Journal articles: 'In-network computing'

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Gupta, Sachin. "Network Security in Cloud Computing." International Journal for Research in Applied Science and Engineering Technology 8, no. 11 (November 30, 2020): 636–41. http://dx.doi.org/10.22214/ijraset.2020.32244.

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Wu, Yulei, Guojun Wang, and Ahmed Al-Dubai. "Advances in trusted network computing." Security and Communication Networks 7, no. 9 (May 30, 2014): 1311–12. http://dx.doi.org/10.1002/sec.1050.

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Chen, Yu. "MEC Network Resource Allocation Strategy Based on Improved PSO in 5G Communication Network." International Journal on Semantic Web and Information Systems 19, no. 1 (August 18, 2023): 1–17. http://dx.doi.org/10.4018/ijswis.328526.

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Relying on features such as high-speed, low latency, support for cutting-edge technology, internet of things, and multimodality, 5G networks will greatly contribute to the transformation of Web 3.0. In order to realize low-latency and high-speed information exchange in 5G communication networks, a method based on the allocation of network computing resource in view of edge computing model is proposed. The method first considers three computing modes: local device computing, local mobile edge computing (MEC) server computing, and adjacent MEC server computing. Then, a multi-scenario edge computing model is further constructed for optimizing energy consumption and delay. At the same time, the encoding-decoding mode is used to optimize PSO algorithm and combined with the improvement of fitness function, which can effectively support the communication network to achieve reasonable allocation of resources, ensuring efficiency of information exchange in the network. In the end, the results show that when the number of users is 500, the method can complete the task assignment within 44s.

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Prof. Krishnakumar L, Prof Krishnakumar L., and Nisha Mariam Varughese. "Intrusion Detection Using Collaborative Network Security Management System in Cloud Computing." Indian Journal of Applied Research 4, no. 3 (October 1, 2011): 145–47. http://dx.doi.org/10.15373/2249555x/mar2014/42.

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Zheng, Changgang, Haoyue Tang, Mingyuan Zang, Xinpeng Hong, Aosong Feng, Leandros Tassiulas, and Noa Zilberman. "DINC: Toward Distributed In-Network Computing." Proceedings of the ACM on Networking 1, CoNEXT3 (November 27, 2023): 1–25. http://dx.doi.org/10.1145/3629136.

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In-network computing provides significant performance benefits, load reduction, and power savings. Still, an in-network service's functionality is strictly limited to a single hardware device. Research has focused on enabling on-device functionality, with limited consideration to distributed in-network computing. This paper explores the applicability of distributed computing to in-network computing. We present DINC, a framework enabling distributed in-network computing, generating deployment strategies, overcoming resource constraints and providing functionality guarantees across a network. It uses multi-objective optimization to provide a deployment strategy, slicing P4 programs accordingly. DINC was evaluated using seven different workloads on both data center and wide-area network topologies, demonstrating feasibility and scalability, providing efficient distribution plans within seconds.

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Hamdy, Noha, Amal Elsayed Aboutabl, Nahla ElHaggar, and Mostafa-Sami M. Mostafa. "Dynamic Task Scheduling Method in Cloud Computing Environment Using Optimized Neural Network." International Journal of Future Computer and Communication 6, no. 4 (December 2017): 143–47. http://dx.doi.org/10.18178/ijfcc.2017.6.4.507.

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Xiaochun, Xu, Xu Guanghui, and Xu Yongsen. "Architectural issues in network-centric computing." ACM SIGSOFT Software Engineering Notes 27, no. 1 (January 2002): 53–57. http://dx.doi.org/10.1145/566493.1148030.

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Ferrari, Adam. "JPVM: network parallel computing in Java." Concurrency: Practice and Experience 10, no. 11-13 (September 1998): 985–92. http://dx.doi.org/10.1002/(sici)1096-9128(199809/11)10:11/13<985::aid-cpe404>3.0.co;2-a.

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Alzahrani, Ali, Theyazn H. H. Aldhyani, Saleh Nagi Alsubari, and Ans D. Alghamdi. "Network Traffic Forecasting in Network Cybersecurity: Granular Computing Model." Security and Communication Networks 2022 (June 20, 2022): 1–14. http://dx.doi.org/10.1155/2022/3553622.

