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共 2734 条记录，以下是141-150 订阅

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A Proactive Cloud Application Auto-Scaler using Reinforcement Learning 15

A Proactive Cloud Application Auto-Scaler using Reinforcemen...

引用

IEEE/ACM 15th international conference on Utility and Cloud computing (UCC) / 9th international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Heimerson, Albin Eker, Johan Arzen, Karl-Erik Lund Univ Dept Automat Control Lund Sweden Ericsson Res Lund Sweden

ISBN: (纸本)9781665460873

this work explores the use of reinforcement learning to design a proactive cloud resource auto-scaler that is able to predict usage across a distributed microservice application. the focus is on serving time-sensitive workloads, e.g., industrial automation, connected XR/VR (eXtended Reality/Virtual Reality), etc., where each job has a deadline and there is some cost associated with missing a deadline. A simple workload model, as well as a microservice application model, is presented. A reinforcement learning agent is trained to identify workloads and predict needed utilization for identified service chains. the results are compared to standard purely reactive techniques.

关键词： Cloud computing Reinforcement Learning Auto-Scaling Microservice Proactive Control

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An Evaluation of Modeling Options for Cloud-native Application Architectures to Enable Quality Investigations 15

An Evaluation of Modeling Options for Cloud-native Applicati...

引用

IEEE/ACM 15th international conference on Utility and Cloud computing (UCC) / 9th international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Duerr, Karolin Lichtenthaeler, Robin Univ Bamberg Distributed Syst Grp Bamberg Germany

ISBN: (纸本)9781665460873

Cloud-native as a style to build web applications embraces the potential of cloud computing. A possibility to evaluate software architectures of cloud-native applications according to quality aspects would be beneficial to developers, but is challenging due to the thematic breadth of cloud-native ranging from design to operation of applications. this work therefore evaluates existing architecture description languages according to their suitability for representing those architectural aspects specific to the cloud-native style. the evaluation is driven by a recently proposed quality model for which a suitable architectural modeling option is needed. We find that several existing languages are suitable as a basis for implementing an extension specific to the quality model. We choose TOSCA as an established standard to implement such an extension with corresponding tooling support.

关键词： cloud-native modeling architecture description language quality evaluation

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Kua: A distributed Object Store over Named Data networking 9

Kua: A Distributed Object Store over Named Data Networking

引用

22nd international conference on Information Centric networking

作者： Patil, Varun Desai, Hemil Zhang, Lixia Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781450392570

Applications such as machine learning training systems or log collection generate and consume large amounts of data. Object storage systems provide a simple abstraction to store and access such large datasets. these datasets are typically larger than the capacities of individual storage servers, and require fault tolerance through replication. In this paper, we present Kua, a distributed object storage system built over Named Data networking (NDN). the data-centric nature of NDN helps Kua maintain a simple design while catering to requirements of storing large objects, providing fault tolerance, low latency and strong consistency guarantees, along with data-centric security. Our prototype Kua implementation provides easy-to-use primitives to let applications store and access data securely, and our initial evaluation suggests that Kua can leverage NDN's capabilities of multicast data delivery and in-network caching to achieve higher efficiency than existing object storage systems.

关键词： Named Data networking Object Store distributed Storage

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Machine Learning (ML) assisted Edge security framework on FPGAs 9

Machine Learning (ML) assisted Edge security framework on FP...

引用

9th international conference on Computer and Communication Engineering, ICCCE 2023

作者： Sheikh, Abdul Manan Islam, Md. Rafiqul Habaebi, Mohamed Hadi Zabidi, Suriza Ahmad Najeeb, Athaur Rahman Bin Basahel, Ahmed International Islamic University Malaysia Department of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350325218

Edge computing (EC) is an act of bringing computational and storage capability near data sources. It helps to reduce response times and bandwidth requirements. However, the rapid proliferation of edge devices has expanded the attack surface and opportunity for adversaries to penetrate corporate networks. the limited computational abilities of edge devices and the heterogeneous nature of communication protocols further increase the security challenges of EC. Also, the trustworthiness of hardware devices is challenged due to security and privacy threats like trojan insertion, IP cloning, and hardware counterfeits. the application of Machine Language (ML) models in the edge computing paradigm creates a distributed intelligence architecture. Also, Field Programmable Gate Arrays (FPGAs) can exploit Physical Unclonable Functions (PUFs) characteristics to generate and store authentication keys. the PUF structure deployed with ML models in the edge layer can learn its complex input-output mapping from the Challenge and Response pairs (CRPs) to identify the suspicious and unknown responses. this article discusses the security and privacy issues in various layers of the EC architecture and proposes intrusion detection systems through the integration of FPGA-based edge sever and ML models. A PUF-assisted ML framework of the intrusion detection system is proposed to authenticate and detect potential attacks on the network. © 2023 IEEE.

