Relevant bibliographies by topics / In-network computing

Academic literature on the topic 'In-network computing'

Author: Grafiati
Published: 10 December 2022
Last updated: 8 June 2024

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Journal articles on the topic "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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Dissertations / Theses on the topic "In-network computing"

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Barros, Bruno Medeiros de. "Security architecture for network virtualization in cloud computing." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-18012017-094453/.

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Network virtualization has been a quite active research area in the last years, aiming to tackle the increasing demand for high performance and secure communication in cloud infrastructures. In special, such research eforts have led to security solutions focused on improving isolation among multiple tenant of public clouds, an issue recognized as critical both by the academic community and by the technology Industry. More recently, the advent of Software-Defined Networks (SDN) and of Network Function Virtualization (NFV) introduced new concepts and techniques for addressing issues related to the isolation of network resources in multi-tenant clouds while improving network manageability and flexibility. Similarly, hardware technologies such as Single Root I/O Virtualization (SR-IOV) enable network isolation in the hardware level while improving performance in physical and virtual networks. Aiming to provide a cloud network environment that effciently tackles multi-tenant isolation, we present three complementary strategies for addressing the isolation of resources in cloud networks. These strategies are then applied in the evaluation of existing network virtualization architectures, exposing the security gaps associated to current technologies, and paving the path for novel solutions. We then propose a security architecture that builds upon the strategies presented, as well as upon SDN, NFV and SR-IOV technologies, to implement secure cloud network domains. The theoretical and experimental analyses of the resulting architecture show a considerable reduction of the attack surface in tenant networks, with a small impact over tenants\' intra-domain and inter-domain communication performance.
Virtualização de redes é uma área de pesquisa que tem ganho bastante atenção nos últimos anos, motivada pela necessidade de se implementar sistemas de comunicação seguros e de alta performance em infraestruturas de computação em nuvem. Em particular, os esforços de pesquisa nesta área têm levado ao desenvolvimento de soluções de segurança que visam aprimorar o isolamento entre os múltiplos inquilinos de sistemas de computação em nuvem públicos, uma demanda reconhecidamente crítica tanto pela comunidade acadêmica quanto pela indústria de tecnologia. Mais recentemente, o advento das Redes Definidas por Software (do inglês Software-Defined Networks - SDN) e da Virtualização de Funções de Rede (do inglês Network Function Virtualization - NFV) introduziu novos conceitos e técnicas que podem ser utilizadas para abordar questões de isolamento de redes virtualizadas em sistemas de computação em nuvem com múltiplos inquilinos, enquanto aprimoram a capacidade de gerenciamento e a flexibilidade de suas redes. Similarmente, tecnologias de virtualização assistida por hardware como Single Root I/O Virtualization - SR-IOV permitem a implementação do isolamento de recursos de hardware, melhorando o desempenho de redes físicas e virtualizadas. Com o intuito de implementar uma solução de virtualização de redes que aborda de maneira eficiente o problema de isolamento entre múltiplos inquilinos, nós apresentamos três estratégias complementares para o isolamento de recursos de rede em sistemas computação em nuvem. As estratégias apresentadas são então aplicadas na avaliação de arquiteturas de virtualização de rede existentes, revelando lacunas de segurança associadas às tecnologias utilizadas atualmente, e abrindo caminho para o desenvolvimento de novas soluções. Nós então propomos uma arquitetura de segurança que utiliza as estratégias apresentadas, e tecnologias como SDN, NFV e SR-IOV, para implementar domínios de rede seguros. As análises teórica e experimental da arquitetura proposta mostram considerável redução das superfícies de ataque em redes virtualizadas, com um pequeno impacto sobre o desempenho da comunicação entre máquinas virtuais de inquilinos da nuvem.
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Paraskelidis, Athanasios. "Wireless network segregation utilising modulo in industrial environments." Thesis, University of Portsmouth, 2010. https://researchportal.port.ac.uk/portal/en/theses/wireless-network-segregation-utilising-modulo-in-industrial-environments(ae94690a-560e-4f7b-93d8-130b4873de96).html.

