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Energy-Efficient Computation Offloading and Resource Allocation in Cloud-Edge Collaborative Computing Systems 认领 引用
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作者 Cheng Zhaoping Jiang Tao +3 位作者 Ke Chenxi Zhang Guoqiang Peng Miaoran Feng Mingjie 《China Communications》 SCIE EI CSCD 2026年第3期316-329,共14页
With the increasing adoption of cloud–edge collaborative computing in delay-sensitive applications,sleep control of edge nodes has become a key approach to reducing operational energy consumption.However,existing sch... With the increasing adoption of cloud–edge collaborative computing in delay-sensitive applications,sleep control of edge nodes has become a key approach to reducing operational energy consumption.However,existing schemes have not balanced energy efficiency and performance under edge node sleep control and still lack a joint optimization mechanism for computation offloading and resource allocation.This paper tackles the problem of optimizing energy-efficient computation offloading and resource allocation(CORA)in cloud-edge collaborative computing systems,where edge servers can dynamically enter sleep mode to reduce power consumption.We model the problem as a mixed-integer nonlinear programming formulation,with the objective of minimizing a weighted sum of overall task latency and the cumulative energy consumption of all IoT devices and edge servers.To handle the hybrid nature of discrete and continuous decision variables and the complex system dynamics,we reformulate the problem for each device as a Markov Decision Process and develop a deep deterministic policy gradient with multi-agent algorithm tailored for such hybrid action spaces.Simulation results show that the proposed CORA strategy achieves superior performance compared to three benchmark schemes with reduced latency and energy consumption. 展开更多
关键词 cloud-edge collaborative computing computation offloading deep reinforcement learning energy-efficient design
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Monitoring and Observability in Edge Computing Systems: Taxonomy, Comparative Analysis, and Research Directions 认领 引用
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作者 Hamza Ahmed Hassan Jamil Syed +3 位作者 Aqsa Aslam Sehar Zehra Ummay Faseeha Nurzati Iwani Othman 《Computers, Materials & Continua》 SCIE EI 2026年第8期165-196,共32页
Edge computing is an emerging model for latency-sensitive and distributed applications.However,the observability of edge computing systems in heterogeneous environments remains a challenge,as most existing approaches ... Edge computing is an emerging model for latency-sensitive and distributed applications.However,the observability of edge computing systems in heterogeneous environments remains a challenge,as most existing approaches are limited to only the system,service,application,and network layers.This paper surveys state-of-the-art solutions for edge observability and monitoring.The paper further introduces a thematic taxonomy that groups the state-of-the-art edge observability and monitoring literature based on monitoring intent,telemetry indicators,observability scope,architectural layers,deployment environments,and observability toolchains.Finally,we compare representative solutions in terms of latency,system overhead,bandwidth consumption,and detection accuracy.Our analysis reveals the trade-offs present in existing solutions regarding the overhead and performance metrics,as well as the limitations they impose when scaling,addressing mobility,or augmenting telemetry.Lastly,we conclude with a discussion on open challenges and future directions for designing next-generation,edge observability frameworks. 展开更多
关键词 Edge computing edge monitoring monitoring solutions observability telemetry microservices performance analysis distributed systems
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Robotic computing system and embodied AI evolution:an algorithm-hardware co-design perspective 认领 引用 被引量：1
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作者 Longke Yan Xin Zhao +7 位作者 Bohan Yang Yongkun Wu Guangnan Dai Jiancong Li Chi-Ying Tsui Kwang-Ting Cheng Yihan Zhang Fengbin Tu 《Journal of Semiconductors》 EI CAS CSCD 2025年第10期6-23,共18页
Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap fr... Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models.This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance.Therefore,algorithm-hardware co-design has emerged as the primary methodology,which ana-lyzes algorithm behaviors on hardware to identify common computational properties.These properties can motivate algo-rithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency.We then reviewed recent works on robotic and embodied AI algorithms and computing hard-ware to demonstrate this algorithm-hardware co-design methodology.In the end,we discuss future research opportunities by answering two questions:(1)how to adapt the computing platforms to the rapid evolution of embodied AI algorithms,and(2)how to transform the potential of emerging hardware innovations into end-to-end inference improvements. 展开更多
关键词 robotic computing system embodied AI algorithm-hardware co-design AI chip large-scale AI models
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Near‑Sensor Edge Computing System Enabled by a CMOS Compatible Photonic Integrated Circuit Platform Using Bilayer AlN/Si Waveguides 认领 引用 被引量：3
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作者 Zhihao Ren Zixuan Zhang +4 位作者 Yangyang Zhuge Zian Xiao Siyu Xu Jingkai Zhou Chengkuo Lee 《Nano-Micro Letters》 SCIE EI CAS CSCD 2025年第11期1-20,共20页
