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Energy Aware Task Scheduling of IoT Application Using a Hybrid Metaheuristic Algorithm in Cloud Computing 认领 引用
1
作者 Ahmed Awad Mohamed Eslam Abdelhakim Seyam +4 位作者 Ahmed R.Elsaeed Laith Abualigah Aseel Smerat Ahmed M.AbdelMouty Hosam E.Refaat 《Computers, Materials & Continua》 SCIE EI 2026年第3期1786-1803,共18页
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul... In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption. 展开更多
关键词 Energy-efficient tasks internet of things(IoT) cloud fog computing artificial ecosystem-based optimization salp swarm algorithm cloud computing
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Interdisciplinary knowledge fusion and practice based on cloud computing technology 认领 引用
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作者 Haowen Liu Bing Li +1 位作者 Fu Lin Jiping Liu 《计算机教育》 2026年第6期206-212,共7页
Nowadays,cloud computing,big data,artificial intelligence,software engineering,and other technologies have developed rapidly.These technologies are widely used in all walks of life and are gradually developing towards... Nowadays,cloud computing,big data,artificial intelligence,software engineering,and other technologies have developed rapidly.These technologies are widely used in all walks of life and are gradually developing towards integration.This paper introduces how to fuse the experiment cases related to big data,Dev Ops or artificial intelligence into the course of cloud computing platform and technology in Wuhan University,practice them with cloud computing,and then improve the course year by year,so as to improve the course and thereby promote the cultivation of high-end composite talents. 展开更多
关键词 Interdisciplinary knowledge fusion Cloud computing Big data DevOps Artificial intelligence
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Discussion on Data Privacy Protection Technologies in Cloud Computing Environment 认领 引用
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作者 Yixuan Dou 《Journal of Electronic Research and Application》 2026年第3期73-85,共13页
Cloud computing offers numerous benefits,including scalability,cost-effectiveness,and accessibility,making it an attractive solution for various organizations.However,the migration of sensitive data to cloud environme... Cloud computing offers numerous benefits,including scalability,cost-effectiveness,and accessibility,making it an attractive solution for various organizations.However,the migration of sensitive data to cloud environments raises significant concerns regarding data privacy protection.This review paper provides a comprehensive overview of data privacy protection technologies in cloud computing.It begins by outlining the historical evolution of cloud computing and associated privacy challenges.The paper then delves into two core themes:access control mechanisms and data encryption techniques.Access control is explored in terms of attribute-based access control(ABAC),role-based access control(RBAC),and break-the-glass mechanisms.Encryption techniques are analyzed by covering homomorphic encryption,differential privacy and federated learning.The paper then offers a comparative analysis of these technologies,highlighting their strengths,weaknesses,and trade-offs in the cloud environment.Finally,the paper addresses the existing challenges and discusses future research directions,including the integration of artificial intelligence for enhanced privacy protection and the development of more robust and efficient encryption methods.This review aims to provide researchers and practitioners with a clear understanding of the current state-of-the-art in data privacy protection technologies for cloud computing and to identify potential avenues for future innovation. 展开更多
关键词 Cloud computing Data privacy Access control Encryption Homomorphic encryption Differential privacy Federated learning
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To what extent is cloud computing changing traditional IT in small and medium-sized enterprises,and what challenges and opportunities does it create? 认领 引用
4
作者 Zhengshangfeng Zhan 《Journal of Fintech and Business Analysis》 2026年第2期1-14,共14页
This study explores how cloud computing influences operational efficiency and cost performance in Small and Medium-sized Enterprises(SMEs)compared to traditional IT systems.Under the digital transformation,SMEs face i... This study explores how cloud computing influences operational efficiency and cost performance in Small and Medium-sized Enterprises(SMEs)compared to traditional IT systems.Under the digital transformation,SMEs face increasing pressure to improve flexibility and efficiency,reduce costs,and respond quickly to market changes.Though revisit academic journals,industry reports,and case studies from 2013 to 2025 in a systematic way.According to the research,cloud computing can significantly lower investments during the early stage and costs of continuous maintenance while promoting scalability,operational agility,and collaboration.It also shows improved data analysis,product development,and market responsiveness.Case studies about Marais USA and SMEs in Kampala show that the benefits of cloud adoption mainly depend on technological standard,strategic countermeasure,and regional infrastructure,with noticeable differences between developed and developing areas.However,there are still a lot of risks that need to be taken seriously.Not only data security and privacy remain major points,but system reliability and cross-platform interoperability also cause technical risks and skills