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With the success of wireless technologies in consumer electronics, standard wireless technologies are envisioned for the deployment in industrial environments as well. Industrial applications involving mobile subsystems or just the desire to save cabling make wireless technologies attractive. In industrial environments, timing and reliability are well catered by the current wired technologies. When wireless links are included, reliability and timing requirements are significantly more difficult to meet, due to the common problems that influence them such as interference, multipath and attenuation. Since the introduction of the IEEE 802.11 standard, researchers have moved from the concept of deploying a single channel and proposed the utilisation of multiple channels within a wireless network. This new scheme posed a new problem, the ability to coordinate the various channels and the majority of the proposed works focus on mechanisms that would reduce the adjacent channel interference caused by the use of partially overlapping channels. These mechanisms are mainly algorithms that define rules to the allocation of the channels for the wireless nodes during each transmission. Many of the approached proposed during the last years have two very common disadvantages, they are hard to implement in real life and they do not take full advantage of the available spectrum, because they use only non-overlapping channels. The industries demand for solutions which would not move away from using proprietary hardware and software and any changes required to be made should not limit the availability of support for their networks. This would keep the cost low as it is the main factor that industries decide to replace their wires with radio links. The proposed idea in this thesis borrows the concept of network segregation, firstly introduced for security purposes in wired networks, by dividing a wireless network into smaller independent subnetworks and in collaboration with a channel assignment, the Modulo. Modulo defines a set of rules that nodes should obey to when they transmit data. The utilization of multiple channels under the guidance of Modulo for each subnetwork, proves to improve the performance of an ad-hoc network even in noisy industrial environments with high levels of interference from external sources.
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Olsson, Eric J. "Literature survey on network concepts and measures to support research in network-centric operations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FOlsson.pdf.

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Zhou, You M. Eng Massachusetts Institute of Technology. "Computing network coordinates in the presence of Byzantine faults." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/46365.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 57-60).
Network coordinate systems allow for efficient construction of large-scale distributed systems on the Internet. Coordinates provide locality information in a compact way, without requiring each node to contact every potential neighbor; distances between two nodes' coordinates represent estimates of the network latency between them. Past work on network coordinates has assumed that all nodes in the system behave correctly. The techniques in these systems do not behave well when nodes are Byzantine. These Byzantine failures, wherein a faulty node can behave arbitrarily, can make the coordinate-based distance estimates meaningless. For example, a Byzantine node can delay responding to some other node, thus distorting that node's computation of its own location. We present a network coordinate system based on landmarks, reference nodes that are used for measurements, some of which may be Byzantine faulty. It scales linearly in the number of clients computing their coordinates and does not require excessive network traffic to allow clients to do so. Our results show that our system is able to compute accurate coordinates even when some landmarks are exhibiting Byzantine faults.
by You Zhou.
M.Eng.
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Crellin, Kenneth Thomas. "Network time : synchronisation in real time distributed computing systems." Master's thesis, University of Cape Town, 1998. http://hdl.handle.net/11427/17933.

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In the past, network clock synchronization has been sufficient for the needs of traditional distributed systems, for such purposes as maintaining Network File Systems, enabling Internet mail services and supporting other applications that require a degree of clock synchronization. Increasingly real time systems arc requiring high degrees of time synchronization. Where this is required, the common approach up until now has been to distribute the clock to each processor by means of hardware (e.g. GPS and cesium clocks) or to distribute time by means of an additional dedicated timing network. Whilst this has proved successful for real time systems, the use of present day high speed networks with definable quality of service from the protocol layers has lead to the possibility of using the existing data network to distribute the time. This thesis demonstrates that by using system integration and implementation of commercial off the shelf (COTS) products it is possible to distribute and coordinate the time of the computer time clocks to microsecond range. Thus providing close enough synchronization to support real time systems whilst avoiding the additional time, infrastructure and money needed to build and maintain a specialized timing network.
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Mousa, Alzawi Mohamed. "Autonomic computing : using adaptive neural network in self-healing systems." Thesis, Liverpool John Moores University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.571894.