The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language proc... The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment. 展开更多
关键词 Photonic integrated circuits Edge computing Aluminum nitride Neural networks Wearable sensors
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Flexible artificial vision computing system based on FeOx optomemristor for speech recognition 认领 引用 被引量：1
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作者 Jie Li Yue Xin +6 位作者 Bai Sun Dengshun Gu Changrong Liao Xiaofang Hu Lidan Wang Shukai Duan Guangdong Zhou 《Journal of Semiconductors》 EI CAS CSCD 2025年第1期225-232,共8页
With the advancement of artificial intelligence,optic in-sensing reservoir computing based on emerging semiconductor devices is high desirable for real-time analog signal processing.Here,we disclose a flexible optomem... With the advancement of artificial intelligence,optic in-sensing reservoir computing based on emerging semiconductor devices is high desirable for real-time analog signal processing.Here,we disclose a flexible optomemristor based on C27H30O15/FeOx heterostructure that presents a highly sensitive to the light stimuli and artificial optic synaptic features such as short-and long-term plasticity(STP and LTP),enabling the developed optomemristor to implement complex analogy signal processing through building a real-physical dynamic-based in-sensing reservoir computing algorithm and yielding an accuracy of 94.88%for speech recognition.The charge trapping and detrapping mediated by the optic active layer of C27H30O15 that is extracted from the lotus flower is response for the positive photoconductance memory in the prepared optomemristor.This work provides a feasible organic−inorganic heterostructure as well as an optic in-sensing vision computing for an advanced optic computing system in future complex signal processing. 展开更多
关键词 reservoir computing flexible optomemristor analogy signal processing optic computing
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Computational Offloading and Resource Allocation for Internet of Vehicles Based on UAV-Assisted Mobile Edge Computing System 认领 引用
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作者 Fang Yujie Li Meng +3 位作者 Si Pengbo Yang Ruizhe Sun Enchang Zhang Yanhua 《China Communications》 SCIE EI CSCD 2025年第9期333-351,共19页
As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational ... As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant. 展开更多
关键词 computational offloading Internet of Vehicles mobile edge computing resource optimization unmanned aerial vehicle
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Optically and electrically modulated artificial synapses based on MoS2 /PZT ferroelectric field-effect transistor for neuromorphic computing system 认领 引用 被引量：2
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作者 Woochan Chung Doohyung Kim +3 位作者 Juri Kim Jongmin Park Sungjun Kim Sejoon Lee 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2025年第15期25-34,共10页
To present an advanced device scheme of high-performance optoelectronic synapses,herein,we demonstrated the electrically-and/or optically-drivable multifaceted synaptic capabilities on the 2D semiconductor channel-bas... To present an advanced device scheme of high-performance optoelectronic synapses,herein,we demonstrated the electrically-and/or optically-drivable multifaceted synaptic capabilities on the 2D semiconductor channel-based ferroelectric field-effect transistor(FeFET)architecture.The device was fabricated in the form of the MoS2/PZT FeFET,and its synaptic weights were effectively controlled by dual stimuli(i.e.,both electrical and optical pulses simultaneously)as well as single stimuli(i.e.,either electrical or optical pulses alone).This could be attributed to the electrical pulse-tunable strong ferroelectric polarization in PbZrxTi1−xO3(PZT)as well as the polarization field-enhanced persistent photoconductivity effect in MoS2.Additionally,it was confirmed that the proposed device possesses substantial activity,achieving approximately 95%pattern recognition accuracy.The results substantiate the great potential of the 2D semiconductor channel-based FeFET device as a high-performance optoelectronic synaptic platform,marking a pivotal stride towards the realization of advanced neuromorphic computing systems. 展开更多
关键词 Molybdenum disulfide Lead zirconate titanate Ferroelectric field-effect transistor Optoelectronic artificial synapse Neuromorphic computing
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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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Resource Allocation Method for Unmanned Aerial Vehicle-Assisted and User Cooperation Non-Linear Energy Harvesting Mobile Edge Computing System 认领 引用
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作者 HE Ximei ZHAO Yisheng +1 位作者 XU Zhihong CHEN Yong 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第6期1220-1231,共12页