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Industry 4.0, also known as the Internet of Things, is a concept that encompasses the joint applicability of operation, the Internet, and information technologies to expand the efficiency expectation of automation to include green and flexible processes and innovative products and services. Industrial network infrastructures must be modified to accommodate extra traffic from a variety of technologies in order to achieve this integration. In order to successfully implement cutting-edge wireless technologies, high-quality service (QoS) must be provided to end users. It is thus important to keep an eye on the functioning of the whole network without impacting base station throughput. Improved network performance is constantly needed, even for already-deployed cellular networks, such as the 4th generation (4G) and 3rd generation (3G). For the purpose of forecasting network traffic, an integrated model based on the long short-term memory (LSTM) model was used to combine clustering rough k-means (RKM) and fuzzy c-means (FCM). Clustering granules derived from FCM and RKM were also utilized to examine the network data for each calendar year. The novelty of our proposed model is the integration of the prediction and forecasting results obtained using existing prediction models with centroids of clusters. The WIDE backbone network’s live network traffic statistics were used to evaluate the proposed solution. The integrated model’s outcomes were assessed using a variety of statistical markers, including mean square error (MSE), root mean square error (RMSE), and standard error. The suggested technique was able to provide findings that were very accurate. The prediction error of LSTM with FCM was less on the basis of the MSE of 0.00783 and RMSE of 0.0885 at the training phase, where the prediction values of LSTM with the RKM had an MSE of 0.00564 and RMSE of 0.7511. Finally, the suggested model may substantially increase the prediction accuracy attained using FCM and RKM clustering.

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Mehibs, Shawq Malik, and Soukaena Hassan Hashim. "Proposed Network Intrusion Detection System ‎In Cloud Environment Based on Back ‎Propagation Neural Network." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 26, no. 1 (December 19, 2017): 29–40. http://dx.doi.org/10.29196/jub.v26i1.351.

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Cloud computing is distributed architecture, providing computing facilities and storage resource as a service over the internet. This low-cost service fulfills the basic requirements of users. Because of the open nature and services introduced by cloud computing intruders impersonate legitimate users and misuse cloud resource and services. To detect intruders and suspicious activities in and around the cloud computing environment, intrusion detection system used to discover the illegitimate users and suspicious action by monitors different user activities on the network .this work proposed based back propagation artificial neural network to construct t network intrusion detection in the cloud environment. The proposed module evaluated with kdd99 dataset the experimental results shows promising approach to detect attack with high detection rate and low false alarm rate

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Gao, Weiting, Yuxiang Xiao, and Haoxu Yin. "Cloud computing technology applied in 5G mobile communication network." Applied and Computational Engineering 19, no. 1 (October 23, 2023): 1–8. http://dx.doi.org/10.54254/2755-2721/19/20230999.

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5G mobile communication network and cloud computing are the technological products and focus of today's era. Compared to 5G, 5G has seen a huge increase in peak speeds to 10-20Gbit/s, air interface latency as low as 1ms and much more. Cloud computing uploads data to the cloud so that users can access it more easily. They bring great convenience and high working efficiency to people's life. The use of cloud computing in 5G could make more efficient.5G, as a combination of new technology and cloud computing, will become a much larger market. This paper mainly describes the theoretical basis of 5G mobile communication network and cloud computing, the application of cloud computing in 5G (including automatic driving technology, surgery mobile communication network) and the current dilemma and the improvement needed. It aims to further promote the combination of 5G mobile communication network and cloud computing.

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Lee, Yang Yang, and Zaini Abdul Halim. "Stochastic computing in convolutional neural network implementation: a review." PeerJ Computer Science 6 (November 9, 2020): e309. http://dx.doi.org/10.7717/peerj-cs.309.

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Stochastic computing (SC) is an alternative computing domain for ubiquitous deterministic computing whereby a single logic gate can perform the arithmetic operation by exploiting the nature of probability math. SC was proposed in the 1960s when binary computing was expensive. However, presently, SC started to regain interest after the widespread of deep learning application, specifically the convolutional neural network (CNN) algorithm due to its practicality in hardware implementation. Although not all computing functions can translate to the SC domain, several useful function blocks related to the CNN algorithm had been proposed and tested by researchers. An evolution of CNN, namely, binarised neural network, had also gained attention in the edge computing due to its compactness and computing efficiency. This study reviews various SC CNN hardware implementation methodologies. Firstly, we review the fundamental concepts of SC and the circuit structure and then compare the advantages and disadvantages amongst different SC methods. Finally, we conclude the overview of SC in CNN and make suggestions for widespread implementation.

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and Communication Networks, Security. "Retracted: Network Traffic Forecasting in Network Cybersecurity: Granular Computing Model." Security and Communication Networks 2023 (December 6, 2023): 1. http://dx.doi.org/10.1155/2023/9801076.

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Pardi, Silvio. "Network in Belle II." EPJ Web of Conferences 245 (2020): 07019. http://dx.doi.org/10.1051/epjconf/202024507019.