gaps and financial investment also limit SMEs'ability to fully utilize cloud technologies.Although emerging approaches such as edge-cloud computing paradigm,multi-cloud strategies,and green generative AI show opportunities to enhance efficiency,resilience,and sustainability,but still require careful implementation and adequate technical capacity.Ultimately,cloud computing can provide substantial benefits to SMEs in cost reduction and flexibility of operations.The proof is that cloud computing is more significant to enhance the operation of SMEs than the traditional IT system although there are differences on its impact based on enterprise size,region and digitally degree.SMEs must take a strategic and professional view of the use of cloud computing potential realization if they are to play to their full,with the support of policy and technological progress. 展开更多
关键词 cloud computing SMEs operational efficiency cost performance digital transformation
Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm 认领 引用 被引量：2
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作者 Jeng-Shyang Pan Na Yu +3 位作者 Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada 《Computers, Materials & Continua》 SCIE EI 2025年第2期2495-2520,共26页
The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resource... The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment. 展开更多
关键词 Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy
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Modified Neural Network Used for Host Utilization Predication in Cloud Computing Environment 认领 引用 被引量：1
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作者 Arif Ullah Siti Fatimah Abdul Razak +1 位作者 Sumendra Yogarayan Md Shohel Sayeed 《Computers, Materials & Continua》 SCIE EI 2025年第3期5185-5204,共20页
Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves ... Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets. 展开更多
关键词 Cloud computing datacenter virtual machine(VM) predication algorithm
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Hybrid Spotted Hyena and Whale Optimization Algorithm-Based Dynamic Load Balancing Technique for Cloud Computing Environment 认领 引用
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作者 N Jagadish Kumar R Praveen +1 位作者 D Selvaraj D Dhinakaran 《China Communications》 SCIE EI CSCD 2025年第8期206-227,共22页
The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is n... The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap. 展开更多
关键词 cloud computing load balancing Spotted Hyena Optimization Algorithm(SHOA) throughput Virtual Machines(VMs) Whale Optimization Algorithm(WOA)
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CBBM-WARM:A Workload-Aware Meta-Heuristic for Resource Management in Cloud Computing 认领 引用 被引量：1
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作者 K Nivitha P Pabitha R Praveen 《China Communications》 SCIE EI CSCD 2025年第6期255-275,共21页
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi... The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks. 展开更多
关键词 autonomic resource management cloud computing coot bird behavior model SLA violation cost workload
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Priority-Based Scheduling and Orchestration in Edge-Cloud Computing:A Deep Reinforcement Learning-Enhanced Concurrency Control Approach 认领 引用
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作者 Mohammad A Al Khaldy Ahmad Nabot +4 位作者 Ahmad Al-Qerem Mohammad Alauthman Amina Salhi Suhaila Abuowaida Naceur Chihaoui 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期673-697,共25页
The exponential growth of Internet of Things(IoT)devices has created unprecedented challenges in data processing and resource management for time-critical applications.Traditional cloud computing paradigms cannot meet... The exponential growth of Internet of Things(IoT)devices has created unprecedented challenges in data processing and resource management for time-critical applications.Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems,while pure edge computing faces resource constraints that limit processing capabilities.This paper addresses these challenges by proposing a novel Deep Reinforcement Learning(DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments.Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency.The framework introduces three key innovations:(1)a DRL-based dynamic priority assignmentmechanism that learns fromsystem behavior,(2)a hybrid concurrency control protocol combining local edge validation with global cloud coordination,and(3)an integrated mathematical model that formalizes sensor-driven transactions across edge-cloud architectures.Extensive simulations across diverse workload scenarios demonstrate significant quantitative improvements:40%latency reduction,25%throughput increase,85%resource utilization(compared to 60%for heuristicmethods),40%reduction in energy consumption(300 vs.500 J per task),and 50%improvement in scalability factor(1.8 vs.1.2 for EDF)compared to state-of-the-art heuristic and meta-heuristic approaches.These results establish the framework as a robust solution for large-scale IoT and autonomous applications requiring real-time processing with consistency guarantees. 展开更多
关键词 Edge computing cloud computing scheduling algorithms orchestration strategies deep reinforcement learning concurrency control real-time systems IoT
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SDVformer:A Resource Prediction Method for Cloud Computing Systems 认领 引用
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作者 Shui Liu Ke Xiong +3 位作者 Yeshen Li Zhifei Zhang Yu Zhang Pingyi Fan 《Computers, Materials & Continua》 SCIE EI 2025年第9期5077-5093,共17页
