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Exploring Spectrum Sharing Algorithms for 6G Cellular Communication Networks

Exploring Spectrum Sharing Algorithms for 6G Cellular Commun...

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international conference on computing and networking Technology (ICCNT)

作者： Ashutosh Pattanaik Shweta Zahid Ahmed Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Pharmacy ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

6G cell verbal exchange networks might be the successor to 5G networks, and their success relies heavily upon optimization measures that are enabled through the exploration of spectrum-sharing algorithms (SSAs). SSAs are hard and fast techniques implemented to maximize machine assets, performance metrics, and operational performance. That is performed by way of lowering interference, permitting spectrum contextualization, and improving the sharing of underutilized and congested spectrum. For 6G verbal exchange networks, a heightened attention on advanced database storage, distributed computing, and interference avoidance techniques must be taken a good way to ensure the maximum green use of be had spectrum. This paper explores a spread of SSAs for 6G mobile communication networks, consisting of dynamic spectrum get admission to, non-orthogonal more than one get right of entry to, and direct tool-enabled opportunistic spectrum usage. We evaluate current research that has been implemented to the improvement of SSAs and offer insight into future demanding situations and programs inside the subject. Furthermore, we talk about the significance of massive records and systems getting to know in the creation of SSA algorithms, providing capability enhancements to the ever-changing 6G communique networks. Standard, the usage of SSAs in 6G verbal exchange networks can make certain the most effective and green usage of be had spectrum, at the same time as additionally imparting extraordinary stages of aid flexibility

关键词： 6G mobile communication Wireless communication Measurement distributed databases Interference Oral communication Security Wireless fidelity Standards Optimization

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ADSwitch: Adaptive CPU Scheduling for distributed Systems

ADSwitch: Adaptive CPU Scheduling for Distributed Systems

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Yidong Zhong Yang Ao Dingding Li Jianguo Li Deze Zeng School of Computer Science South China Normal University Guangzhou Guangdong China School of Computer Science China University of Geosciences Wuhan Hubei China

In traditional CPU scheduling systems, it is challenging to customize scheduling policies for datacenter workloads. Therefore, distributed cluster managers can only perform coarse-grained job scheduling rather than fine-grained CPU scheduling, although the latter is also critical to application performance in the clusters. Some scheduling systems have been proposed to facilitate the implementation and deployment of custom policies. However, these systems neither adequately capture the resource requirements of workloads nor cooperatively manage the CPU scheduling of distributed nodes, thus may failing to fully utilize CPU resources in the clusters. To tackle these issues, we present ADSwitch, a distributed CPU scheduling system that manages CPU resources adaptively in datacenter clusters. We train an efficient workload classification model based on a recurrent neural network (RNN), which enables ADSwitch to dynamically identify the demand of the running workload and switch to an appropriate scheduling policy through a reliable online update mechanism. We implement ADSwitch and evaluate it in a distributed cluster with synthetic workloads. The experimental results demonstrate that ADSwitch achieves up to 33% lower tail latency and 28% higher throughput than the existing scheduling systems without workload characteristic awareness.

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An Efficient Model for Fake News Detection Using Transformer

An Efficient Model for Fake News Detection Using Transformer

引用

international conference on Parallel distributed and Grid computing (PDGC)

作者： Akash Abhishek Singh Manik Chandra Vineet Kansal Shiv Prakash Department of Computer Science & Engineering Institute of Engineering & Technology Lucknow Lucknow India Department of Computer Science & Engineering University of Allahabad Allahabad India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The public discourse is seriously threatened by the massive circulation of false information via social media and other platforms. This study investigates the potential of transformer models to effectively identify fake news, comparing their performance to mores Deep Learning (DL) and Machine Learning (ML) approaches. While DL and ML models have shown promise in this domain, they often struggle to capture the nuances of language context. Transformers, a more recent advancement indeep learning, excel at understanding the contextual relationships within text. By training transformer models on real-world datasets and evaluating their accuracy in identifying fake news, The objective of this study is to aid in the advancement of false news identification systems that are more dependable and precise. Through a critical analysis of transformer models in comparison to existing techniques, this study seeks to pave the way for future advancements in natural language processing (NLP) and the creation of a more trustworthy online information ecosystem.

关键词： Deep learning Training Support vector machines Accuracy Social networking (online) Biological system modeling Transformers Object recognition Fake news Context modeling

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Pairing Computations at the Edge and Cloud Servers to Improve Performance of Heterogeneous Systems

Pairing Computations at the Edge and Cloud Servers to Improv...

