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Design of Personalized Recommendation System for Teaching Resources Based on cloud edge computing

Procedia Computer Science 2024年 243卷 826-833页

作者： Xuemin Chen Guangdong Innovative Technical College Dongguan 523960 China

With the continuous development of cloud computing and edge computing technologies, the education sector is gradually applying these technologies to enhance the management and utilization efficiency of teaching resources. Therefore, to address the issue of information overload mentioned above, it is necessary to establish a personalized recommendation system based on user needs, preferences, and other information, recommending products, information, and resources that may be of interest to users. This can not only save users search time, but also alleviate the problem of information overload to some extent. On this basis, this article discussed a new content oriented recommendation method: constructing a user interest feature vector resource association matching model, and analyzing it to achieve recommendation of similar resources. The experimental results showed that the MAE (Mean Absolute Error) value of personalized recommendation based on CF (Collaborative Filtering) algorithm was below 0.8, which was smaller than other algorithms, indicating high accuracy of recommendation based on CF algorithm.

关键词： Teaching Resources Personalized Recommendation System cloud edge computing Collaborative Filtering Algorithm

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Blockchain-enabled heterogeneous 6G supported secure vehicular management system over cloud edge computing

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INTERNET OF THINGS 2024年 25卷

作者： Razaque, Abdul Khan, Meenhoon Yoo, Joon Alotaibi, Aziz Alshammari, Majid Almiani, Muder Satbayev Univ Dept Cyber Secur Informat Proc & Storage Alma Ata Kazakhstan Bahria Univ Dept Comp Sci Islamabad Pakistan Gachon Univ Sch Comp Seongnam South Korea Taif Univ Comp & Informat Technol Coll Taif Saudi Arabia Gulf Univ Sci & Technol Management Informat Syst MIS Mubarak Al Abdullah Kuwait

The emergence of 6G networks increases both the suitability and the speed of Internet -of -Things (IoT) devices within vehicular communication systems (VCSs). Wireless capabilities can be improved using a 6G network to successfully manage IoT devices in VCSs. 6G networks can be employed to reduce cyberattacks due to their unclaimed and unused operating frequencies. However, 6G networks could be vulnerable to cyberattacks due to their flexibility. The issues of 6G networks can adequately be addressed using blockchain technology. If a smart vehicular system uses IoT, then faster response times, substantial power reductions, and security for possible accident avoidance and safety provision can be achieved. Therefore, an energy -efficient consortium -based blockchain-enabled heterogeneous (EBH) 6G network for IoT devices can provide the best platform for secure management vehicular systems. In this article, we introduce a perspective architecture for the IoT-enabled secure vehicular system using a blockchainenabled heterogeneous 6G network over cloud edge computing. The heterogeneous support of a 6G network is discussed, which is highly effective for smart vehicular management systems. The primary goal of the article is to motivate the community and researchers to use multidisciplinary and cross -cutting technologies integratively.

关键词： IoT Blockchain technology 6G network Privacy-preservation Vehicular management system Heterogeneous network cloud edge computing

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6G wireless communication assisted security management using cloud edge computing

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EXPERT SYSTEMS 2023年第4期40卷 e13061-e13061页

作者： Kamruzzaman, M. M. Jouf Univ Dept Comp Sci Coll Comp & Informat Sci Sakaka Saudi Arabia

Security management is the process of identifying a company's assets (such as people, buildings, equipment, systems, and information assets) and then developing, documenting, and implementing policies and procedures to secure those assets. Meanwhile, artificial intelligence (AI) applications are flourishing thanks to advances in deep learning and numerous hardware architecture improvements based on cloud edge computing (CEC) issues are associated with the Internet of Things (IoT), including inadequate security measures, user ignorance, and the dreaded active monitoring. Therefore, a 6G wireless communication-assisted security management using artificial intelligence (WC-SM-AI) has been introduced to enhance security. The energy-efficient 6G real-time communication framework and the enhanced deep neural network security module are key components of this architecture. The first module optimizes network lifespan and spectral efficiency while reducing energy consumption and latency. Another module offers a more secure network connection while increasing privacy, data integrity, and access. For this reason, this article discusses how AI can strengthen the security of 6G networks while promoting strategy problems and solutions.

