： Measurement Uncertainty Service function chaining Traffic control network architecture Throughput Load management

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DockerGen: A Knowledge Graph based Approach for Software Containerization 45

DockerGen: A Knowledge Graph based Approach for Software Con...

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45th Annual International IEEE-Computer-Society Computers, Software, and Applications conference (COMPSAC)

作者： Ye, Hongjie Zhou, Jiahong Chen, Wei Zhu, Jiaxin Wu, Guoquan Wei, Jun Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China State Key Lab Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665424639

Docker is the de-facto container technology for software system deployment and delivery. A Dockerfile specifies how to containerize a system into a Docker image. However, creating a Dockerfile is not trivial since resolving the dependencies (e.g., third-party libraries) of diverse software requires comprehensive domain knowledge. In this paper, we propose DockerGen to containerize software packages automatically. DockerGen constructs a knowledge graph containing rich knowledge of building Docker images by analyzing nearly 220 thousand Dockerfiles. DockerGen exploits the knowledge graph to containerize the target software by creating a Dockerfile specifying the base image, dependencies, and the operation workflow. We evaluate DockerGen on 100 software packages of various categories. DockerGen achieves a 73% build success rate and a 59% configuration success rate. The experimental result indicates it is viable to automate software containerization based on a domain knowledge graph.

关键词： Docker Dockerfile containerization knowledge graph software package dependency

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Prediction of the Cyanobacteria Coverage in Time-series Images based on Convolutional Neural network 21

Prediction of the Cyanobacteria Coverage in Time-series Imag...

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4th International conference on Control and Computer Vision, ICCCV 2021

作者： Ye, Xiangyu Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology China

ISBN: (纸本)9781450390477

In recent years, the problem of lake eutrophication has become increasingly severe. The monitoring and control of cyanobacteria in lakes are of great significance. The information obtained by existing monitoring methods is relatively lagging, and it is impossible to monitor the sudden outbreak of cyanobacteria in time. Getting cyanobacteria information directly through camera images is a breakthrough. In this paper, after analyzing the characteristics of time series cyanobacteria images, we propose a block prediction scheme based on the CNN model. Experiments show that this method can quickly calculate the coverage of cyanobacteria in the monitoring image in a short time. It can also effectively distinguish cyanobacteria-rich water areas, which significantly facilitates water quality monitoring and cyanobacteria management. We can draw a chart of the changes in the coverage of cyanobacteria by analyzing multi-day time-series images. The chart helps us conduct a short-term water quality analysis to better deal with the outbreak of cyanobacteria. © 2021 ACM.

关键词： Lakes

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OWETC: Open world encrypted traffic classification based on semi-supervised class incremental learning

OWETC: Open world encrypted traffic classification based on ...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Meng Zhao Zhu Di Zhe Xia Jing Tian Jianwen Xiang Lin Jun Hubei Key Laboratory of Transportation of Internet of Things School of Computer Science and Artifical Intelligence Wuhan University of Technology Wuhan China China Electronic Product Reliability and Environmental Testing Research Institute Guangdong China Wuhan University of Technology Chongqing Research Institute Chongqing China

To address the dynamic changes in network environments and effectively tackle the open-world traffic classification challenge, we introduce a novel approach. Conventional traffic classification methods are constrained by their reliance on closed traffic sets and offline training scenarios, limiting their ability to recognize a fixed number of traffic classes and identify unknown traffic types. Our solution involves a traffic classification method founded on a combination of semi-supervised class-incremental learning and open-set recognition. To mitigate the issue of catastrophic forgetting in class-incremental learning, we leverage a substantial amount of external unlabeled traffic data. Specifically, we employ a class anchor clustering loss classifier with a discriminator to select external unlabeled traffic data as replay samples. This approach effectively mitigates the problem of catastrophic forgetting in class-incremental learning. Furthermore, the class anchor clustering loss is employed for incremental model training, enabling the model to identify unknown traffic data. We validate the performance of the model on encrypted traffic classical datasets, and the experimental results show that our method significantly outperforms traffic classification models based on other incremental learning methods. In particular, OWETC has a 3.28% performance improvement in average accuracy over multiple incremental sessions compared to the best baseline. Additionally, our enhanced open-set sampler displays a notable 17% reduction in false alarm rates with only a minor 1% accuracy degradation, surpassing the capabilities of existing open-set recognition methods.

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