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Self-management is the main objective of Autonomic Computing (AC), and it is needed to increase the running system's reliability, stability, and performance. Investigation some issues related to complex systems such as; self-awareness system, when and where an error state occurs, knowledge for system stabilization, analyze the problem, healing plan with different solutions for adaptation without the need for human intervention. This research work focuses on self-healing, which is the most important component of Autonomic Computing. Self-healing is a technique that has different phases, which aims to detect, analyze, and repair existing faults within the system. All of these phases are accomplished in a real-time system. In this approach, the system is capable of performing a reconfiguration action in order to recover from a permanent fault. Moreover, self- healing system should have the ability to modify its own behavior in response to changes within the environment. However, there are some challenges that still face the implementation of self-healing in real system adaptation. These challenges are monitoring, interpretation, resolution, and adaptation. Artificial Neural Networks have been proposed to overcome these challenges. Neural network proposed to minimize the error between the desired response and the actual output by modifying its weights. , ... ~' Furthermore, Neural Networks have a built-in capability to adapt their weights in nonstatinary environment, and that is required in real time problems as in self-healing systems. A recurrent neural network is used to show the ability of neural network to overcome the challenges associated with self-healing. A modified pipelined neural network is introduced to fulfill the requirements in this field. Two different applications were suggested and used to examine the validity of research work. Client server / / application has shown promising results comparing to the outcomes of feedforward -- neural network. Moreover, with the overcurrent relay experiment in the field of power system has achieved good results using pipelined recurrent neural network. The main point for the comparison between pipelined recurrent neural network and feedforward neural network is the continuous learning or online learning. This is important since autonomic systems aim to apply the monitoring of system behaviors and apply the suitable re configuration plan during the running time of the system.
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Tasiopoulos, A. "On the deployment of low latency network applications over third-party in-network computing resources." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10049954/.

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An increasing number of Low Latency Applications (LLAs) in the entertainment (Virtual/Augmented Reality), Internet-of-Things (IoT), and automotive domains require response times that challenge the traditional application provisioning process into distant data centres. At the same time, there is a trend in deploying In-Network Computing Resources (INCRs) closer to end users either in the form of network equipment, with capabilities of performing general-purpose computations, and/or in the form of commercial off-the-self “data centres in a box”, i.e., cloudlets, placed at different locations of Internet Service Providers (ISPs). That is, INCRs extend cloud computing at the edge and middle-tier locations of the network, providing significantly smaller response times than those achieved by the current “client-to-cloud” network model. In this thesis, we argue about the necessity of exploiting INCRs for application provisioning with the purpose of improving LLAs’ Quality of Service (QoS) by essentially deploying applications closer to end users. To this end, this thesis investigates the deployment of LLAs over INCRs under fixed, mobile, and disrupted user connectivity environments. In order to fully reap the benefits of INCRs, we develop for each connectivity scenario algorithmic frameworks that are centred around the concept of a market, where LLAs lease existing INCRs. The proposed frameworks take into account the particular characteristics of INCRs, such as their limited capacity in hosting application instances, and LLAs, by addressing the number of instances each application should deploy at each computing resource over time. Furthermore, since typically the smooth operation of network applications is supported by Network Functions, such as load balancers, firewalls etc., we consider the deployment of complementary Virtual Network Functions for backing LLAs’ provisioning over INCRs. Overall, the key goal of this thesis is the investigation of using an enhanced Internet through INCRs as the communication platform for LLAs.
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Ju, Weiyu. "Mobile Deep Neural Network Inference in Edge Computing with Resource Restrictions." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25038.