Accurate prediction of cloud resource utilization is critical.It helps improve service quality while avoiding resource waste and shortages.However,the time series of resource usage in cloud computing systems often exh... Accurate prediction of cloud resource utilization is critical.It helps improve service quality while avoiding resource waste and shortages.However,the time series of resource usage in cloud computing systems often exhibit multidimensionality,nonlinearity,and high volatility,making the high-precision prediction of resource utilization a complex and challenging task.At present,cloud computing resource prediction methods include traditional statistical models,hybrid approaches combining machine learning and classical models,and deep learning techniques.Traditional statistical methods struggle with nonlinear predictions,hybrid methods face challenges in feature extraction and long-term dependencies,and deep learning methods incur high computational costs.The above methods are insufficient to achieve high-precision resource prediction in cloud computing systems.Therefore,we propose a new time series prediction model,called SDVformer,which is based on the Informer model by integrating the Savitzky-Golay(SG)filters,a novel Discrete-Variation Self-Attention(DVSA)mechanism,and a type-aware mixture of experts(T-MOE)framework.The SG filter is designed to reduce noise and enhance the feature representation of input data.The DVSA mechanism is proposed to optimize the selection of critical features to reduce computational complexity.The T-MOE framework is designed to adjust the model structure based on different resource characteristics,thereby improving prediction accuracy and adaptability.Experimental results show that our proposed SDVformer significantly outperforms baseline models,including Recurrent Neural Network(RNN),Long Short-Term Memory(LSTM),and Informer in terms of prediction precision,on both the Alibaba public dataset and the dataset collected by Beijing Jiaotong University(BJTU).Particularly compared with the Informer model,the average Mean Squared Error(MSE)of SDVformer decreases by about 80%,fully demonstrating its advantages in complex time series prediction tasks in cloud computing systems. 展开更多
关键词 Cloud computing time series prediction DVSA SG filter T-MOE
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Energy Efficient and Resource Allocation in Cloud Computing Using QT-DNN and Binary Bird Swarm Optimization 认领 引用
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作者 Puneet Sharma Dhirendra Prasad Yadav +2 位作者 Bhisham Sharma Surbhi B.Khan Ahlam Almusharraf 《Computers, Materials & Continua》 SCIE EI 2025年第10期2179-2193,共15页
The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems.This research presents an innovative hybrid framework t... The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems.This research presents an innovative hybrid framework that combines a Quantum Tensor-based Deep Neural Network(QT-DNN)with Binary Bird Swarm Optimization(BBSO)to enhance resource allocation while preserving Quality of Service(QoS).In contrast to conventional approaches,the QT-DNN accurately predicts task-resource mappings using tensor-based task representation,significantly minimizing computing overhead.The BBSO allocates resources dynamically,optimizing energy efficiency and task distribution.Experimental results from extensive simulations indicate the efficacy of the suggested strategy;the proposed approach demonstrates the highest level of accuracy,reaching 98.1%.This surpasses the GA-SVM model,which achieves an accuracy of 96.3%,and the ART model,which achieves an accuracy of 95.4%.The proposed method performs better in terms of response time with 1.598 as compared to existing methods Energy-Focused Dynamic Task Scheduling(EFDTS)and Federated Energy-efficient Scheduler for Task Allocation in Large-scale environments(FESTAL)with 2.31 and 2.04,moreover,the proposed method performs better in terms of makespan with 12 as compared to Round Robin(RR)and Recurrent Attention-based Summarization Algorithm(RASA)with 20 and 14.The hybrid method establishes a new standard for sustainable and efficient administration of cloud computing resources by explicitly addressing scalability and real-time performance. 展开更多
关键词 Cloud computing quality of service virtual machine allocation deep neural network
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A Dynamic Workload Prediction and Distribution in Cloud Computing Using Deep Reinforcement Learning and LSTM 认领 引用
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作者 Nampally Vijay Kumar Satarupa Mohanty Prasant Kumar Pattnaik 《Journal of Harbin Institute of Technology(New Series)》 CAS 2025年第4期64-71,共8页
Maintaining high-quality service supply and sustainability in modern cloud computing is essential to ensuring optimal system performance and energy efficiency.A novel approach is introduced in this study to decrease a... Maintaining high-quality service supply and sustainability in modern cloud computing is essential to ensuring optimal system performance and energy efficiency.A novel approach is introduced in this study to decrease a system's overall delay and energy consumption by using a deep reinforcement learning(DRL)model to predict and allocate incoming workloads flexibly.The proposed methodology integrates workload prediction utilising long short-term memory(LSTM)networks with efficient load-balancing techniques led by deep Q-learning and Actor-critic algorithms.By continuously analysing current and historical data,the model can efficiently allocate resources,prioritizing speed and energy preservation.The experimental results demonstrate that our load balancing system,which utilises DRL,significantly reduces average response times and energy usage compared to traditional methods.This approach provides a scalable and adaptable strategy for enhancing cloud infrastructure performance.It consistently provides reliable and durable performance across a range of dynamic workloads. 展开更多