Aimed at the doubly near-far problems in a large range suffered by the remote user group and in a small range existing in both nearby and remote user groups during energy harvesting and computation offloading,a resour... Aimed at the doubly near-far problems in a large range suffered by the remote user group and in a small range existing in both nearby and remote user groups during energy harvesting and computation offloading,a resource allocation method for unmanned aerial vehicle(UAV)-assisted and user cooperation non-linear energy harvesting mobile edge computing(MEC)system is proposed.The UAV equipped with an MEC server is introduced to provide energy and computing services for the remote user group to alleviate the doubly near-far problem in a large range suffered by the remote user group.The doubly near-far problem in a small range existing in both nearby and remote user groups is mitigated by user cooperation.The specific user cooperation strategy is that the user near the base station or the UAV is used as a relay to transfer the computing task of the user far from the base station or the UAV to the MEC server for computing.By jointly optimizing users’offloading time,users’transmitting power,and the hovering position of the UAV,the resource allocation problem is modeled as a nonlinear programming problem with the objective of maximizing computation efficiency.The suboptimal solution is obtained by adopting the differential evolution algorithm.Simulation results show that,compared with the resource allocation method based on genetic algorithm and the without user cooperation method,the proposed method has higher computation efficiency. 展开更多
关键词 mobile edge computing(MEC) non-linear energy harvesting unmanned aerial vehicle(UAV) user cooperation resource allocation
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Modeling and analysis of cloud computing system survivability based on Bio-PEPA 认领 引用 被引量：1
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作者 Zhao Guosheng Ren Mengqi +1 位作者 Wang Jian Liao Yiwei 《Journal of Southeast University(English Edition)》 EI CAS 2018年第1期21-27,共7页
For the cloud computing system,combined wth the memory function and incomplete matching of the biological immune system,a formal modeling and analysis method of the cloud computing system survivability is proposed by ... For the cloud computing system,combined wth the memory function and incomplete matching of the biological immune system,a formal modeling and analysis method of the cloud computing system survivability is proposed by analyzing the survival situation of critical cloud services.First,on the basis of the SAIR(susceptible,active,infected,recovered)model,the SEIRS(susceptible,exposed,infected,recovered,susceptible)model and the vulnerability diffusion model of the distributed virtual system,the evolution state of the virus is divided into six types,and then the diffusion rules of the virus in the service domain of the cloud computing system and the propagation rules between service domains are analyzee.Finally,on the basis of Bio-PEPA(biological-performance evaluation process algebra),the formalized modeling of the survivability evolution of critical cloud services is made,and the SLIRAS(susceptible,latent,infected,recovered,antidotal,susceptible)model is obtained.Based on the stochastic simulation and the ODEs(ordinary differential equations)simulation of the Bio-PEPA model,the sensitivity parameters of the model are analyzed from three aspects,namely,the virus propagation speed of inter-domain,recovery ability and memory ability.The results showthat the proposed model has high approximate fitting degree to the actual cloud computing system,and it can well reflect the survivable change of the system. 展开更多
关键词 cloud computing system Bio-I>EPA(biologicalperformance evaluation process algebra) survivability stochastic simulation
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A distributed deadlock detection algorithm for mobile computing system 认领 引用
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作者 程欣 刘宏伟 +2 位作者 左德承 金峰 杨孝宗 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第5期521-527,共7页
The mode of mobile computing originated from distributed computing and it has the un-idempotent operation property, therefore the deadlock detection algorithm designed for mobile computing systems will face challenges... The mode of mobile computing originated from distributed computing and it has the un-idempotent operation property, therefore the deadlock detection algorithm designed for mobile computing systems will face challenges with regard to correctness and high efficiency. This paper attempts a fundamental study of deadlock detection for the AND model of mobile computing systems. First, the existing deadlock detection algorithms for distributed systems are classified into the resource node dependent (RD) and the resource node independent (RI) categories, and their corresponding weaknesses are discussed. Afterwards a new RI algorithm based on the AND model of mobile computing system is presented. The novelties of our algorithm are that: 1) the blocked nodes inform their predecessors and successors simultaneously; 2) the detection messages (agents) hold the predecessors information of their originator; 3) no agent is stored midway. Additionally, the quit-inform scheme is introduced to treat the excessive victim quitting problem raised by the overlapped cycles. By these methods the proposed algorithm can detect a cycle of size n within n-2 steps and with (n^2-n-2)/2 agents. The performance of our algorithm is compared with the most competitive RD and RI algorithms for distributed systems on a mobile agent simulation platform. Experiment results point out that our algorithm outperforms the two algorithms under the vast majority of resource configurations and concurrent workloads. The correctness of the proposed algorithm is formally proven by the invariant verification technique. 展开更多
关键词 mobile computing system deadlock detection AND model cycle overlap
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"Smart Cafe":A Mobile Local Computing System Based On Indoor Virtual Cloud 认领 引用 被引量：2
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作者 PU Lingjun XU Jingdong +1 位作者 YU Bowen ZHANG Jianzhong 《China Communications》 SCIE CSCD 2014年第4期38-49,共12页
