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MPDP: A Probabilistic Architecture for Microservice Performance Diagnosis and Prediction

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computer Systems Science & Engineering 2024年第5期48卷 1273-1299页

作者： Talal H.Noor Department of computer Science College of Computer Science and EngineeringTaibah UniversityMadinah42353Saudi Arabia

In recent years,container-based cloud virtualization solutions have emerged to mitigate the performance gap between non-virtualized and virtualized physical ***,there is a noticeable absence of techniques for predicting microservice performance in current research,which impacts cloud service users’ability to determine when to provision or de-provision *** microservice performance poses challenges due to overheads associated with actions such as variations in processing time caused by resource contention,which potentially leads to user *** this paper,we propose,develop,and validate a probabilistic architecture named Microservice Performance Diagnosis and Prediction(MPDP).MPDP considers various factors such as response time,throughput,CPU usage,and othermetrics to dynamicallymodel interactions betweenmicroservice performance indicators for diagnosis and *** experimental data fromourmonitoring tool,stakeholders can build various networks for probabilistic analysis ofmicroservice performance diagnosis and prediction and estimate the best microservice resource combination for a given Quality of Service(QoS)*** generated a dataset of microservices with 2726 records across four benchmarks including CPU,memory,response time,and throughput to demonstrate the efficacy of the proposed MPDP *** validate MPDP and demonstrate its capability to predict microservice *** compared various Bayesian networks such as the Noisy-OR Network(NOR),Naive Bayes Network(NBN),and Complex Bayesian Network(CBN),achieving an overall accuracy rate of 89.98%when using CBN.

关键词： Cloud computing microservices monitoring performance QoS diagnosis prediction Bayesian network

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

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computers, Materials & Continua 2024年第2期78卷 2557-2573页

作者： Dayu Xu Jiaming Lu Xuyao Zhang Hongtao Zhang college of Mathematics and computer Science Zhejiang A&F UniversityHangzhou311300China college of Economics and Management Zhejiang A&F UniversityHangzhou311300China

Data stream clustering is integral to contemporary big data ***,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current *** paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the *** original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior *** the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further *** work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat *** arrival,this ensures that new data points interact solely with clusters in very close *** significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some *** apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA *** results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.

关键词： Data stream clustering TEDA KD-tree scapegoat tree

NeurDB: an AI-powered autonomous data system

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Science China(Information Sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore college of computer Science and Technology Zhejiang University Department of computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

A nano-plasmonic HMIM waveguide based concurrent dual-band BPF using circular ring resonator

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Optoelectronics Letters 2024年第3期20卷 152-156页

作者： Miriyala Sridhar Surendra Kumar Bitra TS N Murthy Koppireddi Padmaraju Department of Electronics and communication Engineering Koneru Lakshmaiah Education FoundationGunturIndia Astra Microwave Products Ltd. HyderabadIndia Department of Electronics and communication Engineering JNTU-GV College of EngineeringVizianagaramIndia Department of Electronics and communication Engineering Jawaharlal Nehru Technological University KakinadaKakinadaIndia

This article analyzes the transmission line characteristics of plasmonic hybrid metal insulator metal(HMIM) wave-guide, circular ring resonator(CRR) based dual-band band-pass filters with two transmission poles in both pass-bands in the optical regime using coupled line feed. The transmission line characteristics of an HMIM waveguide, such as characteristic impedance(Z_(PV)), effective refractive index(n_(eff)) and propagation length(L_(spp)), have been obtained by using full wave simulation.

关键词： waveguide resonator circular

Digital twin driven and intelligence enabled content delivery in end-edge-cloud collaborative 5G networks

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Digital communications and Networks 2024年第2期10卷 328-336页

作者： Bo Yi Jianhui Lv Xingwei Wang Lianbo Ma Min Huang college of computer Science and Engineering Northeastern UniversityShenyang110169China Pengcheng Lab. Shenzhen518055China college of Software Northeastern UniversityShenyang110819China college of Information Science and Engineering Northeastern UniversityShenyang110819China

The rapid development of 5G/6G and AI enables an environment of Internet of Everything(IoE)which can support millions of connected mobile devices and applications to operate smoothly at high speed and low ***,these massive devices will lead to explosive traffic growth,which in turn cause great burden for the data transmission and content *** challenge can be eased by sinking some critical content from cloud to *** this case,how to determine the critical content,where to sink and how to access the content correctly and efficiently become new *** work focuses on establishing a highly efficient content delivery framework in the IoE *** particular,the IoE environment is re-constructed as an end-edge-cloud collaborative system,in which the concept of digital twin is applied to promote the *** on the digital asset obtained by digital twin from end users,a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention(TPA)enabled Long Short-Term Memory(LSTM)***,the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning(RL)***,a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing *** experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate,the average throughput,the successful content delivery rate and the average routing overhead.

关键词： Digital twin IoE Content delivery Caching Routing

UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of computer Science Air UniversityIslamabad44000Pakistan Department of computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

Node Sociability Based Intelligent Routing for Post-Disaster Emergency Networks

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China communications 2024年第8期21卷 104-114页

作者： Li Jiameng Xiong Xuanrui Liu Min Amr Tolba School of communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Graduate School of Information Production and SystemsWaseda UniversityFukuoka 808-0135Japan School of communications and Information Engineering Chongqing College of Mobile CommunicationChongqing 401520China computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia

In a post-disaster environment characterized by frequent interruptions in communication links,traditional wireless communication networks are *** the“store-carry-forward”mechanism characteristic of Delay Tolerant Networks(DTNs)can transmit data from Internet of things devices to more reliable base stations or data centres,it also suffers from inefficient data transmission and excessive transmission *** address these challenges,we propose an intelligent routing strategy based on node sociability for post-disaster emergency network ***,we introduce an intelligent routing strategy based on node intimacy,which selects more suitable relay nodes and assigns the corresponding number of message copies based on comprehensive utility ***,we present an intelligent routing strategy based on geographical location of nodes to forward message replicas secondarily based on transmission utility ***,experiments demonstrate the effectiveness of our proposed algorithm in terms of message delivery rate,network cost ratio and average transmission delay.

关键词： delay tolerant networks Internet of things node sociability routing strategy

Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf communication and Healthcare

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computers, Materials & Continua 2024年第1期78卷 127-144页

作者： Khursheed Aurangzeb Khalid Javeed Musaed Alhussein Imad Rida Syed Irtaza Haider Anubha Parashar Department of computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia Department of computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France Department of computer Science and Engineering Manipal University JaipurJaipur303007India

Hand gestures have been used as a significant mode of communication since the advent of human *** facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free *** technology is pivotal in healthcare and communication for the deaf *** significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature ***,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and ***,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer *** proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,*** results demonstrate the effectiveness of CNN as a promising HGRoc *** findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

关键词： computer vision deep learning gait recognition sign language recognition machine learning

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree