SFC placement and dynamic resource allocation based on VNF performance-resource function and service requirement in cloud-edge environment

With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.

Achieving Fuzzy Matching Data Sharing for Secure Cloud-Edge Communication

In this paper,we propose a novel fuzzy matching data sharing scheme named FADS for cloudedge communications.FADS allows users to specify their access policies,and enables receivers to obtain the data transmitted by the senders if and only if the two sides meet their defined certain policies simultaneously.Specifically,we first formalize the definition and security models of fuzzy matching data sharing in cloud-edge environments.Then,we construct a concrete instantiation by pairing-based cryptosystem and the privacy-preserving set intersection on attribute sets from both sides to construct a concurrent matching over the policies.If the matching succeeds,the data can be decrypted.Otherwise,nothing will be revealed.In addition,FADS allows users to dynamically specify the policy for each time,which is an urgent demand in practice.A thorough security analysis demonstrates that FADS is of provable security under indistinguishable chosen ciphertext attack(IND-CCA)in random oracle model against probabilistic polynomial-time(PPT)adversary,and the desirable security properties of privacy and authenticity are achieved.Extensive experiments provide evidence that FADS is with acceptable efficiency.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

Anomaly Detection and Access Control for Cloud-Edge Collaboration Networks

Software-defined networking(SDN)enables the separation of control and data planes,allowing for centralized control and management of the network.Without adequate access control methods,the risk of unau-thorized access to the network and its resources increases significantly.This can result in various security breaches.In addition,if authorized devices are attacked or controlled by hackers,they may turn into malicious devices,which can cause severe damage to the network if their abnormal behaviour goes undetected and their access privileges are not promptly restricted.To solve those problems,an anomaly detection and access control mechanism based on SDN and neural networks is proposed for cloud-edge collaboration networks.The system employs the Attribute Based Access Control(ABAC)model and smart contract for fine-grained control of device access to the network.Furthermore,a cloud-edge collaborative Key Performance Indicator(KPI)anomaly detection method based on the Gated Recurrent Unit and Generative Adversarial Nets(GRU-GAN)is designed to discover the anomaly devices.An access restriction mechanism based on reputation value and anomaly detection is given to prevent anomalous devices.Experiments show that the proposed mechanism performs better anomaly detection on several datasets.The reputation-based access restriction effectively reduces the number of malicious device attacks.

Efficient Multi-Authority Attribute-Based Searchable Encryption Scheme with Blockchain Assistance for Cloud-Edge Coordination

Cloud storage and edge computing are utilized to address the storage and computational challenges arising from the exponential data growth in IoT.However,data privacy is potentially risky when data is outsourced to cloud servers or edge services.While data encryption ensures data confidentiality,it can impede data sharing and retrieval.Attribute-based searchable encryption(ABSE)is proposed as an effective technique for enhancing data security and privacy.Nevertheless,ABSE has its limitations,such as single attribute authorization failure,privacy leakage during the search process,and high decryption overhead.This paper presents a novel approach called the blockchain-assisted efficientmulti-authority attribute-based searchable encryption scheme(BEM-ABSE)for cloudedge collaboration scenarios to address these issues.BEM-ABSE leverages a consortium blockchain to replace the central authentication center for global public parameter management.It incorporates smart contracts to facilitate reliable and fair ciphertext keyword search and decryption result verification.To minimize the computing burden on resource-constrained devices,BEM-ABSE adopts an online/offline hybrid mechanism during the encryption process and a verifiable edge-assisted decryption mechanism.This ensures both low computation cost and reliable ciphertext.Security analysis conducted under the random oracle model demonstrates that BEM-ABSE is resistant to indistinguishable chosen keyword attacks(IND-CKA)and indistinguishable chosen plaintext attacks(INDCPA).Theoretical analysis and simulation results confirm that BEM-ABSE significantly improves computational efficiency compared to existing solutions.

A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications

Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods.In view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation model.More specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service attributes.The Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation model.Finally,we evaluate the effectiveness of the NCF-MS method on three public datasets.The experimental results show that our proposed method achieves better performance than existing methods.

A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation

How to collaboratively offload tasks between user devices,edge networks(ENs),and cloud data centers is an interesting and challenging research topic.In this paper,we investigate the offoading decision,analytical modeling,and system parameter optimization problem in a collaborative cloud-edge device environment,aiming to trade off different performance measures.According to the differentiated delay requirements of tasks,we classify the tasks into delay-sensitive and delay-tolerant tasks.To meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible,we propose a cloud-edge device collaborative task offoading scheme,in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy,respectively.We establish a four-dimensional continuous-time Markov chain as the system model.By using the Gauss-Seidel method,we derive the stationary probability distribution of the system model.Accordingly,we present the blocking rate of delay-sensitive tasks and the average delay of these two types of tasks.Numerical experiments are conducted and analyzed to evaluate the system performance,and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading scheme.Finally,we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.

