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检索条件"任意字段=Proceedings - 24th International Conference on Distributed Computing Systems Workshops"

共 1489 条记录，以下是221-230 订阅

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Extension Principle for Descriptor-type systems with distributed Time-delay

Extension Principle for Descriptor-type Systems with Distrib...

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international conference on System theory, Control, and computing (ICSTCC)

作者： Altuğ İftar Eskişehir Technical University Eskişehir Turkey

the principle of extension is defined for linear time-invariant descriptor-type distributed-time-delay systems. Necessary and sufficient conditions for one system being an extension of another are also presented.

ISBN: (数字)9781728198095

ISBN: (纸本)9781728198101

关键词： Linear systems Null space Large-scale systems Space missions Delay systems Couplings

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Intelligent Monitoring of IoT Devices using Neural Networks

Intelligent Monitoring of IoT Devices using Neural Networks

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international conference on Intelligence in Next Generation Networks (ICIN)

作者： Ashima Chawla Pradeep Babu Trushnesh Gawande Erik Aumayr Paul Jacob Sheila Fallon Software Research Institute Athlone Institute of Technology Athlone Ireland Cloud Pipeline Engineering Workday Dublin Ireland Application Development Platform Ericsson Athlone Ireland Network Management Lab Ericsson Athlone Ireland

ISBN: (数字)9781728177052

ISBN: (纸本)9781728177069

the Internet of things (IoT) has seen expeditious growth in recent times with 7 billion connected devices in 2020, thus leading to the vital importance of real-time monitoring of IoT devices. through this paper, we demonstrate the idea of building a cloud-native application to monitor smart home devices. the application intends to provide valuable performance metrics from the perspective of end-users and react to anomalies in real-time. In this demo paper, we conduct the demonstration using Autoencoder (an unsupervised technique) based Deep Neural Networks (DNNs) to learn the normal operating conditions of power consumption of smart devices. When an anomaly is detected, the DNNs take proactive action and send appropriate commands back to the device. In addition, the users are provided with a real-time graphical representation of power consumption. this will help to save electricity on a domestic as well as industrial level. Finally, we discuss the future prospects of monitoring IoT devices.

关键词： Cloud computing Technological innovation Power demand Neural networks Real-time systems Internet of things Monitoring

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14th international conference on Risks and Security of Internet and systems, CRiSIS 2019

14th International Conference on Risks and Security of Inter...

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14th international conference on Risks and Security of Internet and systems, CRiSIS 2019

ISBN: (纸本)9783030415679

the proceedings contain 24 papers. the special focus in this conference is on Risks and Security of Internet and systems. the topics include: WPA3 Connection Deprivation Attacks;An Approach for thwarting Malicious Secret Channel: the Case of IP Record Route Option Header-Based Covert Channels;toward Ciphertext Policy Attribute Based Encryption Model: A Revocable Access Control Solution in Cloud computing;A Framework for GDPR Compliance in Big Data systems;"I do it because they do it": Social-Neutralisation in Information Security Practices of Saudi Medical Interns;unsupervised Machine Learning for Card Payment Fraud Detection;intrusion Detection Study and Enhancement Using Machine Learning;watch Out! Doxware on the Way..;CDISS-BEMOS: A New Color Document Image Steganography System Based on Beta Elliptic Modeling of the Online Signature;an Industrial Trial of an Approach to Identification and Modelling of Cybersecurity Risks in the Context of Digital Secondary Substations;A Graph Based Model for UAVs Group-Wide Collaboration Applied on an Anti-terrorism Scenario;modelling and Executing Time-Aware Processes in Trustless Blockchain Environment;Multi-scale Adaptive threshold for DDoS Detection;a Recommender System for User-Specific Vulnerability Scoring;distributed Detection System Using Wavelet Decomposition and Chi-Square Test;continuous Risk Management for Industrial IoT: A Methodological View;systematic Asset Identification and Modeling During Requirements Engineering;inference Control in distributed Environment: A Comparison Study;MAPPER: Mapping Application Description to Permissions;Delegation of Computation Using FV Cryptosystem;Hardware Optimization on FPGA for the Modular Multiplication in the AMNS Representation;a Semantic Framework with Humans in the Loop for Vulnerability-Assessment in Cyber-Physical Production systems.

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proceedings - 24th IEEE international conference on High Performance computing workshops, HiPCW 2017

Proceedings - 24th IEEE International Conference on High Per...