With network developing and virtualization rising, more and more indoor environment (POIs) such as care, library, office, even bus and subway can provide plenty of bandwidth and computing resources. Meanwhile many peo... With network developing and virtualization rising, more and more indoor environment (POIs) such as care, library, office, even bus and subway can provide plenty of bandwidth and computing resources. Meanwhile many people daily spending much time in them are still suffering from the mobile device with limited resources. This situation implies a novel local cloud computing paradigm in which mobile device can leverage nearby resources to facilitate task execution. In this paper, we implement a mobile local computing system based on indoor virtual cloud. This system mainly contains three key components: 1)As to application, we create a parser to generate the "method call and cost tree" and analyze it to identify resource- intensive methods. 2) As to mobile device, we design a self-learning execution controller to make offtoading decision at runtime. 3) As to cloud, we construct a social scheduling based application-isolation virtual cloud model. The evaluation results demonstrate that our system is effective and efficient by evaluating CPU- intensive calculation application, Memory- intensive image translation application and I/ O-intensive image downloading application. 展开更多
关键词 mobile local computing system application partition dynamic offloading strategy virtual cloud model social scheduling
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A Greedy Algorithm for Task Offloading in Mobile Edge Computing System 认领 引用 被引量：38
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作者 Feng Wei Sixuan Chen Weixia Zou 《China Communications》 SCIE CSCD 2018年第11期149-157,共9页
Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mo... Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system. 展开更多
关键词 mobile edge computing task off- loading greedy choice energy resource allo- cation
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VPFL:A verifiable privacy-preserving federated learning scheme for edge computing systems 认领 引用 被引量：7
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作者 Jiale Zhang Yue Liu +3 位作者 Di Wu Shuai Lou Bing Chen Shui Yu 《Digital Communications and Networks》 SCIE CSCD 2023年第4期981-989,共9页
Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the centra... Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server.However,the frequently transmitted local gradients could also leak the participants’private data.To protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been proposed.However,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously.To tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission stage.Firstly,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex cryptosystem.Secondly,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge server.Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and integrity.At last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy. 展开更多
关键词 Federated learning Edge computing Privacy-preserving Verifiable aggregation Homomorphic cryptosystem
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Proximal Policy Optimization-Based Committee Selection Algorithm in Blockchain-Enabled Mobile Edge Computing Systems 认领 引用 被引量：4
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作者 Wenjun Wu Dehao Sun +2 位作者 Kaiqi Jin Yang Sun Pengbo Si 《China Communications》 SCIE CSCD 2022年第6期50-65,共16页
To cope with the low latency requirements and security issues of the emerging applications such as Internet of Vehicles(Io V)and Industrial Internet of Things(IIo T),the blockchain-enabled Mobile Edge Computing(MEC)sy... To cope with the low latency requirements and security issues of the emerging applications such as Internet of Vehicles(Io V)and Industrial Internet of Things(IIo T),the blockchain-enabled Mobile Edge Computing(MEC)system has received extensive attention.However,blockchain is a computing and communication intensive technology due to the complex consensus mechanisms.To facilitate the implementation of blockchain in the MEC system,this paper adopts the committee-based Practical Byzantine Fault Tolerance(PBFT)consensus algorithm and focuses on the committee selection problem.Vehicles and IIo T devices generate the transactions which are records of the application tasks.Base Stations(BSs)with MEC servers,which serve the transactions according to the wireless channel quality and the available computing resources,are blockchain nodes and candidates for committee members.The income of transaction service fees,the penalty of service delay,the decentralization of the blockchain and the communication complexity of the consensus process constitute the performance index.The committee selection problem is modeled as a Markov decision process,and the Proximal Policy Optimization(PPO)algorithm is adopted in the solution.Simulation results show that the proposed PPO-based committee selection algorithm can adapt to the system design requirements with different emphases and outperforms other comparison methods. 展开更多
关键词 blockchain mobile edge computing deep reinforcement learning consensus mechanism
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Enhancing Reliability via Checkpointing in Cloud Computing Systems 认领 引用 被引量：4
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作者 Ao Zhou Qibo Sun Jinglin Li 《China Communications》 SCIE CSCD 2017年第7期108-117,共10页