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Belle II has started the Phase 3 data taking with a fully equipped detector. The data flow at the maximum luminosity is expected to be 12PB of data/year and will be analysed by a cutting-edge computing infrastructure spread over 26 Countries. Several of the major computing centres for HEP in Europe, USA and Canada will store the second copy of RAW data. In this scenario, the international network infrastructure for research plays a key role in supporting and orchestrating all the activities of data analysis and replication. The large-scale network data challenge will also take advantage from LHCONE VRF service and the support of network experts of KEKCC, Belle II sites and NREN. The program of major upgrade in 2019 empowered the connection among Japan, Europe and USA over a 100Gb geographic ring. In this work, we summarize the network requirements needed to accomplish all the tasks provided by the Belle II computing model. We also highlight the status of the major network links that support and advance Belle II. Lastly, we present the results of the last Network Data Challenge campaign performed between KEK and the main RAW data centres with the additional usage of the Data Transfer Node service provided by GÉANT.

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Ali, Ali. "Securing Pervasive Computing Networks: Enhancing Network Security via Network Virtualization in Wireless Communications Infrastructure." Journal of Intelligent Systems and Internet of Things 12, no. 2 (2024): 75–88. http://dx.doi.org/10.54216/jisiot.120206.

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The seamless integration of technology for computing into everyday items and environments is known as pervasive computing. To protect against cyber threats and vulnerabilities, robust security mechanisms are necessary. Conventional security measures, including gateways and the use of encryption, may not be sufficient to address the unique challenges encountered in ubiquitous computing systems. But these techniques are still vital. In addition to the variety of devices, resource limitations, mobility needs, and the possibility of large-scale distributed attacks, these obstacles also include the potential for attack. Network virtualization, that abstracts and separates network facilities and functions, is a promising way to increasing security in pervasive computing deployments: it abstracts and isolates network resources and processes. Wireless communication play a significant part in the development of a digital infrastructure that is both resilient and trustworthy. The processes of dynamic resource allocation, isolation, and management of network bandwidth are made possible through the utilization of virtualization, leads to the proposal of Secure Wireless Virtual Resource Allocation and Authentication Algorithm(SWVRA3) to make the abstraction of the network's physical resources into virtualized entities By using network virtualization, pervasive computing applications and services can be secured with logically segregated virtual networks. The cross-contamination and security breaches can be reduced by this separation. Furthermore, flexible configuration, dynamic allocation of resources, and centralized virtual control are allowed by network visualization that improves threat incidence response, enforcement of policies, and security surveillance.

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Fan, Zhou. "Analysis of Computer Network Security Issues in Cloud Computing Environment." Lifelong Education 9, no. 6 (September 28, 2020): 57. http://dx.doi.org/10.18282/le.v9i6.1297.

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Aiming at the computer network security issues in the cloud computing environment, this article outlines the great prospects of cloud computing technology and its significance, and respectively dissects the hidden dangers of computer network security problems under the use of cloud computing and the corresponding solutions. Computer network security issues in computing contribute to the smooth and long-term development of computer network security.

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Korniyenko, Bogdan, and Liliya Galata. "MODELING OF INFORMATION SECURITY SYSTEM IN COMPUTER NETWORK." Information systems and technologies security, no. 1 (1) (2019): 36–41. http://dx.doi.org/10.17721/ists.2019.1.36-41.

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This article presents simulation modeling process as the way to study the behavior of the Information Security system. Graphical Network Simulator is used for modeling such system and Kali Linux is used for penetration testing and security audit. To implement the project GNS3 package is selected. GNS3 is a graphical network emulator that allows you to simulate a virtual network of more than 20 different manufacturers on a local computer, connect a virtual network to a real one, add a full computer to the network, Third-party Applications for network packet analysis are supported. Depending on the hardware platform on which GNS3 will be used, it is possible to build complex projects consisting of routers Cisco, Cisco ASA, Juniper, as well as servers running network operating systems. Using modeling in the design of computing systems, you can: estimate the bandwidth of the network and its components; identify vulnerability in the structure of computing system; compare different organizations of a computing system; make a perspective development forecast for computer system; predict future requirements for network bandwidth; estimate the performance and the required number of servers in the network; compare various options for computing system upgrading; estimate the impact of software upgrades, workstations or servers power, network protocols changes on the computing system. Research computing system parameters with different characteristics of the individual components allows us to select the network and computing equipment, taking into account its performance, quality of service, reliability and cost. As the cost of a single port in active network equipment can vary depends on the manufacturer's equipment, technology used, reliability, manageability. The modeling can minimize the cost of equipment for the computing system. The modeling becomes effective when the number of workstations is 50-100, and when it more than 300, the total savings could reach 30-40% of project cost

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Piotrowski, Dominik Mirosław. "Cloud Computing as Network Environment in Students Work." Bilgi Dünyası 14, no. 1 (April 30, 2013): 191–98. http://dx.doi.org/10.15612/bd.2013.142.