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24th IEEE international conference on High Performance computing workshops, HiPCW 2017

ISBN: (纸本)9781538614396

the proceedings contain 9 papers. the topics discussed include: software troubleshooting using machine learning;the effect of non volatile memory on a distributed storage system;thermal profiling and modeling of Hadoop clusters using BigData applications;parallel LDA with over-decomposition;characterization of vertex-centric breadth first search for lattice graphs;a perspective on the future of CFD and analysis;high-resolution modeling of multiscale atmospheric convection;numerical simulation of aerospace applications using overset mesh;and performance optimization of OpenFOAM∗ on clusters of Intel® Xeon Phi™ processors.

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An optimization method for resource allocation in fog computing 13

An optimization method for resource allocation in fog comput...

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2020 IEEE Congress on Cybermatics: 13th IEEE international conferences on Internet of things, ithings 2020, 16th IEEE international conference on Green computing and Communications, GreenCom 2020, 13th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th IEEE international conference on Smart Data, SmartData 2020

作者： Yin, Chao Li, Tongfang Qu, Xiaoping Yuan, Sihao School of Information Science and Technology Jiujiang University Jiujiang332005 China

ISBN: (纸本)9781728176475

With the rapid development of the network, a huge amount of data will be generated. Existing technologies usually use cloud computing to process data. However, when all the data of users need to be transferred to the cloud for processing, it will cause long transmission delay due to the distance between users and cloud computing data center. Fog computing connects network edge devices downward and cloud data center upward, which greatly shortens the transmission distance of user data. Fog computing is an effective way to solve the problems of high delay, bandwidth bottleneck and security. In this paper, we have developed an optimization method, which establish an optimization problem with minimum average task delay as optimization objective and various system resources. By variable substitution, the nonconvex optimization problem is equivalent to a new optimization problem whose objective is nonconvex and all constraints are convex. In order to solve the global optimal solution of the nonconvex optimization problem, a new lower bound is designed by relaxing the objective to a lower bound quadratic function. We have evaluated our system by simulation test. the results verify the effectiveness of the optimization method. © 2020 IEEE.

关键词： Resource allocation

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SYN Flood Attack Detection and Mitigation using Machine Learning Traffic Classification and Programmable Data Plane Filtering

SYN Flood Attack Detection and Mitigation using Machine Lear...

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international conference on Intelligence in Next Generation Networks (ICIN)

作者： Marinos Dimolianis Adam Pavlidis Vasilis Maglaris Network Management & Optimal Design Laboratory (NETMODE) School of Electrical & Computer Engineering National Technical University of Athens (NTUA) Greece

ISBN: (数字)9781728177052

ISBN: (纸本)9781728177069

distributed Denial of Service (DDoS) attacks are widely used by malicious actors to disrupt network infrastructures/services. A common attack is TCP SYN Flood that attempts to exhaust memory and processing resources. Typical mitigation mechanisms, i.e. SYN cookies require significant processing resources and generate large rates of backscatter traffic to block them. In this paper, we propose a detection and mitigation schema that focuses on generating and optimizing signature-based rules. To that end, network traffic is monitored and appropriate packet-level data are processed to form signatures i.e. unique combinations of packet field values. these are fed to machine learning models that classify them to malicious/benign. Malicious signatures corresponding to specific destinations identify potential victims. TCP traffic to victims is redirected to high-performance programmable XDPenabled firewalls that filter off ending traffic according to signatures classified as malicious. To enhance mitigation performance malicious signatures are subjected to a reduction process, formulated as a multi-objective optimization problem. Minimization objectives are (i) the number of malicious signatures and (ii) collateral damage on benign traffic. We evaluate our approach in terms of detection accuracy and packet filtering performance employing traces from production environments and high rate generated attack traffic. We showcase that our approach achieves high detection accuracy, significantly reduces the number of filtering rules and outperforms the SYN cookies mechanism in high-speed traffic scenarios.

关键词： Filtering Firewalls (computing) Machine learning Telecommunication traffic Floods Computer crime Optimization

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Cyber-Physical Production System Self-Description-Based Data Access Layer 24

Cyber-Physical Production System Self-Description-Based Data...

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24th IEEE international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Stock, Daniel Schel, Daniel Bauernhansl, thomas Fraunhofer Inst Mfg Engn & Automat IPA Competence Ctr DigITools Mfg Stuttgart Germany

ISBN: (纸本)9781728103037

Cyber-Physical Production systems (CPPS) are Cyber-Physical systems (CPS), which are applied in manufacturing environments to carry out production related tasks, thus turning them into smart manufacturing environments. they are the evolution of combined regular manufacturing equipment, embedded computing devices and distributed computing functionality, which has been sparked by technical advances in information and communication technology (ICT) and progressively networked production. One of the properties of CPS is the ability to be composed of other CPS and the ability to encapsulate so-called self-x capabilities. their networked nature enables access to large amounts of data, which can be used to extract information and knowledge about machines, processes, and the manufacturing environment. this paper discusses approaches how data access, storage, and information extraction is currently being handled. We propose how these approaches can be extended and how an information-oriented system and infrastructure for CPPS can be constructed to create a Cyber-Physical Data Access Layer which is based on the self-description capability of CPPS components.