Cloud computing is becoming an important solution for providing scalable computing resources via Internet. Because there are tens of thousands of nodes in data center, the probability of server failures is nontrivial.... Cloud computing is becoming an important solution for providing scalable computing resources via Internet. Because there are tens of thousands of nodes in data center, the probability of server failures is nontrivial. Therefore, it is a critical challenge to guarantee the service reliability. Fault-tolerance strategies, such as checkpoint, are commonly employed. Because of the failure of the edge switches, the checkpoint image may become inaccessible. Therefore, current checkpoint-based fault tolerance method cannot achieve the best effect. In this paper, we propose an optimal checkpoint method with edge switch failure-aware. The edge switch failure-aware checkpoint method includes two algorithms. The first algorithm employs the data center topology and communication characteristic for checkpoint image storage server selection. The second algorithm employs the checkpoint image storage characteristic as well as the data center topology to select the recovery server. Simulation experiments are performed to demonstrate the effectiveness of the proposed method. 展开更多
关键词 cloud computing cloud service reliability fault tolerance data center network
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Secure intelligent reflecting surface assisted mobile edge computing system with wireless power transfer 认领 引用 被引量：2
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作者 Dawei Wang Xuanrui Li +4 位作者 Menghan Wu Yixin He Yi Lou Yu Pang Yi Lu 《Digital Communications and Networks》 SCIE CSCD 2024年第6期1874-1880,共7页
In this paper,we study an Intelligent Reflecting Surface(IRS)assisted Mobile Edge Computing(MEC)system under eavesdropping threats,where the IRS is used to enhance the energy signal transmission and the offloading per... In this paper,we study an Intelligent Reflecting Surface(IRS)assisted Mobile Edge Computing(MEC)system under eavesdropping threats,where the IRS is used to enhance the energy signal transmission and the offloading performance between Wireless Devices(WDs)and the Access Point(AP).Specifically,in the proposed scheme,the AP first powers all WDs with the wireless power transfer through both direct and IRS-assisted links.Then,powered by the harvested energy,all WDs securely offload their computation tasks through the two links in the time division multiple access mode.To determine the local and offloading computational bits,we formulate an optimization problem to jointly design the IRS's phase shift and allocate the time slots constrained by the security and energy requirements.To cope with this non-convex optimization problem,we adopt semidefinite relaxations,singular value decomposition techniques,and Lagrange dual method.Moreover,we propose a dichotomy particle swarm algorithm based on the bisection method to process the overall optimization problem and improve the convergence speed.The numerical results illustrate that the proposed scheme can boost the performance of MEC and secure computation rates compared with other IRS-assisted MEC benchmark schemes. 展开更多
关键词 Intelligent reflecting surface Mobile edge computing Power transfer Information security
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A Partitioning Methodology That Optimizes the Communication Cost for Reconfigurable Computing Systems 认领 引用
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作者 Ramzi Ayadi Bouraoui Ouni Abdellatif Mtibaa 《International Journal of Automation and computing》 2012年第3期280-287,共8页
This paper focuses on the design process for reconfigurable architecture. Our contribution focuses on introducing a new temporal partitioning algorithm. Our algorithm is based on typical mathematic flow to solve the t... This paper focuses on the design process for reconfigurable architecture. Our contribution focuses on introducing a new temporal partitioning algorithm. Our algorithm is based on typical mathematic flow to solve the temporal partitioning problem. This algorithm optimizes the transfer of data required between design partitions and the reconfiguration overhead. Results show that our algorithm considerably decreases the communication cost and the latency compared with other well known algorithms. 展开更多
关键词 Temporal partitioning data flow graph communication cost reconfigurable computing systems field-programmable gate array (FPGA)
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Research on Generalized Computing Systems 认领 引用 被引量：3
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作者 Min, Yao Jianhua, Luo 《Journal of Systems Engineering and Electronics》 EI 1998年第3期39-43,共5页
This paper presents a kind of artificial intelligent system-generalized computing system (GCS for short), and introduces its mathematical description, implement problem and learning problem.
关键词 Artificial intelligence Generalized computing Generalized computing systems Generalized learning
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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system 认领 引用
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作者 Uchechukwu Awada Jiankang Zhang +2 位作者 Sheng Chen Shuangzhi Li Shouyi Yang 《Digital Communications and Networks》 SCIE CSCD 2024年第6期1837-1850,共14页
Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enabl... Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive tasks.One fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource utilization.Existing approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource availability.These approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider lock-in.Therefore,we present Edge Colla,which is based on the integration of edge resources running across multi-edge deployments.Edge Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize them.Extensive experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes. 展开更多
关键词 Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling
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