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The purpose of the paper is to show the need for literacy education from a variety of services available in the cloud computing as a specialist information field of activity. Teaching at university in the field of cloud computing related to the management of information could provide tangible benefits in the form of useful learning outcomes. This allows students and future information professionals to begin enjoying the benefits of cloud computing SaaS model at work, thereby freeing up of buying expensive licenses and installing on your PC commercial software. Paper brings wider issues of cloud computing. The aim of the presentation is to show the ability to use a variety of services and applications available in the cloud as a workshop. These include: office and communication, tools, graphics editors, presentation software, dropbox, applications allowing the managed time and many others. The term “cloud computing” is related to the concept of virtualization. Moreover, paper also addresses the issue of data security in the cloud.

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Qianying Zhao, Bo Lei, and Min Wei. "Survey of computing power network." ITU Journal on Future and Evolving Technologies 3, no. 3 (December 7, 2022): 632–44. http://dx.doi.org/10.52953/bxbj6384.

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In September 2021, ITU-T released the first international recommendation for computing power networks (CPNs), ITU-T Y.2501 jointly led by China Telecom, China Unicom and Huawei, and started the Y.2500-Y.2599 series of recommendations for computing power networks. The computing power network is a new type of network that integrates multilevel computing resources, realizes the efficient coordination of the cloud, edge and network, improves the utilization efficiency of computing resources and provides users with the optimal computing and network resource services. Since the first proposal of the computing power network in 2019, it has made breakthroughs in its research and standardization work. As a new field of international standards, also a new field of the communication industry, this paper gives a comprehensive analysis of its definition, framework, service model, key technologies and current progress and challenges, summarizing the research direction of future computing power networks which will have strong implications for the research and implementation of the computing power network.

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Zhao, Zhongyu. "Neural Network Training in Distribution Computing Method." Journal of Physics: Conference Series 1802, no. 3 (March 1, 2021): 032056. http://dx.doi.org/10.1088/1742-6596/1802/3/032056.

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Chen, Xiaogui. "Application of Cloud Computing in Network Platform." Journal of Physics: Conference Series 1927, no. 1 (May 1, 2021): 012028. http://dx.doi.org/10.1088/1742-6596/1927/1/012028.

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Reddy, S. Bharath. "Anomaly Detection System in Cloud Computing Network." HELIX 8, no. 2 (February 28, 2018): 3188–97. http://dx.doi.org/10.29042/2018-3188-3197.

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arsh, Utk, Vaibhav Gupta, Yashwant Kumar Singh, and Shashi Kant Singh. "Data Leakage Detection in Cloud Computing Network." International Journal of Engineering Trends and Technology 46, no. 1 (April 25, 2017): 6–11. http://dx.doi.org/10.14445/22315381/ijett-v46p202.

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Mesheryakov, Roman, Alexander Moiseev, Anton Demin, Vadim Dorofeev, and Vasily Sorokin. "Using Parallel Computing in Queueing Network Simulation." Key Engineering Materials 685 (February 2016): 943–47. http://dx.doi.org/10.4028/www.scientific.net/kem.685.943.

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The paper is devoted to the simulation of queueing networks on high performance computer clusters. The objective is to develop a mathematical model of queueing network and simulation approach to the modelling of the general network functionality, as well as to provide a software implementation on a high-performance computer cluster. The simulation is based on a discrete-event approach, object oriented programming, and MPI technology. The model of the queueing networks simulation system was developed as an application that allows a user to simulate networks of rather free configuration. The experiments on a high performance computer cluster emphasize the high efficiency of parallel computing.

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Donaldson, V., F. Berman, and R. Paturi. "Program Speedup in a Heterogeneous Computing Network." Journal of Parallel and Distributed Computing 21, no. 3 (June 1994): 316–22. http://dx.doi.org/10.1006/jpdc.1994.1062.

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Zhao, Shi Yi, Hui Jun Yang, and Kai Yin. "Application and Challenge of Cloud Computing in Army Informationization Network." Advanced Materials Research 546-547 (July 2012): 1119–23. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1119.

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This article analyzes the "cloud computing" concept and characteristics, and the application of "cloud computing" in the army informatization network from the five areas, introduces several challenges brought about by cloud computing.

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Cheng, Yongbing. "Design and Implementation of Cloud Computing Network Security Virtual Computing and Defense Technology." Security and Communication Networks 2022 (August 30, 2022): 1–7. http://dx.doi.org/10.1155/2022/7876199.

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In order to improve the defense effect in network security, a research and implementation method of cloud computing network security virtual computing and defense technology is proposed. This architecture makes full use of the structural advantages of the virtualized environment, which can realize the trusted measurement of the user’s virtual machine in a more reliable way and can support the user’s diverse authentication requests. This paper introduces the concept of cloud computing, the classification of cloud computing, and the characteristics of cloud computing network security. In the case of fully considering the coupling relationship between the physical network and the logical network, the topology of the cloud computing network is established, and based on the network topology, the relevant network theory is used to analyze the cloud computing network. The avalanche failure under the computing network is studied. The research results show that the relative performance under different trusted measurement periods can reach more than 97%, which can flexibly meet the needs of user trusted authentication and can effectively provide trusted protection for user virtual machines. Adding additional protection measures to some special nodes in the cloud computing network topology to ensure that they are not damaged when attacked can greatly improve the robustness of the entire cloud computing network topology, therby ensuring that the network can avoid the attack. A large area will not be paralyzed due to the avalanche effect, and at the same time, the function and topology of the network itself have not changed. This method can effectively improve the security protection effect in network security.