关键词 DRL LSTM cloud computing load balancing Q-Learning
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Enhancing Anomaly Detection in Cloud Computing Through Metaheuristics Feature Selection with Ensemble Learning Approach 认领 引用
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作者 Jansi Sophia Mary C Mahalakshmi K 《China Communications》 SCIE EI CSCD 2025年第8期168-182,共15页
Cloud computing(CC) provides infrastructure,storage services,and applications to the users that should be secured by some procedures or policies.Security in the cloud environment becomes essential to safeguard infrast... Cloud computing(CC) provides infrastructure,storage services,and applications to the users that should be secured by some procedures or policies.Security in the cloud environment becomes essential to safeguard infrastructure and user information from unauthorized access by implementing timely intrusion detection systems(IDS).Ensemble learning harnesses the collective power of multiple machine learning(ML) methods with feature selection(FS)process aids to progress the sturdiness and overall precision of intrusion detection.Therefore,this article presents a meta-heuristic feature selection by ensemble learning-based anomaly detection(MFS-ELAD)algorithm for the CC platforms.To realize this objective,the proposed approach utilizes a min-max standardization technique.Then,higher dimensionality features are decreased by Prairie Dogs Optimizer(PDO) algorithm.For the recognition procedure,the MFS-ELAD method emulates a group of 3 DL techniques such as sparse auto-encoder(SAE),stacked long short-term memory(SLSTM),and Elman neural network(ENN) algorithms.Eventually,the parameter fine-tuning of the DL algorithms occurs utilizing the sand cat swarm optimizer(SCSO) approach that helps in improving the recognition outcomes.The simulation examination of MFS-ELAD system on the CSE-CIC-IDS2018 dataset exhibits its promising performance across another method using a maximal precision of 99.71%. 展开更多
关键词 anomaly detection cloud computing ensemble learning intrusion detection system prairie dogs optimization
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Design and Optimization of Smart Factory Architecture Combining IoT and Cloud Computing 认领 引用
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作者 Yinghao Tang 《信息工程期刊（中英文版）》 2025年第1期23-29,共7页
The design and optimization of smart factory architectures integrating the Internet of Things(IoT)and cloud computing have emerged as a crucial factor in enhancing the efficiency and agility of modern manufacturing sy... The design and optimization of smart factory architectures integrating the Internet of Things(IoT)and cloud computing have emerged as a crucial factor in enhancing the efficiency and agility of modern manufacturing systems.In the context of Industry 4.0,smart factories leverage advanced technologies such as IoT,cloud computing,artificial intelligence(AI),and data analytics to enable real-time decision-making,predictive maintenance,and optimization of resources.This paper presents an overview of a smart factory architecture that combines IoT devices,cloud platforms,and data integration layers to create a flexible,scalable,and efficient manufacturing system.The proposed architecture is designed to enhance operational performance,energy efficiency,and resource management while ensuring system scalability and flexibility to meet changing market demands.Additionally,the paper discusses key optimization techniques,including edge computing,predictive maintenance,and cloud-based analytics,which contribute to the overall effectiveness of smart factory operations.The integration of these technologies promises to deliver significant improvements in productivity,cost savings,and sustainability in manufacturing environments. 展开更多
关键词 Smart Factory IoT Cloud Computing Predictive Maintenance Resource Management Edge Computing Data Analytics
Revolutionizing Learning:The Role of AI,IoT,and Cloud Computing in Smart Education 认领 引用
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作者 Chiweng Leng 《Journal of Contemporary Educational Research》 2025年第6期12-17,共6页
The rapid advancement of technology has paved the way for innovative approaches to education.Artificial intelligence(AI),the Internet of Things(IoT),and cloud computing are three transformative technologies reshaping ... The rapid advancement of technology has paved the way for innovative approaches to education.Artificial intelligence(AI),the Internet of Things(IoT),and cloud computing are three transformative technologies reshaping how education is delivered,accessed,and experienced.These technologies enable personalized learning,optimize teaching processes,and make educational resources more accessible to learners worldwide.This paper examines the integration of these technologies into smart education systems,highlighting their applications,benefits,and challenges,and exploring their potential to bridge gaps in educational equity and inclusivity. 展开更多
关键词 Artificial intelligence Internet of Things Cloud computing Smart education Personalized learning
