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Architectural Vision for Quantum computing in the Edge-cloud Continuum

Architectural Vision for Quantum Computing in the Edge-Cloud...

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2nd IEEE international conference on Quantum Software (IEEE QSW)

作者： Furutanpey, Alireza Barzen, Johanna Bechtold, Marvin Dustdar, Schahram Leymann, Frank Raith, Philipp Truger, Felix TU Vienna Distributed Syst Grp Vienna Austria Univ Stuttgart Inst Architecture Applicat Syst Stuttgart Germany

ISBN: (纸本)9798350304794

Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs will soon be possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources. Hence, this work presents an architecture for Quantum computing in the edge-cloud continuum. We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs. We describe how warm-starting allows defining workflows that exploit the hierarchical resources spread across the continuum. Then, we introduce a distributed inference engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements that incur the highest degree of heterogeneity. We propose solutions focusing on classical layer partitioning and quantum circuit cutting to demonstrate the potential of utilizing classical and quantum computation across the continuum. To evaluate the importance and feasibility of our vision, we provide a proof of concept that exemplifies how extending a classical partition method to integrate quantum circuits can improve the solution quality. Specifically, we implement a split neural network with optional hybrid QNN predictors. Our results show that extending classical methods with QNNs is viable and promising for future work.

关键词： Quantum computing Edge computing Compute Continuum Split computing Circuit Cutting Task Partitioning DNN Partitioning Classical-Quantum Hybrid Machine Learning Quantum Neural Networks Warm-Starting

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Real-Time Task Scheduling and Resource Scheduling in Fog computing using Deep Learning Techniques 2

Real-Time Task Scheduling and Resource Scheduling in Fog Com...

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2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Ranjan, Vivek Sharma, Lalit Noida Institute of Engineering and Technology Department of Cs Greater Noida India Galgotias University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350347456

cloud capabilities may now be accessed from the network's edge thanks to fog computing. In order to fulfill the needs of organizations that have particularly high standards for the quality of the services they provide, it is essential to choose the most suitable site for the execution of applications. So, a cloud service has to have a dependable task scheduling system in order to establish in which geographic zone a certain piece of information may be put into action. We provide two different schedulers that are able to maximize work scheduling by using the resources of cloud computing and fog computing respectively. Both planners are grounded on nonlinear mathematical programming as their primary methodology. When deciding which processors should be assigned a certain work, the schedulers do not use any rules that have already been established;rather, they take into consideration a range of characteristics, such as availability and performance, when making their choices. The recommended algorithms usually perform much better than the scheduling problem that are already in use, such as the Randomised and Round Robins approaches, without sacrificing the quality of the service provided, as shown by the numerical solution. This research aims to enhance the total effectiveness of execution of tasks in IoT systems by consciously selecting certain real-time activities to carry out at the fog layer. This is done with the intention of increasing overall efficiency. We present a way for scheduling activities in a cloud-fog computing environment that takes use of fuzzy logic to split up work between the cloud and fog layers. This approach can be found in our paper "A Technique for Creating Schedules in a cloud-Fog computing Environment. The approach selects appropriate processing units to carry out the designated tasks in the fog nodes that include homogeneous devices, taking into consideration the requirements of the job (such as compute, storage, and bandwidth), as well as

关键词： Fog computing

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Enhancing cloud Storage Security with Intrusion Detection System using CNN and Grey Wolf Optimization Algorithm 2

Enhancing Cloud Storage Security with Intrusion Detection Sy...

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2nd international conference on Sustainable computing and Data Communication Systems, ICSCDS 2023

作者： Eswari, G. Monica, G.K. Deepak, V. Sunil, K.M. Kumar, B. Prem Velammal Institute of Technology Department of Information Technology Panchetti Chennai India

ISBN: (纸本)9781665491990

cloud computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. This research article proposes a novel approach for enhancing the security of cloud storage systems by developing an Intrusion Detection System (IDS) that utilizes feature extraction, pre-processing, and advanced analysis tools. Specifically, the proposed IDS leverage a Convolutional Neural Network (CNN) model and a grey wolf optimization algorithm to detect potential cyber-attacks on cloud storage. The research work focuses on extracting relevant features from the data and performing preprocessing to remove any irrelevant or noisy data points. The CNN model is then trained using the pre-processed data, while the grey wolf optimization algorithm is engaged to optimize the runtime performance of the IDS. The outcome of the proposed approach demonstrates its ability to detect various types of cyber-attacks with high accuracy and efficiency, which can help to improve the security of cloud storage systems. By measuring the effectiveness and efficiency of the algorithm, it can be determined whether GWO is a suitable optimization algorithm for intrusion detection in cloud security. © 2023 IEEE.