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Zeng, Pan. "Neural Computing in Mechanics." Applied Mechanics Reviews 51, no. 2 (February 1, 1998): 173–97. http://dx.doi.org/10.1115/1.3098995.

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Recently, the artificial neural network has experienced a surge in popularity and is now one of the most rapidly expanding areas of research across many disciplines. The main reason is in its powerful and adaptive abilities to treat various complex problems. One can be sure that with its further developments, neural networks will strongly impact many conventional disciplines from the standpoint of methodology. In the field of mechanics, the research and application of both neural network and revolutionary computing are especially active and successful. The back propagated multilayered network is one of the main types applied to engineering. The related works concern almost all topics of engineering science and mechanics, such as, approximation of structural analysis, assessment of structural damage, fault diagnosis, prediction, strategic management, decision making, structural optimization, etc. The aim of this review is to summarize and recapitulate the up-to-date developments and applications of neural networks and computing in mechanics, with emphasis on the back propagation algorithm of multilayer networks. Not only are the fundamental principles outlined clearly, but some typical examples are also presented. It is hoped that this review article can promote the development and applications of neural network and computing in mechanics. This article contains 221 references.

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Zhang, Zhenzhong, Wei Sun, and Yanliang Yu. "Research on Intelligent Scheduling Mechanism in Edge Network for Industrial Internet of Things." Security and Communication Networks 2022 (January 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/5358873.

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With the vigorous development of the Internet of Things, the Internet, cloud computing, and mobile terminals, edge computing has emerged as a new type of Internet of Things technology, which is one of the important components of the Industrial Internet of Things. In the face of large-scale data processing and calculations, traditional cloud computing is facing tremendous pressure, and the demand for new low-latency computing technologies is imminent. As a supplementary expansion of cloud computing technology, mobile edge computing will sink the computing power from the previous cloud to a network edge node. Through the mutual cooperation between computing nodes, the number of nodes that can be calculated is more, the types are more comprehensive, and the computing range is even greater. Broadly, it makes up for the shortcomings of cloud computing technology. Although edge computing technology has many advantages and has certain research and application results, how to allocate a large number of computing tasks and computing resources to computing nodes and how to schedule computing tasks at edge nodes are still challenges for edge computing. In view of the problems encountered by edge computing technology in resource allocation and task scheduling, this paper designs a dynamic task scheduling strategy for edge computing with delay-aware characteristics, which realizes the reasonable utilization of computing resources and is required for edge computing systems. This paper proposes a resource allocation scheme combined with the simulated annealing algorithm, which minimizes the overall performance loss of the system while keeping the system low delay. Finally, it is verified through experiments that the task scheduling and resource allocation methods proposed in this paper can significantly reduce the response delay of the application.

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Xia, Yao Wen. "Network Teaching System Trial Based on Cloud Computing." Advanced Materials Research 756-759 (September 2013): 1903–7. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1903.

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Cloud computing is a new network application mode, with the arrival of the era of cloud computing, cloud computing technology has been applied to education in the field of network teaching practice. Based on the concept of cloud computing service level, on the basis of research, the cloud computing application to the university network teaching platform, then puts forward the implementation of the application scheme of cloud computing in the network remote teaching in a trial and application analysis of cloud computing and education field of education informatization with the positive influence.

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Wang, Xiaofeng, Wei Zhu, and Yueming Dai. "Network simulation task partition method in heterogeneous computing environment." International Journal of Modeling, Simulation, and Scientific Computing 05, no. 03 (May 5, 2014): 1450010. http://dx.doi.org/10.1142/s179396231450010x.

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To reduce the running time of network simulation in heterogeneous computing environment, a network simulation task partition method, named LBPHCE, is put forward. In this method, the network simulation task is partitioned in comprehensive consideration of the load balance of both routing computing simulation and packet forwarding simulation. First, through benchmark experiments, the computation ability and routing simulation ability of each simulation machine are measured in the heterogeneous computing environment. Second, based on the computation ability of each simulation machine, the network simulation task is initially partitioned to meet the load balance of packet forwarding simulation in the heterogeneous computing environment, and then according to the routing computation ability, the scale of each partition is fine-tuned to satisfy the balance of the routing computing simulation, meanwhile the load balance of packet forwarding simulation is guaranteed. Experiments based on PDNS indicate that, compared to traditional uniform partition method, the LBPHCE method can reduce the total simulation running time by 26.3% in average, and compared to the liner partition method, it can reduce the running time by 18.3% in average.