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Recent advances in deep neural networks (DNNs) have substantially improved the accuracy of intelligent applications. However, the pursuit of a higher accuracy has led to an increase in the complexity of DNNs, which inevitably increases the inference latency. For many time-sensitive mobile inferences, such a delay is intolerable and could be fatal in many real-world applications. To solve this problem, one effective scheme known as DNN partition is proposed, which significantly improves the inference latency by partitioning the DNN to a mobile device and an edge server to jointly process the inference. This approach utilises the stronger computing capacity of the edge while reducing the data transmission. Nevertheless, this approach requires a reliable network connection, which is oftentimes unstable. Therefore, DNN partition is vulnerable in the presence of service outages. In this thesis, we are motivated to investigate how to maintain the quality of the service during service outages to avoid interruptions. Inspired by the recently developed early exit technique, we propose three solutions: (1) When the service outage time is predictable, we propose eDeepSave to decide which frames to process during the service outage. (2) When the service outage time is not predictable but relatively short, we design LEE to effectively learn the optimal exit point in a per-instance manner. (3) When the service outage time is not predictable and relatively long, we present the DEE scheme to learn the optimal action (to exit or not) at each exit point, so that the system can dynamically exit the inference by utilising the observed environmental information. For each scheme, we provide detailed mathematical proofs of the performance and then test their performance in real-world experiments as well as the extensive simulations. The results of the three schemes demonstrate their effectiveness in maintaining the service during the service outage under a variety of scenarios.
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Nowshin, Fabiha. "Spiking Neural Network with Memristive Based Computing-In-Memory Circuits and Architecture." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103854.

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In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. There are two main types of Artificial Neural Networks (ANNs), Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). In this thesis we first study the types of RNNs and then move on to Spiking Neural Networks (SNNs). SNNs are an improved version of ANNs that mimic biological neurons closely through the emission of spikes. This shows significant advantages in terms of power and energy when carrying out data intensive applications by allowing spatio-temporal information processing. On the other hand, emerging non-volatile memory (eNVM) technology is key to emulate neurons and synapses for in-memory computations for neuromorphic hardware. A particular eNVM technology, memristors, have received wide attention due to their scalability, compatibility with CMOS technology and low power consumption properties. In this work we develop a spiking neural network by incorporating an inter-spike interval encoding scheme to convert the incoming input signal to spikes and use a memristive crossbar to carry out in-memory computing operations. We develop a novel input and output processing engine for our network and demonstrate the spatio-temporal information processing capability. We demonstrate an accuracy of a 100% with our design through a small-scale hardware simulation for digit recognition and demonstrate an accuracy of 87% in software through MNIST simulations.
M.S.
In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. Artificial Neural Networks (ANNs) are models that mimic biological neurons where artificial neurons or neurodes are connected together via synapses, similar to the nervous system in the human body. here are two main types of Artificial Neural Networks (ANNs), Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). In this thesis we first study the types of RNNs and then move on to Spiking Neural Networks (SNNs). SNNs are an improved version of ANNs that mimic biological neurons closely through the emission of spikes. This shows significant advantages in terms of power and energy when carrying out data intensive applications by allowing spatio-temporal information processing capability. On the other hand, emerging non-volatile memory (eNVM) technology is key to emulate neurons and synapses for in-memory computations for neuromorphic hardware. A particular eNVM technology, memristors, have received wide attention due to their scalability, compatibility with CMOS technology and low power consumption properties. In this work we develop a spiking neural network by incorporating an inter-spike interval encoding scheme to convert the incoming input signal to spikes and use a memristive crossbar to carry out in-memory computing operations. We demonstrate the accuracy of our design through a small-scale hardware simulation for digit recognition and demonstrate an accuracy of 87% in software through MNIST simulations.
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Shafaatdoost, Mani. "Approaches to Provisioning Network Topology of Virtual Machines in Cloud Systems." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/784.

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The current infrastructure as a service (IaaS) cloud systems, allow users to load their own virtual machines. However, most of these systems do not provide users with an automatic mechanism to load a network topology of virtual machines. In order to specify and implement the network topology, we use software switches and routers as network elements. Before running a group of virtual machines, the user needs to set up the system once to specify a network topology of virtual machines. Then, given the user’s request for running a specific topology, our system loads the appropriate virtual machines (VMs) and also runs separated VMs as software switches and routers. Furthermore, we have developed a manager that handles physical hardware failure situations. This system has been designed in order to allow users to use the system without knowing all the internal technical details.
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Books on the topic "In-network computing"

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Sharma, Deepak Kumar, Koyel Datta Gupta, and Rinky Dwivedi. Green Computing in Network Security. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003097198.

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Thampi, Sabu M., Erol Gelenbe, Mohammed Atiquzzaman, Vipin Chaudhary, and Kuan-Ching Li, eds. Advances in Computing and Network Communications. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6977-1.