关键词： Efficiency

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Topological Dependencies in Deep Learning for Mobile Edge: distributed and Collaborative High-Speed Inference 2

Topological Dependencies in Deep Learning for Mobile Edge: D...

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2nd international conference on Electronics and Renewable Systems, ICEARS 2023

作者： Abd Algani, Yousef Methkal Kumar, A Suresh Ala Walid, Md. Abul Balu, S. Velayutham, Priya Sasi Kumar, A. The Arab Academic College for Education in Israel-Haifa Department of Mathematics Israel Rathinam Technical Campus Department of Computer Science and Engineering Tamil Nadu Coimbatore India Khulna9203 Bangladesh Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tamil Nadu Namakkal India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Inurture Education Solutions Pvt Ltd Bangalore India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Karnataka State India

ISBN: (纸本)9798350346640

Edge computing is now widely deployed across the globe. Although the Internet serves as the foundation for edge computing, the actual usefulness of this type of computing is seen when it is combined with the process of obtaining data from sensors and deriving relevant information from that data. It is predicted that in the not-too-distant era, most edge devices will be equipped with intelligent systems powered by deep learning. Unfortunately, in order to train, methods based on deep learning require a significant quantity of data of a high standard, and these methods are quite costly in terms of the amount of processing, memory, and power that they use. distributed deep neural networks, or DDNNs, are something suggested by using distributed computing hierarchies. The cloud, fog, and devices make up these tiers. Although a DDNN can facilitate the interpretation of a DNN in the cloud, it also allows for interpretation to be carried out quickly and precisely on the edges and on end devices by making use of shallow parts of the neural network. With the help of a scalable cloud-based infrastructure, a DDNN can expand both in terms of volume of its neural network and the number of users it serves around the world. For DNN applications, the distributed nature of DDNNs results in improvements to sensor fusion, fault tolerance in the system, and data privacy. In order to implement DDNN, first the portions of a DNN are mapped onto a dispersed computing structure. By learning both components together, the devices' need become limited for both connectivity and energy while the model provided the value of the selected features in the cloud. The final product includes provision for instinctive sensor fusion as well as fault tolerance that has been built directly into the system. This study demonstrates as a proof of concept that a DDNN may make use of the geographical variety of sensors to improve the accuracy of object detection and lower the cost of communication. The suggested

关键词： Edge computing

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Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised cloud computing Approach 2

Leveraging Deep Autoencoders for Security in Big Data Framew...

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2nd international conference on distributed computing and Optimization Techniques, ICDCOT 2024

作者： Roy, Sourav Nijaguna, G.S. Shankar, Smitha Sharath Adnan, Myasar Mundher Umaeswari, P. Department of Electronics and Communication Engineering Bangalore India S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India The Islamic University Najaf Iraq R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798350382952

Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. © 2024 IEEE.

关键词： Cryptography

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Application of cloud computing technology in computer network security storage 2

Application of cloud computing technology in computer networ...

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2nd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Derong, Meng Tianjin University of Technology Tianjin China

ISBN: (纸本)9781665442763

This article will start from the cloud computing technology, understand the current situation of modern network security management, aiming at the key technology of cloud computing technology in network security, and put forward effective and feasible security protection suggestions, strengthening the security of computer network storage. With the gradual popularization of the application of Computer Network Information Technology, cloud computing technology is now widely used in computer, Internet of things and other related fields, not only to facilitate people's lives, but also to meet the needs of work, it makes the work more convenient, efficient and fast, but it also makes the network security risk increase constantly. Research the application of cloud computing technology in the computer network security storage, it is significant to reduce the network security risk of cloud computing technology application. © 2022 IEEE.

关键词： Network security

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Mitigating distributed Denial of Service (DDoS) Attacks in cloud Networks Using Neural Networks 2

Mitigating Distributed Denial of Service (DDoS) Attacks in C...

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2nd international conference on Advanced computing and Communication Technologies, ICACCTech 2024

作者： Singh, Jagendra Ali, Jabir Sharma, Preeti Kumar, Vinish Sharma, Meenakshi Singh, Ramendra School of Computer Science Engineering & Technology Bennett University Greater Noida India Ghaziabad India Ghaziabad India Inderprastha Engineering College Department of Electronics & Communication Engineering Ghaziabad India Ghaziabad India