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Pradhananga, Yanish, and Pothuraju Rajarajeswari. "Tiarrah Computing: The Next Generation of Computing." International Journal of Informatics and Communication Technology (IJ-ICT) 6, no. 2 (August 1, 2017): 129. http://dx.doi.org/10.11591/ijict.v6i2.pp129-138.

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The evolution of Internet of Things (IoT) brought about several challenges for the existing Hardware, Network and Application development. Some of these are handling real-time streaming and batch bigdata, real- time event handling, dynamic cluster resource allocation for computation, Wired and Wireless Network of Things etc. In order to combat these technicalities, many new technologies and strategies are being developed. Tiarrah Computing comes up with integration the concept of Cloud Computing, Fog Computing and Edge Computing. The main objectives of Tiarrah Computing are to decouple application deployment and achieve High Performance, Flexible Application Development, High Availability, Ease of Development, Ease of Maintenances etc. Tiarrah Computing focus on using the existing opensource technologies to overcome the challenges that evolve along with IoT. This paper gives you overview of the technologies and design your application as well as elaborate how to overcome most of existing challenge.

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Pradhananga, Yanish, and Pothuraju Rajarajeswari. "Tiarrah Computing: The Next Generation of Computing." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (April 1, 2018): 1247. http://dx.doi.org/10.11591/ijece.v8i2.pp1247-1255.

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The evolution of Internet of Things (IoT) brought about several challenges for the existing Hardware, Network and Application development. Some of these are handling real-time streaming and batch bigdata, real- time event handling, dynamic cluster resource allocation for computation, Wired and Wireless Network of Things etc. In order to combat these technicalities, many new technologies and strategies are being developed. Tiarrah Computing comes up with integration the concept of Cloud Computing, Fog Computing and Edge Computing. The main objectives of Tiarrah Computing are to decouple application deployment and achieve High Performance, Flexible Application Development, High Availability, Ease of Development, Ease of Maintenances etc. Tiarrah Computing focus on using the existing opensource technologies to overcome the challenges that evolve along with IoT. This paper gives you overview of the technologies and design your application as well as elaborate how to overcome most of existing challenge.

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Xu, Dawei, Qing Liu, Liehuang Zhu, Zhonghua Tan, Feng Gao, and Jian Zhao. "GCNRDM: A Social Network Rumor Detection Method Based on Graph Convolutional Network in Mobile Computing." Wireless Communications and Mobile Computing 2021 (October 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/1690669.

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Mobile computing is a new technology emerging with the development of mobile communication, Internet, database, distributed computing, and other technologies. Mobile computing technology will enable computers or other information intelligent terminal devices to realize data transmission and resource sharing in the wireless environment. Its role is to bring useful, accurate, and timely information to any customer at anytime, anywhere, and to change the way people live and work. In mobile computing environment, a lot of Internet rumors hidden among the huge amounts of information communication network can cause harm to society and people’s life; this paper proposes a model of social network rumor detection based on convolution networks, the use of adjacency matrix between the nodes represent user and the relationship between the constructions of social network topology. We use a high-order graph neural network (K-GNN) to extract the rumor posting features. At the same time, the graph attention network (GAT) is used to extract the association features of other nodes of the network topology. The experimental results show that the method of the detection model in this paper improves the accuracy of prediction classification compared with deep learning methods such as RNN, GRU, and attention mechanism. The innovation of the paper proposes a rumor detection model based on the graph convolutional network, which lies in considering the propagation structure among users. It has a strong practical value.

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Yu, Qiu Dong, Yun Chen Tian, and Xu Feng Hua. "Research on Security of Agricultural Information Model Based on Cloud Computing." Applied Mechanics and Materials 687-691 (November 2014): 1970–73. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1970.

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In 2007, as a new computing paradigm, Cloud Computing go into people's sight. It is a technology that develop with the parallel computing, distributed computing, utility computing, a new computing paradigm and the emergence of grid computing development. It is also the result of network storage, virtualization and load balancing development. As an Internet-based super computing paradigm, cloud computing allows users to dynamically share the hardware, software, and data resources. In the process of sharing resources, it is inevitable to involve security issues of network transmission. In order to solve the security problems of network data integration cloud computing facing agricultural resources, this study proposes a network security model which can be proved to be much useful in the near future.

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Wen, Lin Bin. "Research on Multilayer Security Audit Research Based on Attack Graph in Cloud Computing." Applied Mechanics and Materials 644-650 (September 2014): 3408–11. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3408.