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Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems 认领 引用
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作者 Ahyoung Lee Jui Mhatre +1 位作者 Rupak Kumar Das Min Hong 《Computers, Materials & Continua》 SCIE EI 2023年第1期435-452,共18页
Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applic... Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail. 展开更多
关键词 Mobile cloud computing hybrid mobile cloud computing load balancing healthcare solution
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Fuzzy Firefly Based Intelligent Algorithm for Load Balancing inMobile Cloud Computing 认领 引用
17
作者 Poonam Suman Sangwan 《Computers, Materials & Continua》 SCIE EI 2023年第1期1783-1799,共17页
This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits ... This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit. 展开更多
关键词 Cloud computing cloudlet mobile cloud computing fuzzy firefly load balancing makespan degree of imbalance
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Analysis of Hot Topics in Cloud Computing 认领 引用 被引量：5
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作者 Li Deyi Chen Guisheng Zhang Haisu 《ZTE Communications》 2010年第4期1-5,共5页
In the field of cloud computing, topics such as computing resource virtualization, differences between grid and cloud computing, relationship between high-performance computers and cloud computing centers, and cloud s... In the field of cloud computing, topics such as computing resource virtualization, differences between grid and cloud computing, relationship between high-performance computers and cloud computing centers, and cloud security and standards have attracted much research interest. This paper analyzes these topics and highlights that resource virtualization allows information services to be scalable, intensive, and specialized; grid computing involves using many computers for large-scale computing tasks, while cloud computing uses one platform for multiple services; high-performance computers may not be suitable for a cloud computing; security in cloud computing focuses on trust management between service suppliers and users; and based on the existing standards, standardization of cloud computing should focus on interoperability between services. 展开更多
关键词 cloud computing virtualization grid computing security of cloud computing standards of cloud computing
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MTBAC: A Mutual Trust Based Access Control Model in Cloud Computing 认领 引用 被引量：13
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作者 LIN Guoyuan WANG Danru +1 位作者 BIE Yuyu LEI Min 《China Communications》 SCIE CSCD 2014年第4期154-162,共9页
As a new computing mode,cloud computing can provide users with virtualized and scalable web services,which faced with serious security challenges,however.Access control is one of the most important measures to ensure ... As a new computing mode,cloud computing can provide users with virtualized and scalable web services,which faced with serious security challenges,however.Access control is one of the most important measures to ensure the security of cloud computing.But applying traditional access control model into the Cloud directly could not solve the uncertainty and vulnerability caused by the open conditions of cloud computing.In cloud computing environment,only when the security and reliability of both interaction parties are ensured,data security can be effectively guaranteed during interactions between users and the Cloud.Therefore,building a mutual trust relationship between users and cloud platform is the key to implement new kinds of access control method in cloud computing environment.Combining with Trust Management(TM),a mutual trust based access control(MTBAC) model is proposed in this paper.MTBAC model take both user's behavior trust and cloud services node's credibility into consideration.Trust relationships between users and cloud service nodes are established by mutual trust mechanism.Security problems of access control are solved by implementing MTBAC model into cloud computing environment.Simulation experiments show that MTBAC model can guarantee the interaction between users and cloud service nodes. 展开更多
关键词 cloud computing access control trust model mutual trust mechanism MTBAC
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Toward Cloud Computing QoS Architecture:Analysis of Cloud Systems and Cloud Services 认领 引用 被引量：23
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作者 Mohammad Hossein Ghahramani MengChu Zhou Chi Tin Hon 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2017年第1期6-18,共13页
Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are prov... Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are provisioned with minimal management efforts of users. Yet there are some obstacles and concerns about the use of clouds. Guaranteeing quality of service QoS by service providers can be regarded as one of the main concerns for companies tending to use it. Service provisioning in clouds is based on service level agreements representing a contract negotiated between users and providers. According to this contract, if a provider cannot satisfy its agreed application requirements, it should pay penalties as compensation. In this paper, we intend to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue. © 2014 Chinese Association of Automation. 展开更多
关键词 Cloud computing Distributed computer systems System of systems Systems engineering Telecommunication services Web services
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