关键词： Cyber-Physical Production System self-x middleware information systems Data Warehouse Data Lake digital shadow

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distributed scheduling in Kubernetes based on MAS for Fog-in-the-loop applications 24

Distributed scheduling in Kubernetes based on MAS for Fog-in...

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24th IEEE international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Casquero, Oskar Armentia, Aintzane Sarachaga, Isabel Perez, Federico Orive, Dario Marcos, Marga Univ Basque Country EIB Bilbao Dept Automat Control & Syst Engn Bilbao Spain

ISBN: (纸本)9781728103037

With cloud computing gaining momentum in industrial environments, the next step seems to move the computing infrastructure closer to the devices that collect the data at plant level. Fog computing is a paradigm that takes advantage of the fast response times of working relatively close to the plant, and the storage, processing and availability features of the cloud. Fog computing can be used to improve the controllability of automation processes by introducing a higher-level control loop: Fog-in-the-loop (FIL). FIL allows capturing data from the plant, processing it to extract information and feedback actions to the plant based on the processing results. therefore, FIL applications are context-aware applications that require the deployment of distributed components and dynamic reconfiguration. In this context, we are involved in a project that aims at integrating a model-based, multipurpose Multi-Agent System (MAS) platform with a containerized application orchestrator to meet the requirements of Fog-in-the-loop applications. Specifically, this paper describes a custom scheduler for Kubernetes orchestrator that distributes the scheduling task among the processing nodes by means of the MAS. this new scheduling approach proved to be faster than the centralized scheduling approach used by the default scheduler of K8s.

关键词： Orchestrator Kubernetes K8s Multi-Agent systems MAS JADE Fog-in-the-Loop

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Consistency of the Fittest: Towards Dynamic Staleness Control for Edge Data Analytics 24th

Consistency of the Fittest: Towards Dynamic Staleness Contro...

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24th international European conference on Parallel and distributed computing (Euro-Par)

作者： Aral, Atakan Brandic, Ivona Vienna Univ Technol Inst Informat Syst Engn Vienna Austria

ISBN: (纸本)9783030105495;9783030105488

A critical challenge for data stream processing at the edge of the network is the consistency of the machine learning models in distributed worker nodes. Especially in the case of non-stationary streams, which exhibit high degree of data set shift, mismanagement of models poses the risks of suboptimal accuracy due to staleness and ignored data. In this work, we analyze model consistency challenges of distributed online machine learning scenario and present preliminary solutions for synchronizing model updates. Additionally, we propose metrics for measuring the level and speed of data set shift.

关键词： Edge computing Data analytics Consistency Staleness

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Adaptive Stream-based Shifting Bottleneck Detection in IoT-based computing Architectures 24

Adaptive Stream-based Shifting Bottleneck Detection in IoT-b...

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24th IEEE international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Najdataei, Hannaneh Subramaniyan, Mukund Gulisano, Vincenzo Skoogh, Anders Papatriantafilou, Marina Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781728103037

Cloud computing is revolutionizing the backbone of data analysis applications, including industrial ones. One of its main pillars is the separation of the logic with which data is accessed (e.g., to study the efficiency of a manufacturing system) from the actual hardware (e.g., server) that maintains and analyses the data. Large distributed cyber-physical systems enabled by, among other technologies, the Internet of things (IoT), made nonetheless clear that "what to do" with the data and "where to do it" are not disjoint problems;i.e., cloud computing on its own is not enough. Fog and edge computing have emerged as complementary options, to distribute the analysis, helping with challenges by means of close-to-the-source data analysis. We show for a key problem for industrial processes, that of shifting bottleneck detection, how to take advantage of such multi-tier computing architectures, to perform continuous and configurable analysis of data from Manufacturing Execution systems. We propose a processing framework, STRATUM, and an algorithm, AMBLE, for continuous, data stream processing. STRATUM seamlessly distributes and parallelizes the processing across the tiers and AMBLE guarantees consistent analysis in spite of timing fluctuations, which are commonly introduced due to e.g. the communication system;it also achieves efficiency through appropriate data structures for in-memory processing. the experimental study on a real-world dataset, taken from a production line over two years and including 8.5 million entries, shows the benefits of the proposed solution in enabling configurable and efficient analysis.

关键词： Production systems Microsoft Windows Computer architecture Data analysis Data models throughput

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