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Thampi, Sabu M., Erol Gelenbe, Mohammed Atiquzzaman, Vipin Chaudhary, and Kuan-Ching Li, eds. Advances in Computing and Network Communications. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6987-0.

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Das, Vinu V., and Janahanlal Stephen, eds. Advances in Communication, Network, and Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35615-5.

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Zhang, Yuchao, and Ke Xu. Network Management in Cloud and Edge Computing. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0138-8.

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Schmeck, Hartmut, Theo Ungerer, and Lars Wolf, eds. Trends in Network and Pervasive Computing — ARCS 2002. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45997-9.

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Abawajy, Jemal H. Network and traffic engineering in emerging distributed computing applications. Hershey, PA: Information Science Reference, 2013.

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Kavian, Yousef S., and Mark Stephen Leeson. Resilient optical network design: Advances in fault-tolerant methodologies. Hershey, PA: Information Science Reference, 2012.

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1941-, Bradley Stephen P., and Nolan Richard L, eds. Sense & respond: Capturing value in the network era. Boston, Mass: Harvard Business School Press, 1998.

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Baeza-Yates, Ricardo, Ugo Montanari, and Nicola Santoro, eds. Foundations of Information Technology in the Era of Network and Mobile Computing. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-0-387-35608-2.

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Book chapters on the topic "In-network computing"

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Parker, J. D. "Synchronization in Network Protocols." In Workshops in Computing, 344–67. London: Springer London, 1990. http://dx.doi.org/10.1007/978-1-4471-3534-0_17.

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Arsiwalla, Xerxes D., and Paul Verschure. "Computing Information Integration in Brain Networks." In Advances in Network Science, 136–46. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28361-6_11.

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Gray, Paul, Alan Krantz, Soeren Olesen, and Vaidy Sunderam. "Advances in heterogeneous network computing." In Recent Advances in Parallel Virtual Machine and Message Passing Interface, 83–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0056562.

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Dwivedi, Rinky, and Koyel Datta Gupta. "Green Computing at a Glance." In Green Computing in Network Security, 1–8. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003097198-1.

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Monien, B., and H. Sudborough. "Embedding one Interconnection Network in Another." In Computing Supplementum, 257–82. Vienna: Springer Vienna, 1990. http://dx.doi.org/10.1007/978-3-7091-9076-0_13.

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Mayes, K. R., J. Bridgland, S. Quick, and A. Nisbet. "Network performance in arena." In High-Performance Computing and Networking, 1007–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61142-8_684.

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Nanda Kishore, M. S., S. K. V. Jayakumar, G. Satya Reddy, P. Dhavachelvan, D. Chandramohan, and N. P. Soumya Reddy. "Web Service Suitability Assessment for Cloud Computing." In Trends in Network and Communications, 622–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22543-7_64.

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Dongarra, Jack, Piotr Luszczek, Felix Wolf, Jesper Larsson Träff, Patrice Quinton, Hermann Hellwagner, Martin Fränzle, et al. "Systems Biology, Network Inference in." In Encyclopedia of Parallel Computing, 1997–2002. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_466.

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Chen, Yu, Kun Liu, and Ziqian Zhang. "Research on Network Optimization and Network Security in Power Wireless Private Network." In Advances in Intelligent Systems and Computing, 1335–44. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8462-6_153.

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Qi, Jiaju, and Long Zhao. "Data Center Network in Cloud Computing." In Encyclopedia of Wireless Networks, 260–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_322.

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Conference papers on the topic "In-network computing"

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Cinelis, Gintaris, and Kestutis Scevinskas. "Education of Professional Computing Competence in University." In eCAADe 2004: Architecture in the Network Society. eCAADe, 2004. http://dx.doi.org/10.52842/conf.ecaade.2004.459.

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Cinelis, Gintaris, and Kestutis Scevinskas. "Education of Professional Computing Competence in University." In eCAADe 2004: Architecture in the Network Society. eCAADe, 2004. http://dx.doi.org/10.52842/conf.ecaade.2004.459.