关键词： 6G wireless communications artificial intelligence cloud edge computing security management

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A cloud edge computing method for economic dispatch of active distribution network with multi-microgrids

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第PartA期214卷

作者： Li, Xueping Wang, Jie Lu, Zhigang Cai, Yao Yanshan Univ Key Lab Power Elect Energy Conservat & Motor Dr H Qinhuangdao Peoples R China

In view of the risks and challenges of privacy data leakage and the communication burden in the traditional economic dispatch for active distribution network with multi-microgrids, this paper proposes a cloud edge computing method for economic dispatch of active distribution network with multi-microgrids. In this method, the cloud server is responsible for the calculation of active distribution network, and each edge server is in charge of the calculation of its own microgrid. The multi-agent deep reinforcement learning is employed to realize cloud edge collaborative computing, where each edge server and cloud server corresponds to an agent. Through case analysis, the reliability of the cloud edge computing method is confirmed. The simulation results show that the proposed method can provide a high-quality solution for economic dispatch of active distribution network with multi-microgrids on the premise of protecting data privacy and reducing the communication burden.

关键词： cloud edge computing Multi-agent deep reinforcement learning Economic dispatch Active distribution F

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Collaborative Resource Sharing Game Based cloud-edge Offload computing Orchestration Scheme

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IEEE ACCESS 2022年 10卷 74523-74532页

作者： Kim, Sungwook Sogang Univ Dept Comp Sci Seoul 04107 South Korea

In the foreseeable future, the rapid growth of mobile communications and cloud systems has substantially promoted edge computing paradigm. Although edge computing technology has been attracting much interest, most current research is application-specific without considering a control perspective of cloud providers that provides general-purpose edge services. In this study, we present a new cloud-edge computing orchestration scheme that integrates the different resource sharing problems to maximize benefits for offloading computing services. Specifically, we focus on two cooperative game solutions -Sim bargaining solution and interval Banzhaf Value- to effectively share the computing, communication and cache resources in the cloud-edge system platform. In the proposed scheme, we formalize two different cooperative games, and they work together and act cooperatively to satisfy contradictory requirements. Under widely different service request situations, we aim at accelerating an efficient resource orchestration through dynamic workload balancing. The primary goal of our cooperative game approach is to find optimal control decisions toward the appropriate system operation. Finally, we evaluate the proposed scheme in terms of several performance criteria from the numerical simulations. Experimental results show that we can achieve a mutually desirable solution with a good system performance balance while enhancing the resource efficiency better than the existing cloud-edge control protocols.

关键词： Games Servers Resource management cloud computing Internet of Things Quality of service Task analysis cloud edge computing Internet of Thing resource sharing problems Sim bargaining solution interval Banzhaf value

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A computing Resource Allocation Optimization Strategy for Massive Internet of Health Things Devices Considering Privacy Protection in cloud edge computing Environment

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JOURNAL OF GRID computing 2021年第2期19卷 17-17页

作者： Wang, Jianxi Wang, Liutao Pingdingshan Univ Sch Comp Pingdingshan 467000 Henan Peoples R China

With the development of 5G and the explosive growth of massive Internet of Health Things devices, existing computing resource allocation strategies have many problems such as long delay and poor security performance. Therefore, this paper proposes an optimization strategy for computing resource allocation of massive IoHT devices considering privacy protection in cloud edge computing environment. Firstly, a 5G heterogeneous cloud edge computing network is constructed. Besides, according to network status, the computing requirements of devices are allocated to local execution or edge computing. The computing delay, communication and computing resource allocation of edge servers are modeled accordingly. Then, taking the delay and energy consumption of network computing resource allocation as optimization goal, the priority of subtasks is sorted to realize the optimal allocation of computing resources. Finally, a protection model for instant messaging privacy data is designed by considering the risk of large-scale privacy data leakage in IoHT. Terminal devices under the same local area network are connected to edge servers by Socket without cloud server forwarding, which improves the security performance of privacy data. Experiment and demonstrate the performance of our proposed strategy on MATLAB simulation platform. The results show that the increase of edge computing server will affect the CPU proportion. Moreover, compared with other strategies, the number of users, the number of edge computing servers, the computing capacity of devices and the task arrival rate have the least impact on the average delay of proposed strategy, which effectively improves the performance of allocation strategy.