ISBN: (纸本)9798331519056

The research aims to prove the effectiveness of three neural network models the convolutional neural network (CNN) and Long Short-Term Memory (LSTM) Hybrid, Variational Autoencoders, and Bidirectional LSTM in neutralizing DDoS attacks on online shopping websites. The models are ranked using critical performance metrics relevant to cybersecurity. Firstly, the CNN-LSTM Hybrid has demonstrated a very high True positive rate (TPR), with a reading of 0.95, as well as a low False positive rate (FPR) of 0.03, resulting in a precision of 0.93 and a recall of 0.95. The readings show that the model is accurate in distinguishing between regular traffic and DDoS attacks, having an overall Fl-Score of 0.94. On the anomaly detection-based Variational Autoencoders (VAEs), the TPR was 0.92, the FPR was 0.05, the precision was 0.90, and the recall 0.92. The bidirectional LSTM model returned a TPR of 0.94 and an FPR of 0.04, and both precision and recall were also brought to 0.92 with an Fl-score of 0.93. These models improve online shopping website security from cyberattacks by preventing data availability and consistency. As DDoS attack discovery is a major step for continuing institutions, the designs serve administrators and greatly protect online buyers. © 2024 IEEE.

关键词： Convolutional neural networks

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Securing Account Hijacking Security Threats in cloud Environment Using Artificial Neural Networks 1

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2nd international conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Devi, Renu Gill, Sumeet Narwal, Ekta Department of Mathematics M.D. University Rohtak Rohtak India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

cloud computing has become increasingly popular and successful due to recent technological advances. It offers storage and computing resources as needed. cloud computing provides financial advantages for users. But, most cloud service provider companies’ funds are used in cloud data security. In the present work, the threats related to cloud account hijacking have been illustrated and the model to secure from this threat has been presented. We have used Feed-forward Back-Propagation Algorithm for storage in cloud environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feedforward neural networks

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Development of Personalized Mobile Information Service Platform of University Library Based on Multi-Core Processor and cloud computing Platform 2

Development of Personalized Mobile Information Service Platf...

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2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Mishra, Saket Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350347456

The library can quickly create and manage its own data center. University library will allow to maintain greater control over applications and data storage that contain sensitive private information about students. Using multi-core processors are considered the design of future product microprocessors. Not only is it a solution for Central Processing Unit (CPU) speeds, also reduces consumption because many cores at lower frequencies collectively produce less to the cores with their total computing. Existing methods are K-Nearest Neighbor (KNN) and Principle Component Analysis (PCA) is an information resources have gradually shifted from digital analog analysis the Digital libraries are beginning to enter the computing increased the cost and time. cloud computing Digital Information Resources Integration Library is suitable for architecture model applications, to provide a new solution for digital library, construction intensity and digital resource sharing. To overcome the issue in this work proposed Sequence Recurrent Neural Network (SRNN) using mobile library services, some of which can be used by various university libraries and other services already find help libraries that gather their information in a fast and time-limited way without external and time restrictions. The value of this is Help of University libraries can identify and adapt mobile processors. With these mobile initiatives, libraries can offer a wide range of mobile services to interested users. It has been shown by test results that adding processor cores to the data center increases system performance, reduces consumption, and has other benefits. © 2023 IEEE.

关键词： Program processors

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Efficient Task Scheduling in cloud Environment Based on Hyper Min Max Task Scheduling 2

Efficient Task Scheduling in Cloud Environment Based on Hype...

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2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Gnanaprakasam, D. Mohanraj, M. Srinivas, T. Aditya Sai Bhaggiaraj, S. Baskaran, J. Sivankalai, S. Dr. Mahalingam College of Engineering and Technology Department of Electrical and Electronics Engineering Pollachi India Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India Ashoka Women's Engineering College Department of Computer Science and Engineering India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Krishna College of Engineering and Technology Department of Mechanical Engineering Coimbatore India Chennai India

ISBN: (纸本)9798350347456

The growing field of cloud computing deals with large tasks for processing resources. from the application point of view, the research on task scheduling mechanism of data transfer in large-scale cloud computing environment is relatively poor. Unbalanced scheduling leads to traffic overload, energy loss, and failure of hardware control. In addition, residential appliances do not consider delay reduction in power consumption. Hence, Internet of Things (IoT) dominates the current trends in the Internet. The large number of things (things) associated with the Internet creates a large amount of information that requires a lot of effort and work preparation to make it valuable. To resolve this problem, we propose a Hyper Min max task scheduling (HMMTS) based in cascade shrink priority (CSP) to allocate task to optimize the scheduling. With intent a Changeover Load Balancer (CLB) and The Preemptive Flow Manager (PFM) is responsible for the application of load balancing strategy based on the mixed load balancing algorithm improves the task allocation better to balance load to improve the response time. Experimental results have been demonstrated with respect to better load balancing, lower power rate, and time consumption rate in both phase and random uniform propagation. Simulated results performance of this process can reduce data processing time and achieve load neutralization. © 2023 IEEE.

关键词： cloud computing

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