关键词： Hardware security

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Information-Centric Service Mesh for Autonomous In-network computing 9

Information-Centric Service Mesh for Autonomous In-network C...

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22nd international conference on Information Centric networking

作者： Kanai, Kenji Tsuda, Toshitaka Nakazato, Hidenori Katto, Jiro Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan Waseda Univ Dept Comp Sci & Comm Engn Tokyo Japan

ISBN: (纸本)9781450392570

In this paper, we introduce an information-centric service mesh for autonomous in-network computing. the information-centric service mesh offers service-centric networking, not connectivity-based networking provided by the current container orchestration, like Kubernetes (K8s). We implement the information-centric service mesh by adopting an ambassador container style and using Cefore and Cefpyco which is the CCNx software implementation developed by NICT in Japan. For the proof-of-concept test, we demonstrate the end-to-end delay of more complex service chain models (up to 17 nodes) on our K8s cluster.

关键词： ICN Service Function Chaining Autonomous Network

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A Roadmap of Social networking Drivers and Challenges in the Era of Digital Banking 9

A Roadmap of Social Networking Drivers and Challenges in the...

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9th international conference on Social Networks Analysis, Management and Security, SNAMS 2022

作者： Razmak, Jamil Farhan, Wejdan El Refae, Ghaleb A. College of Business Al Ain University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350320480

We conducted a literature review to draw a short roadmap of both the drivers and challenges related to social networking in the banking industry. We extracted from the literature simple models based on drivers and challenges, on which banking policy makers, technologists, and researchers can build in the future. Our findings show that various trends in social networking drivers and challenges can have either a negative or a positive impact on the banking industry. Banks must use different tools, such as force-field analysis, to compare between these drivers and challenges. the use of modern technologies, such as cloud computing and AI aligning, along with managerial actions such as hiring specialized technologists and adopting marketing strategies through social networking, will help the banking industry maximize its opportunities and minimize its challenges. © 2022 IEEE.

关键词： Marketing

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Enabling Intelligence Inclusiveness in Edge to Cloud Continuum: Challenges and Opportunities 9

Enabling Intelligence Inclusiveness in Edge to Cloud Continu...

引用

9th IEEE international conference on Network Softwarization (IEEE NetSoft) - Boosting Future Networks through Advanced Softwarization

作者： Palomares, Javier Coronado, Estefania Cervello-Pastor, Cristina Siddiqui, Shuaib I2CAT Fdn Barcelona Spain Univ Castilla La Mancha High Performance Networks & Architectures Albacete Spain Univ Politecn Cataluna Dept Network Engn Barcelona Spain

ISBN: (纸本)9798350399806

Edge to Cloud Continuum is a concept that integrates cloud computing and cellular networks that has been gaining popularity due to its potential to provide a seamless user experience and address the challenges of managing complex multi-domain networks involving massive IoT devices. Enabling intelligence in the Edge to Cloud Continuum can further enhance its capabilities, offering benefits such as reduced latency, improved scalability, enhanced resource utilization, and increased context awareness. this paper provides insights into the opportunities and challenges of enabling intelligence in Edge to Cloud Continuum, highlighting the potential of this technology. this study presents a comprehensive review of the existing literature on enabling intelligence in Edge to Cloud Continuum, to reach the research questions that will construct the PhD. Various tools and technologies that can be used to integrate intelligence into the Edge to Cloud Continuum system were explored and analyzed. In addition, this study provides a detailed work plan for the upcoming months of the project.

关键词： Edge to Cloud Continuum Multi-domain Enabling Intelligence distributed Systems 6G

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CrossFit: Fine-grained Benchmarking of Serverless Application Performance across Cloud Providers 15

CrossFit: Fine-grained Benchmarking of Serverless Applicatio...