Market Equilibrium Based on Cloud-edge Collaboration

Market participants can only bid with lagged information disclosure under the existing market mechanism,which can lead to information asymmetry and irrational market behavior,thus influencing market efficiency.To promote rational bidding behavior of market participants and improve market efficiency,a novel electricity market mechanism based on cloudedge collaboration is proposed in this paper.Critical market information,called residual demand curve,is published to market participants in real-time on the cloud side,while participants on the edge side are allowed to adjust their bids according to the information disclosure prior to closure gate.The proposed mechanism can encourage rational bids in an incentive-compatible way through the process of dynamic equilibrium while protecting participants’privacy.This paper further formulates the mathematical model of market equilibrium to simulate the process of each market participant’s strategic bidding behavior towards equilibrium.A case study based on the IEEE 30-bus system shows the proposed market mechanism can effectively guide bidding behavior of market participants,while condensing exchanged information and protecting privacy of participants.

User Churn Prediction Hierarchical Model Based on Graph Attention Convolutional Neural Networks

The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.

A Cloud-edge Cooperative Dispatching Method for Distribution Networks Considering Photovoltaic Generation Uncertainty

With the increasing penetration of renewable energy generation,uncertainty and randomness pose great challenges for optimal dispatching in distribution networks.We propose a cloud-edge cooperative dispatching(CECD)method to exploit the new opportunities offered by Internet of Things(IoT)technology.To alleviate the huge pressure on the modeling and computing of large-scale distribution system,the method deploys edge nodes in small-scale transformer areas in which robust optimization subproblem models are introduced to address the photovoltaic(PV)uncertainty.Considering the limited communication and computing capabilities of the edge nodes,the cloud center in the distribution automation system(DAS)establishes a utility grid master problem model that enforces the consistency between the solution at each edge node with the utility grid based on the alternating direction method of multipliers(ADMM).Furthermore,the voltage constraint derived from the linear power flow equations is adopted for enhancing the operation security of the distribution network.We perform a cloud-edge system simulation of the proposed CECD method and demonstrate a dispatching application.The case study is carried out on a modified 33-node system to verify the remarkable performance of the proposed model and method.

Cloud-edge-based We-Market:Autonomous Bidding and Peer-to-peer Energy Sharing Among Prosumers

With the extensive penetration of distributed renewable energy and self-interested prosumers,the emerging power market tends to enable user autonomy by bottom-up control and distributed coordination.This paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer(P2P)energy sharing among prosumers.A novel cloud-edge-based We-Market is presented,where the prosumers,as edge nodes with independent control,balance the electricity cost and thermal comfort by formulating a dynamic household energy management system(HEMS).Meanwhile,the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility function.In the cloud center,a distributed convergence bidding(CB)algorithm based on consistency criterion is developed,which promotes faster and fairer bidding through the interactive iteration with the edge nodes.Besides,the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing capacity.Numerical results show the effectiveness and practicability of the proposed We-Market,which achieves 15%cost reduction with shorter running time.Comparative analysis indicates better scalability,which is more suitable for largerscale We-Market implementation.

FPGA-based edge computing: Task modeling for cloud-edge collaboration

With the development of the Internet of Things and devices continuing to scale,using cloud computing resources to process data in real-time is challenging.Edge computing technologies can improve real-time performance in processing data.By introducing the FPGA into the computing node and using the dynamic reconfigurability of the FPGA,the FPGA-based edge node can increase the edge node capability.In this paper,a task-based collaborative method for an FPGA-based edge computing system is proposed in order to meet the collaboration among FPGA-based edge nodes,edge nodes,and the cloud.The modeling of the task includes two parts,task information and task-dependent file.Task information is used to describe the running information and dependency infor-mation required for the task execution.Task-dependent file contains the configuration bit-stream of FPGA in running of the task.By analyzing the task behavior,this paper builds four basic behaviors,analyzes the critical attributes of each behavior,and summa-rizes the task model suitable for FPGA-based edge nodes.Tasks with specific functions can be created by modifying different attributes of model nodes.Finally,the availability of the model and the task-based collaborative method are verified by simulation exper-iments.The experimental results that the task model proposed in this paper can meet cloud-edge collaboration in the FPGA-based edge computing environment.

Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid

Real-time fault detection is important for operation of smart grid.It has become a trend of future development to design an anomaly detection system based on deep learning by using the powerful computing power of the cloud.However,delay of Internet transmission is large,which may make the delay time of detection and transmission go beyond the limits.However,the edge-based scheme may not be able to undertake all data detection tasks due to limited computing resources of edge devices.Therefore,we propose a cloud-edge collaborative smart grid fault detection system,next to which edge devices are placed,and equipped with a lightweight neural network with different precision for fault detection.In addition,a sub-optimal and realtime communication and computing resource allocation method is proposed based on deep reinforcement learning.This method greatly speeds up solution time,which can meet the requirements of data transmission delay,maximize the system throughput,and improve communication efficiency.Simulation results show the scheme is superior in transmission delay and improves real-time performance of the smart grid detection system.

Smart additive manufacturing: Current artificial intelligenceenabled methods and future perspectives

Additive manufacturing(AM) has been increasingly used in production. Because of its rapid growth, the efficiency and robustness of AM-based product development processes should be improved. Artificial intelligence(AI) is a powerful tool that has outperformed humans in numerous complex tasks. AI-enabled intelligent agents can reduce the workforce required to scale up AM production and achieve higher resource utilization efficiency. This study provides an introduction of AI techniques. Then,the current development of AI-enabled AM product development is investigated. Existing intelligent agents are used for problems in product design, process design and production stages. Based on the review, current research gaps and future research directions are identified. To guide future development of more efficient and comprehensive intelligent agents, a smart AM framework based on cloud-edge computing is proposed. Global consideration can be realized in the cloud environment, and a fast response can be achieved at the edge nodes.

Collaborative Offloading Method for Digital Twin Empowered Cloud Edge Computing on Internet of Vehicles

Digital twinning and edge computing are attractive solutions to support computing-intensive and servicesensitive Internet of Vehicles applications.Most of the existing Internet of Vehicles service offloading solutions only consider edge–cloud collaboration,but the collaboration between small cell eNodeB(SCeNB)should not be ignored.Service delays far lower than offloading tasks to the cloud can be obtained through reasonable collaborative computing between nodes.The proposed framework realizes and maintains the simulation of collaboration between SCeNB nodes by constructing a digital twin that maintains SCeNB nodes in the central controller,thereby realizing user task offloading positions,sub-channel allocation,and computing resource allocation.Then an algorithm named AUC-AC is proposed,based on the dominant actor–critic network and the auction mechanism.In order to obtain a better command of global information,the convolutional block attention mechanism(CBAM)is used in the digital twin of each SCeNB node to observe its environment and learn strategies.Numerical results show that our experimental scheme is better than several baseline algorithms in terms of service delay.

Hybrid Multiple Access and Service-Oriented Resource Allocation for Heterogeneous QoS Provisioning in Machine Type Communications

Non-orthogonal multiple access(NOMA)has emerged as one important enabling technology for future wireless communications and services,including machine type communication(MTC).Unfortunately,supporting diverse MTC services and massive connectivity is still challenging due to the very different service requirements,scarce radio resources,limited battery capacity of MTC devices,as well as rapidly changing network conditions.In this paper,a hybrid-multipleaccess(HMA)scheme for service-oriented resource allocation scheme is proposed in supporting diverse MTC services for resource constrained devices and networks.In the proposed scheme,HMA allows MTC devices to choose a suitable type of multiple access technique according to their channel conditions,power constraints,and quality of service(QoS)requirements.To support service-oriented resource allocation,the physical network is firstly sliced into several virtualized networks based on QoS requirements and hardware conditions of MTC devices.A novel utility function integrating network performance and the power consumption in MTC devices is proposed.Furthermore,the resource allocation problem is formulated as an optimization problem to maximize the different utility functions under constraints of user QoS requirements and maximum transmitted power.To improve computational capacity as well as reduce the operational latency,a cloud-edge collaborative scheme is further designed to share the computation loads between the cloud and edge.Simulation results demonstrate the proposed service-oriented resource allocation scheme is effective and illustrate that the proposed hybrid multiple access method provides better performance than NOMA in terms of effective energy efficiency.

Prediction based dynamic resource allocation method for edge computing first networking

Aiming at the factory with high-complex and multi-terminal in the industrial Internet of things(IIoT),a hierarchical edge networking collaboration(HENC)framework based on the cloud-edge collaboration and computing first networking(CFN)is proposed to improve the capability of task processing with fixed computing resources on the edge effectively.To optimize the delay and energy consumption in HENC,a multi-objective optimization(MOO)problem is formulated.Furthermore,to improve the efficiency and reliability of the system,a resource prediction model based on ridge regression(RR)is proposed to forecast the task size of the next time slot,and an emergency-aware(EA)computing resource allocation algorithm is proposed to reallocate tasks in edge CFN.Based on the simulation result,the EA algorithm is superior to the greedy resource allocation in time delay,energy consumption,quality of service(QoS)especially with limited computing resources.