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Cloud computing, the future development direction of the IT industry, has the profound influence of cloud computing applications, which is bound to the field of higher education, the construction of university network education resources platform is the integration of all kinds of educational resources of colleges and universities, and to provide fast and convenient resource storage, sharing, learning and computational ability. This article is from the university network education platform of cloud computing based on the advantages of the proposed strategy, construction of information resources of University under the cloud computing environment. This is how the cloud model construction resources platform of network education in Colleges and universities. With the rapid development of Internet, people from all walks of life to and gradually mature "cloud computing" model of combining the road to seek an opportunity. However, in the field of education, the application of cloud computing is scanty. Research on the current college network education situation and abuse with according to the characteristics of cloud computing, advantages, and puts forward the conception of network education platform based on cloud computing, and further demonstrates its feasibility. It is the high time to improve the transplantation and service application pattern of cloud computing in the field of education.

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Gong, Xiaomin, Shuangyin Ren, Chunjiang Wang, and Jingchao Wang. "Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network." Sensors 24, no. 4 (February 7, 2024): 1086. http://dx.doi.org/10.3390/s24041086.

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Computing resource measurement and computing routing are essential technologies in the computing first network (CFN), serving as its foundational elements. This paper introduces a Software Defined Computing First Network (SD-CFN) architecture. Building upon this framework, a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM) is proposed, incorporating methods such as the entropy weight method and K-Means clustering. The DCRMM algorithm outperforms the Maximum-closest Static Algorithm (MSA) and Maximum Closest Dynamic Algorithm (MDA) in terms of node stability, node utilization, and node matching accuracy. Additionally, a Reinforcement Learning and Software Defined Computing First Networking Routing (RSCR) algorithm is presented as a software-defined computing routing solution within the SD-CFN. RSCR introduces a knowledge plane responsible for computing routing calculations. It comprehensively considers factors such as link latency, available bandwidth, and packet loss rate. Simulation experiments conducted on the GÉANT topology demonstrate that RSCR outperforms the OSPF algorithm in terms of link latency, packet loss rate, and throughput. DCRMM and RSCR offer innovative solutions for computing resource measurement and computing routing in computing first networks.

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Hoang, Nguyen Van. "Multi-Access Edge Computing Architecture and Smart Agriculture Application in Ubiquitous Power Internet of Things." International Journal of Mobile Network Communications & Telematics 13, no. 4 (August 27, 2023): 01–19. http://dx.doi.org/10.5121/ijmnct.2023.13401.

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The Ubiquitous Power Internet of Things (UPIoT) is a deep integration of the interconnected power network and communication network, enabling full perception of the system status and business operations for power production, transmission, and consumption. To address the challenges of real-time perception, rapid response, and privacy protection, UPIoT can benefit from the use of edge computing technology. Edge computing is a new and innovative computing architecture that enables quick and efficient processing of data close to the source, bypassing network latency and bandwidth issues. By shifting computing power to the edge of the network, edge computing reduces the strain on cloud computing centers and decreases input response time for users. However, access latency can still be a bottleneck, which may overshadow the benefits of edge computing, particularly for data-intensive services. While edge computing offers promising solutions for the IoT network, there are still some issues to address, such as security, incomplete data, and investment and maintenance costs. In this paper, researcher conducts a comprehensive survey of edge computing and how edge device placement can improve performance in IoT networks. The paper includes a comparative use case of smart agriculture edge computing implementations and discusses the various challenges faced in implementing edge computing in the UPIoT context. The results also aim to inspire new edge-based IoT security designs by providing a complete review of IoT security solutions at the edge layer in UPIoT.

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Zheng, Zhe, Yu Han, Yingying Chi, Fusheng Yuan, Wenpeng Cui, Hailong Zhu, Yi Zhang, and Peiying Zhang. "Network Resource Allocation Algorithm Using Reinforcement Learning Policy-Based Network in a Smart Grid Scenario." Electronics 12, no. 15 (August 3, 2023): 3330. http://dx.doi.org/10.3390/electronics12153330.

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The exponential growth in user numbers has resulted in an overwhelming surge in data that the smart grid must process. To tackle this challenge, edge computing emerges as a vital solution. However, the current heuristic resource scheduling approaches often suffer from resource fragmentation and consequently get stuck in local optimum solutions. This paper introduces a novel network resource allocation method for multi-domain virtual networks with the support of edge computing. The approach entails modeling the edge network as a multi-domain virtual network model and formulating resource constraints specific to the edge computing network. Secondly, a policy network is constructed for reinforcement learning (RL) and an optimal resource allocation strategy is obtained under the premise of ensuring resource requirements. In the experimental section, our algorithm is compared with three other algorithms. The experimental results show that the algorithm has an average increase of 5.30%, 8.85%, 15.47% and 22.67% in long-term average revenue–cost ratio, virtual network request acceptance ratio, long-term average revenue and CPU resource utilization, respectively.