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IEEE/ACM 15th international conference on Utility and Cloud computing (UCC) / 9th international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Scheuner, Joel Deng, Rui Steghofer, Jan-Philipp Leitner, Philipp Univ Gothenburg Chalmers Dept Comp Sci & Engn Gothenburg Sweden

ISBN: (纸本)9781665460873

Serverless computing emerged as a promising cloud computing paradigm for deploying cloud-native applications but raises new performance challenges. Existing performance evaluation studies focus on micro-benchmarking to measure an individual aspect of serverless functions, such as CPU speed, but lack an in-depth analysis of differences in application performance across cloud providers. this paper presents CrossFit, an approach for detailed and fair cross-provider performance benchmarking of serverless applications based on a providerindependent tracing model. Our case study demonstrates how detailed distributed tracing enables drill-down analysis to explain performance differences between two leading cloud providers, AWS and Azure. the results for an asynchronous application show that trigger time contributes most delay to the end-to-end latency and explains the main performance difference between cloud providers. Our results further reveal how increasing and bursty workloads affect performance stability, median latency, and tail latency.

关键词： serverless FaaS distributed tracing observability performance benchmarking

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9th China National conference on Big Data and Social computing, BDSC 2024

9th China National Conference on Big Data and Social Computi...

引用

9th China National conference on Big Data and Social computing, BDSC 2024

ISBN: (纸本)9789819758029

the proceedings contain 28 papers. the special focus in this conference is on Big Data and Social computing. the topics include: Network Analysis Reveals Regional Disparity in COVID-19 Policymaking;exploring Urban Spatial-temporal Patterns via Large-scale Vehicle Travel Data: the Role of Geographical Attributes and Traveler Characteristics;mapping Gridded Wealth Index Using Open Geospatial Data in Zambia;bidirectional Multi-grain Graph Convolution Network for Origin-Destination Demand Prediction;the Prospects of Multi-modal Pre-trained Models in Epidemic Forecasting;deep Reinforcement Learning Based Dynamic Bus Timetable Scheduling with Bidirectional Constraints;modeling Knowledge Spillover Effects in High-Speed Rail Development: A Discrete Simulation Approach Using Cellular Automata;Educators’ networking Interacts with Digital Competence Heterogeneity to Enhance the Implementation of AIEd: A Mixed‐Methods Study;intelligent Fatigue Driving Detection Method Based on Fusion of Smartphone and Smartwatch Data;SCPM-R+ER: A R+ER-based Algorithm for Mining Spatial Co-location Patterns;extracting Spatial High Utility Co-location Patterns Based on Fuzzy Feature Clusters;incremental Network Traffic Category Models Based on Hybrid Learning Strategies;Modeling the BGP Prefix Hijack via Pollution and Recovery Processes;a Weakly Supervised Method for Encrypted Traffic Classification in the Dark Web;rumor Detection Based on Conflict and Bot Features;a Study of Digital Nomad Culture and Local Social Practices–Based on Fieldwork Research in a Certain Area of Southwest China;analysis of the Relationship Between Temperature and Insomnia Based on Social Media Text;do Gender Role Attitudes Affect Fertility Intentions ?—Evidence from international Data;Dynamic Shifts: the Rise of Unicorns in the AI Ecosystem;measurement and Analysis of China’s Fashion Events on Social Media: A Study of Shanghai Fashion Week;enhanced Product Embedding with Sememe for Product Search;temporal Knowledge Grap

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Research on Cloud computing-Based Editorial and Publishing Collaboration Platform with Intelligent Scheduling Optimization 24

Research on Cloud Computing-Based Editorial and Publishing C...

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9th international conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Zhang, Hong Shi, Jing Yan, Jing Liu, Ting Binzhou Polytechnic China

ISBN: (纸本)9798400718137

To address the challenges of limited computing resources, complex data processing, and high security requirements in the editorial and publishing process, this paper proposes a collaborative platform that integrates cloud computing and intelligent scheduling optimization technologies. the platform leverages the distributed architecture and elastic resource management of cloud computing to achieve efficient collaboration in the editorial and publishing workflow, while enhancing task allocation efficiency through intelligent scheduling algorithms. the platform utilizes Principal Component Analysis (PCA) and Crow Search Algorithm (CSA) for intelligent task scheduling optimization, ensuring stable system operation in multi-task scenarios. the specific approach includes dimensionality reduction of user data to eliminate redundant information and constructing a fitness function based on the task model to achieve dynamic updates of the optimal scheduling scheme. Experimental results demonstrate that the proposed scheduling method performs excellently in terms of task execution time, cost, and service quality. this research provides a cloud computing platform with intelligent scheduling optimization for digital editorial and publishing, offering technical support for the development of information management. © 2024 Copyright held by the owner/author(s).

关键词： Cloud platforms

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