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international conference on Fog and Mobile Edge computing (FMEC)

作者： Md Raihan Uddin Abu Asaduzzaman Department of Electrical and Computer Engineering Wichita State University Wichita Kansas USA

ISBN: (数字)9798350366488

ISBN: (纸本)9798350366495

High speed internet and advanced networking technology contribute to having large number of various edge devices in heterogeneous edge-cloud systems. In conventional cloud computing systems, all device data is processed in the centralized cloud servers. The growing number of devices, i.e., increasing amount of device data, poses a challenge to the cloud servers to process data in a time- and energy-efficient manner. Studies show promise to reduce execution time and energy consumption by introducing collaborative edge-cloud computing paradigm. In this work, we study collaborative edge-cloud computing by introducing a framework of pairing the computations at edge and cloud resources to minimize execution time and energy consumption. First, the cloud servers (CSs) are made about 90% utilized by adjusting the device data i.e., computed data. Then, each edge server (ES) is optimized using $\mathbf{5 0 \%}$ or less of the previously generated device data i.e., cloud computed data. Finally, computations (i.e., device data) are distributed among the ESs and CSs, and performance is assessed to obtain the optimal pairing of computations. A heterogeneous system with one CS, two ESs, 10 edges, and 30 devices of five different types is modeled and simulated using VisualSim. Experimental results show that the proposed method helps reduce execution time and energy consumption by 90% and 56%, respectively. The proposed framework holds a promise for enhancing the scalability of heterogeneous systems, an avenue we intend to explore in our upcoming venture.

关键词： Performance evaluation Cloud computing Energy consumption Multi-access edge computing Scalability distributed databases Collaboration Throughput Energy efficiency Servers

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On Federated Multi-Armed Bandits for Mobile Social Networks

On Federated Multi-Armed Bandits for Mobile Social Networks

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international conference on distributed computing Systems

作者： Kazuya Sakai Takeshi Kitamura Min-Te Sun Wei-Shinn Ku Department of Electrical Engineering and Computer Science Tokyo Metropolitan University Tokyo Japan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Computer Science and Software Engineering Auburn University Auburn Alabama USA

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Multi-armed bandits (MABs) are widely used for decision making processes, in which an agent tries to maximize a long-term reward by balancing exploiting and exploring actions. In this paper, we are particularly interested in federated MABs for mobile social networks (MSNs), where a collection of agents learn action values by locally observing rewards and exchanging estimated action values with other agents. Our research differs from the existing federated MABs in the underlying network setting, such that agents can communicate with each other only at opportunistic contact events. To this end, we first design the weighted-connectivity (WC) centrality to quantify the importance of agents in an MSN, and then, we propose the weighted-connectivity upper confidence bound (WC-UCB) algorithm for the MSN contexts. The key idea to reduce biases at each agent and to utilize limited opportunities of federated updates is to prioritize the estimates of action-value functions computed by the agents with a high WC centrality. In addition, the performance bound in terms of the cumulative regret is analyzed. The performance of the proposed algorithm is evaluated by simulations using real mobility traces and the results demonstrate that our WC-UCB outperforms the state-of-the-art algorithms in terms of the average reward and the cumulative regret.

关键词： Social networking (online) Simulation Computational modeling Decision making Optical wavelength conversion Complexity theory distributed computing

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DKWS: A distributed System for Keyword Search on Massive Graphs (Extended Abstract)

DKWS: A Distributed System for Keyword Search on Massive Gra...

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international conference on Data Engineering

作者： Jiaxin Jiang Byron Choi Xin Huang Jianliang Xu Sourav S Bhowmick School of Computing National University of Singapore Singapore Department of Computer Science Hong Kong Baptist University Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Addressing the complexities of querying unstructured graphs such as knowledge graphs and social networks, this paper introduces D KWS, a novel distributed keyword search system. Leveraging a monotonic property, we ensure correct parallelization of our advanced keyword search algorithm, which incorporates tight pruning bounds and is divided into monotonic backward and forward search phases. The system is further augmented by the notify-push paradigm and the PINE programming model, facilitating asynchronous communication and preemptive searches to mitigate staleness in distributed environments. Extensive experiments on real-world datasets demonstrate DKWS's performance advantage, being up to two orders of magnitude faster and incurring 7.6 times lower communication costs than the existing systems.

关键词： Asynchronous communication Costs Social networking (online) Keyword search Knowledge graphs Programming Data engineering

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Scalable Server Location for distributed Interactive Applications under Budget Constraints 4

Scalable Server Location for Distributed Interactive Applica...

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4th international Balkan conference on Communications and networking, BalkanCom 2021

作者： Tsipis, Athanasios Komianos, Vasileios Ionian University Dept. of Digital Media and Communication Kefalonia Greece Ionian University Dept. of Audio and Visual Arts Corfu Greece

ISBN: (纸本)9781665402583

Mobile Edge computing (MEC) has emerged as a popular paradigm for the support of distributed interactive applications (DIAs), promising continuous service provision and availability to mobile users, that is otherwise not easily attainable under the centralized cloud-only model. Yet, in order to alleviate the app provider from unnecessary capital expenditure, a judicious location of the edge servers must be adopted, that can effectively cope with both the network demands and the deployment cost, and still abide by the budget constraints of the app provider. In this paper, we formally formulate the problem as a budget-constrained facility location problem. We then propose a distributed and scalable algorithm, that employs elastic server relocation, to address it. Trace-driven simulations under various MEC deployment scenarios verify the expected behavior and testify to the algorithm's accountability in obeying the budget limits and converging to a placement solution close to the optimal in finite time slots, rendering it suitable for DIAs ecosystems. © 2021 IEEE.