关键词： cloud edge computing Privacy protection Internet of health things computing resource allocation Priority Lightweight security

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Privacy-Preserving Fine-Grained Data Sharing With Dynamic Service for the cloud-edge IoT

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE computing 2025年第2期22卷 1329-1346页

作者： Sun, Jianfei Bao, Yangyang Qiu, Weidong Lu, Rongxing Zhang, Songnian Guan, Yunguo Cheng, Xiaochun Singapore Management Univ Sch Comp & Informat Syst Singapore 188065 Singapore Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200240 Peoples R China Peoples Bank China Credit Reference Ctr Shanghai 201201 Peoples R China Shanghai Jiao Tong Univ Sch Cyber Sci & Engn Shanghai 200240 Peoples R China Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada Swansea Univ Dept Comp Sci Swansea SA1 8EN Wales

The cloud-edge computing model has been expected to play a revolutionary role in promoting the quality of future generation large-scale Internet of Things (IoT) services. However, security and privacy in data sharing remain crucial issues hindering the success of cloud-edge IoT services. While some solutions based on attribute-based encryption (ABE) have been proposed to address these issues, they still face practical challenges such as attribute privacy leakage, resource-constrained devices, dynamic user groups, inflexible and inefficient service response. To address these challenges, this paper proposes a privacy-preserving fine-grained data sharing scheme with dynamic service (PF2DS), which implements access control by calculating the inner product between an attribute vector and an access vector. PF2DS is also capable of providing dynamic user group services through an efficient and indirect user revocation mechanism that periodically updates the key-embedded leaf nodes. Building on PF2DS, edge-assisted PF2DS (EPF2DS) delegates most of the operations to the edge device, which facilitates the performance of resource-constrained IoT devices. EPF2DS also supports efficient and asynchronous keyword search over the ciphertexts stored in the cloud. We demonstrate the security by the rigorous security proof. Both theoretical comparisons and experimental simulations demonstrate the practicality and superiority of our schemes over existing works.

关键词： Internet of Things Data privacy Security Privacy cloud computing Vectors Encryption Attribute-based access control cloud edge computing keyword search

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SPACE4AI-D: A Design-Time Tool for AI Applications Resource Selection in computing Continua

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IEEE TRANSACTIONS ON SERVICES computing 2024年第6期17卷 4324-4339页

作者： Sedghani, Hamta Filippini, Federica Ardagna, Danilo Politecn Milan I-20133 Milan Italy

Nowadays, Artificial Intelligence (AI) applications are becoming increasingly popular in a wide range of industries, mainly thanks to Deep Neural Networks (DNNs) that needs powerful resources. cloud computing is a promising approach to serve AI applications thanks to its high processing power, but this sometimes results in an unacceptable latency because of long-distance communication. Vice versa, edge computing is close to where data are generated and therefore it is becoming crucial for their timely, flexible, and secure management. Given the more distributed nature of the edge and the heterogeneity of its resources, efficient component placement and resource allocation approaches become critical in orchestrating the application execution. In this paper, we formulate the resource selection and AI applications component placement problem in a computing continuum as a Mixed Integer Non-Linear Problem (MINLP), and we propose a design-time tool for its efficient solution. We first propose a Random Greedy algorithm to minimize the cost of the placement while guaranteeing some response time performance constraints. Then, we develop some heuristic methods such as Local Search, Tabu Search, Simulated Annealing and Genetic Algorithms, to improve the initial solutions provided by the Random Greedy. To evaluate our proposed approach, we designed an extensive experimental campaign, comparing the heuristics methods with one another and then the best heuristic against Best Cost Performance Constraint (BCPC) algorithm, a state-of-the-art approach. The results demonstrate that our proposed approach finds lower-cost solution than BCPC (27.6% on average) under the same time limit in large-scale systems. Finally, during the validation in a real edge system including FaaS resources our approach finds the globally optimal solution, suffering a deviation of around 12% between actual and predicted costs.