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Mangla, Cherry, Shalli Rani, and Henry Kwame Atiglah. "Secure Data Transmission Using Quantum Cryptography in Fog Computing." Wireless Communications and Mobile Computing 2022 (January 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/3426811.

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Fog computing’s idea is to bring virtual existence into objects used on a daily basis. The “objects” layer of fog architecture is also known as the smart object layer (SOL). SOL has provided the fog network with a strong platform to outperform. Although the fog architecture decentralizes data, uses more data centers, and collects and transmits it to adjacent servers for faster processing in fog networks, it faces several security challenges. The security problems of fog computing need to be alleviated for the exploitation of all benefits of fog computing in classical networks. This article has addressed the security challenges in fog computing, potential solutions via quantum cryptography, a use case portraying the importance of quantum cryptography in fog computing along future scope, and research directions.

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Bai, ChenChen, XiaoMing Gong, ShuangYin Ren, Tianji Xu, and JingChao Wang. "A Routing Algorithm Based on Software-Defined Computing First Network." Journal of Physics: Conference Series 2670, no. 1 (December 1, 2023): 012017. http://dx.doi.org/10.1088/1742-6596/2670/1/012017.

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Abstract With the continuous development of mobile Internet technology, new services and applications appear, such as AR/VR, Internet of Vehicles, and Ultra HD Video. They not only require higher network bandwidth resources but also need more computing resources to ensure normal operation and user experience. As a new network technology, computing first network promotes the deep integration of distributed computing resources and networks to make efficient use of these resources. This paper proposes a computing power routing algorithm based on software-defined computing first network (SD-CFN) architecture. It comprehensively considers computing resources and available bandwidth resources of network links. Experimental results show that compared with the nearby scheduling strategy, the proposed computing power routing algorithm can better allocate computing resources to services. Moreover, it can improve the utilization rate of computing resources and provide optimal bandwidth resources in the process of service transmission.

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Li, Yong Xia, Guang Xia Xu, and Shuang Yan Liu. "Research on Dependable Level in Network Computing System." Applied Mechanics and Materials 513-517 (February 2014): 1105–8. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1105.

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The most weakness link in credible monitoring is that how to process multidimensional dynamic behavior data effectively. System behavior monitoring often needs to deal with different kinds of behavior data and those data can adopt status snapshot in multi-dimensional vector form to express. Obviously, data has strong useful knowledge information, which is regarded as a kind of classification ability. So we need to finish the mapping and classification between a variety of network behavior snapshot and dependable level. This paper introduces on network state snapshot owning the characteristics of high dimension, heterogeneous and dynamic and uses the theory of interval intuitionistic fuzzy to judge credible degree in the system and generate behavior quality trust level of nodes.

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Qingping*, Guo, Yakup Paker, Dennis Parkinson, and Xiao JinSheng. "Network computing performance evaluation in message passing environment." Parallel Algorithms and Applications 18, no. 4 (December 2003): 217–24. http://dx.doi.org/10.1080/10637190310001639767.

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Kanaan Kadhim, Qusay, Hamid Sadeq Mahdi, and Haitham Ail. "Storage Architecture for Network Security in Cloud Computing." Diyala Journal For Pure Science 14, no. 1 (January 1, 2018): 1–17. http://dx.doi.org/10.24237/djps.1401.205c.

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Pekergin, M. F. "Parallel computing optimization in the Apollo domain network." IEEE Transactions on Software Engineering 18, no. 4 (April 1992): 296–303. http://dx.doi.org/10.1109/32.129218.

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Chen, Jiming, Kaoru Ota, Lu Wang, and Jianping He. "Big data and smart computing in network systems." Peer-to-Peer Networking and Applications 12, no. 5 (July 11, 2019): 1308–10. http://dx.doi.org/10.1007/s12083-019-00784-6.

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Li, Chengzhi, and Huaiyu Dai. "Efficient In-Network Computing with Noisy Wireless Channels." IEEE Transactions on Mobile Computing 12, no. 11 (November 2013): 2167–77. http://dx.doi.org/10.1109/tmc.2012.185.

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Wang, Wenyi, and Hongfen Zhang. "The methods of reduction in network reliability computing." Microelectronics Reliability 37, no. 3 (March 1997): 461–65. http://dx.doi.org/10.1016/s0026-2714(96)00019-4.

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Golick, Jerry. "Network computing in the new thin-client age." netWorker 3, no. 1 (March 1999): 30–40. http://dx.doi.org/10.1145/294626.294637.

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Sueyoshi, Toshiyuki, and Takashi Honma. "DEA network computing in multi-stage parallel processes." International Transactions in Operational Research 10, no. 3 (May 2003): 217–44. http://dx.doi.org/10.1111/1475-3995.00405.

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Journal articles on the topic 'In-network computing'

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