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Su´rez, Juan-Luis, Fernando Sancho Caparrini, and Javier de la Rosa Perez. "The Art-Space of a Global Community: The Network of Baroque Paintings in Hispanic-America." In 2011 Second International Conference on Culture and Computing (Culture Computing). IEEE, 2011. http://dx.doi.org/10.1109/culture-computing.2011.17.

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Nikolaou, Stefanos, Christos Anagnostopoulos, and Dimitrios Pezaros. "In-network Predictive Analytics in Edge Computing." In 2019 Wireless Days (WD). IEEE, 2019. http://dx.doi.org/10.1109/wd.2019.8734267.

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Gelernter, Ben. "Help design challenges in network computing." In the 16th annual international conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/296336.296376.

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Jasti, Amarnath, Surya Mohapatra, Bhargav Potluri, and Ravi Pendse. "Cloud computing in Aircraft Data Network." In 2011 Integrated Communication, Navigation, Surveillance Conference (ICNS). IEEE, 2011. http://dx.doi.org/10.1109/icnsurv.2011.5935273.

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Sun, Xiaoping, Xiangfeng Luo, Jin Liu, Xiaorui Jiang, and Junsheng Zhang. "Semantics in Deep Neural-Network Computing." In 2015 11th International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2015. http://dx.doi.org/10.1109/skg.2015.42.

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Abdullah Antor, Md, Tasmin Jannat Tahsin, Minhas Abedin Sohan, and Syed Ahsanul Kabir. "High Performance Computing in Decentralized Network." In 2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI). IEEE, 2022. http://dx.doi.org/10.1109/sti56238.2022.10103309.

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Badgett, Mel, and Eric W. Cigan. "Simulation in a Network Computing Environment." In 1989 American Control Conference. IEEE, 1989. http://dx.doi.org/10.23919/acc.1989.4790222.

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Duarte, Elias P. "Leveraging In-Network Computing with Network Function Virtualization: Keynote." In 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2020. http://dx.doi.org/10.1109/aiccsa50499.2020.9316464.

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Reports on the topic "In-network computing"

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Sunderam, V. Enhancing functionality and performance in the PVM network computing system. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/285228.

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Sunderam, Vaidy. Enhancing Functionality and Performance in the PVM Network Computing System. Final project report. Office of Scientific and Technical Information (OSTI), December 1998. http://dx.doi.org/10.2172/760872.

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Sunderam, V. Enhancing functionality and performance in the PVM network computing system. Period 1 progress report. Office of Scientific and Technical Information (OSTI), August 1995. http://dx.doi.org/10.2172/93753.

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Renard, Kenneth D., James R. Feight, Michael Amabile, and James Adametz. High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: Introduction. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ada622738.

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Renard, Kenneth D., Joseph D. Rivera, James R. Adametz, and Jordan R. Franssen. High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: Data Marshalling. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ada621309.

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Panneton, Brian, Brendan Tauras, Christopher Wancowicz, and Sean Coyne. High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: Device Status Data. Fort Belvoir, VA: Defense Technical Information Center, August 2015. http://dx.doi.org/10.21236/ada626790.

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Panneton, Brian, and James Adametz. High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: HPC Data Reduction Framework. Fort Belvoir, VA: Defense Technical Information Center, August 2015. http://dx.doi.org/10.21236/ada626053.

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Beyeler, Walter Eugene, Thomas Frank, Jr Corbet, and Jacob A. Hobbs. A demand-driven, capacity-constrained, adaptive algorithm for computing steady-state and transient flows in a petroleum transportation network. Office of Scientific and Technical Information (OSTI), November 2012. http://dx.doi.org/10.2172/1055878.

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Valdez, Luis, Miltos Alamaniotis, and Alexander Heifetz. Anomaly Detection in Gamma Spectra Using Hopfield Neural Network with B-SAT and Grover’s Algorithm on a Quantum Computing Simulator. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1894587.

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Markova, Oksana, Serhiy Semerikov, and Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, May 2018. http://dx.doi.org/10.31812/0564/2250.

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Abstract:
The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the special course “Foundations of Mathematic Informatics” are shown. The program code was presented in a CofeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network`s weights, etc. The features of the Kolmogorov–Arnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.
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