关键词： Mobile edge computing

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An Ensemble Learning-Based DDoS Attack Detection Considering the Impact of ICA

An Ensemble Learning-Based DDoS Attack Detection Considering...

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international conference on computing and networking Technology (ICCNT)

作者： Anique Jana Dipesh Mahato Raja Karmakar Sandipan Ghosal Twinkle Chatterjee Computer Science and Engineering Techno International NewTown Kolkata India Computer Science and Engineering Heritage Institute of Technology Kolkata India Information Technology Techno International NewTown Kolkata India

distributed denial of service (DDoS) attacks can cause considerable financial harm and operational concerns in the internet business era. Detecting such attacks necessitates overcoming two challenges: distinguishing normal packets from attack packets and consistently and effectively identifying distinct attack categories. As a result, an ideal detection system should be simple, reliable, and effective, with low false alarm rates. The goal of this paper is to give an analysis of machine learning (ML) methods for the detection of DDoS attacks using Ensemble Learning with and without implementing Independent Component Analysis (ICA). It improves the feature extraction, source separation, noise reduction, and data pre-processing, resulting in better performance and insights. The KDD-99 dataset is used to train and test ML models, using Logistic Regression, Random Forest, Decision Trees, K-Nearest Neighbors, and Gaussian Naive Bayes. They are used to extract data while categorizing the different types of input features during the training. The performance of the proposed mechanism is analyzed with and without the use of ICA. Results show that the DDoS attack is most successfully detected using the Random Forest classifier with an accuracy of 99.9670%. Moreover, ICA improves the performance of all ML models when compared to unsupervised learning techniques.

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Image Cryptography using AES-256 Algorithm

Image Cryptography using AES-256 Algorithm

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distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： Taniska Chandel Ninad Gangodkar Abhiraj Gautam Yogesh Lohumi Mohd Rehan Ghazi Ashtha Goyal Department of Computer Science and Engineering Graphic Era (Deemed to be) University Dehradun India School of Computing Graphic Era Hill University Dehradun India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Due to the advancement of technology, information security and user privacy have become a critical issue resulting in the use of various encryption techniques. Especially with the increasing use of social networking, it has become necessary to secure multimedia messages and prevent data threats and data manipulation. Image Cryptography focuses on the encryption of images and securing them from unauthorized access and protect their confidentiality and integrity. In order to encrypt and decode pictures, this paper focuses on the Advanced Encryption Standard Algorithm (AES) which is a symmetric encryption algorithm. Furthermore, it reflects on the efficiency of the algorithm by determining the security of the algorithm using various parameters like entropy, NPCR, MSE and Histogram analysis. For better comparison all five of AES modes are presented in this paper; Electronic Codebook (ECB), Cipher Block Chaining (CBC), Cipher Feedback (CFB), Output Feedback (OFB), and Counter (CTR). Overall, it can be concluded that AES is a secure algorithm that can be used for safe data transfer. Furthermore, through the result of the parameters used to determine the security of each mode of the AES algorithm, it can further be concluded that CTR mode of the algorithm is the most secure.

关键词： Ciphers Steganography Privacy Social networking (online) Entropy Encryption Security Cryptography Standards Output feedback

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Intent-based Management for the distributed computing Continuum

Intent-based Management for the Distributed Computing Contin...

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2023 IEEE international conference on Service-Oriented System Engineering (SOSE)

作者： Andrea Morichetta Nikolaus Spring Philipp Raith Schahram Dustdar Distributed Systems Group TU Wien

Managing digital and connected systems has become increasingly challenging in the past decade due to their scale and complexity. A new perspective is required to manage these systems, considering the infrastructure and components from edge to cloud, i.e., in the distributed computing continuum. Serverless computing offers improved scalability and cost efficiency, but balancing and coordinating serverless systems remain complex. Intent-based systems, popular in networking, can provide a solution by translating stakeholder inputs into actions that meet Service Level Objectives (SLOs). Their application in the computing continuum can be highly beneficial, but it has yet to be deeply explored. To bridge this gap, we propose a methodology for deploying an intent-based system for the computing continuum. We implement an architectural framework leveraging the serverless paradigm. Furthermore, we focus on defining and implementing the main components for translating the management requirements into actions executed by serverless functions inspired by a three-layer model. Through a Proof of Concept (PoC) deployed in Amazon's AWS cloud and detailed simulations, we showcase how such an approach can resolve conflicts in a complex system, i.e., balancing efficiency and availability. Our work aims to contribute to effectively managing the computing continuum and highlight the potential of intent-based systems in this domain. The experiments' results show our framework's ability to make appropriate scaling decisions, fulfilling both objectives.

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