关键词： Artificial intelligence cloud computing Time factors Quality of service Costs Containers Partitioning algorithms Runtime Heuristic algorithms Delays AI services component placement cloud edge computing resource selection

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Practical cloud-edge Scheduling for Large-Scale Crowdsourced Live Streaming

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第7期34卷 2055-2071页

作者： Zhang, Ruixiao Yang, Changpeng Wang, Xiaochan Huang, Tianchi Wu, Chenglei Liu, Jiangchuan Sun, Lifeng Tsinghua Univ Dept Comp Sci & Technol Beijing 100190 Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Tsinghua Univ Dept Comp Sci & Technol BNRist Beijing 100190 Peoples R China Tsinghua Univ Minist Educ Dept Comp Sci & Technol Key Lab Pervas Comp Beijing 100064 Peoples R China Dept Innovat Algorithm Huawei Cloud Minist Educ Key Lab Pervas Comp Shenzhen 518129 Peoples R China Simon Fraser Univ Sch Comp Sci Vancouver BC V5A 1S6 Canada

Even though conventional wisdom claims that in order to improve viewer engagement, the cloud-edge providers should serve the viewers with the nearest edge nodes, however, we show that doing this for crowdsourced live streaming (CLS) services can introduce significant costs inefficiency. In this paper, we first carry out large-scale measurement analysis by using the real-world service data from Huawei cloud, a representative cloud-edge provider in China. We observe that the massive number of channels has proposed great burdens to the operating expenditure of the cloud-edge providers, and most importantly, unbalanced viewer distribution makes the edge nodes suffer significant costs inefficiency. To tackle the above concerns, we propose AggCast, a novel CLS scheduling framework to optimize the edge node utilization for the cloud-edge provider. The core idea of AggCast is to aggregate some viewers that are initially scattered on different regions, and assign them to fewer pre-selected nodes, thereby reducing bandwidth costs. In particular, by integrating the useful insights obtained from our large-scale measurement, AggCast can not only ensure that quality of experience (QoS) does not suffer degradation, but also satisfy the systematic requirements of CLS services. AggCast has been A/B tested and fully deployed. The online and trace-driven experiments show that, compared to the most prevalent method, AggCast saves over 16.3% back-to-source (BTS) bandwidth costs while significantly improving QoS (startup latency, stall frequency and stall time are reduced over 12.3%, 4.57% and 3.91%, respectively).

关键词： Costs Quality of service Bandwidth Servers Optimization Computer science Systematics cloud edge computing content delivery Index Terms live streaming resource scheduling traffic engineering

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AggCast: Practical Cost-effective Scheduling for Large-scale cloud-edge Crowdsourced Live Streaming 22

AggCast: Practical Cost-effective Scheduling for Large-scale...

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30th ACM International Conference on Multimedia (MM)

作者： Zhang, Rui-Xiao Yang, Changpeng Wang, Xiaochan Huang, Tianchi Wu, Chenglei Liu, Jiangchuan Sun, Lifeng Tsinghua Univ Beijing Peoples R China Huawei Cloud Algorithm Innovat Lab Shenzhen Peoples R China Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781450392037

Conventional wisdom claims that in order to improve viewer engagement, the cloud-edge providers should serve the viewers with the nearest edge nodes, however, we show that doing this for crowd-sourced live streaming (CLS) services can introduce significant costs inefficiency. We observe that the massive number of channels has greatly burdened the operating expenditure of the cloud-edge providers, and most importantly, unbalanced viewer distribution makes the edge nodes suffer significant costs inefficiency. To this end, we propose AggCast, a novel CLS scheduling framework to optimize the edge node utilization for the cloud-edge provider. The core idea of AggCast is to aggregate some viewers who are initially scattered on different regions, and assign them to fewer pre-selected nodes, thereby reducing bandwidth costs. In particular, by leveraging the insights obtained from our large-scale measurement, AggCast can not only ensure quality of experience (QoS), but also satisfy the systematic requirements of CLS services. AggCast has been A/B tested and fully deployed in a top cloud-edge provider in China for over eight months. The experiments show that, compared to the common practice, AggCast can save over 15% back-to-source (BTS) bandwidth costs while having no negative impacts on QoS.

关键词： cloud edge computing content delivery live streaming

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