This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g...This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.展开更多
In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements o...In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling.Then,a Bi-LSTM-based model is proposed to predict the trajectories of vehicles.The service area is divided into several equal-sized grids.If the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory prediction.Moreover,we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction.Considering the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading.Simulation results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.展开更多
For better flexibility and greater coverage areas,Unmanned Aerial Vehicles(UAVs)have been applied in Flying Mobile Edge Computing(F-MEC)systems to offer offloading services for the User Equipment(UEs).This paper consi...For better flexibility and greater coverage areas,Unmanned Aerial Vehicles(UAVs)have been applied in Flying Mobile Edge Computing(F-MEC)systems to offer offloading services for the User Equipment(UEs).This paper considers a disaster-affected scenario where UAVs undertake the role of MEC servers to provide computing resources for Disaster Relief Devices(DRDs).Considering the fairness of DRDs,a max-min problem is formulated to optimize the saved time by jointly designing the trajectory of the UAVs,the offloading policy and serving time under the constraint of the UAVs'energy capacity.To solve the above non-convex problem,we first model the service process as a Markov Decision Process(MDP)with the Reward Shaping(RS)technique,and then propose a Deep Reinforcement Learning(DRL)based algorithm to find the optimal solution for the MDP.Simulations show that the proposed RS-DRL algorithm is valid and effective,and has better performance than the baseline algorithms.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network e...In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.展开更多
As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays...As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.展开更多
Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of im...Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of image or video processing,which imposes enormous pressure on the UAV computation platform.To solve this issue,in this work,we propose an intelligent Task Offloading Algorithm(iTOA)for UAV edge computing network.Compared with existing methods,iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search(MCTS),the core algorithm of Alpha Go.MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward,such as lowest latency or power consumption.To accelerate the search convergence of MCTS,we also proposed a splitting Deep Neural Network(sDNN)to supply the prior probability for MCTS.The sDNN is trained by a self-supervised learning manager.Here,the training data set is obtained from iTOA itself as its own teacher.Compared with game theory and greedy search-based methods,the proposed iTOA improves service latency performance by 33%and 60%,respectively.展开更多
Peer-to-peer computation offloading has been a promising approach that enables resourcelimited Internet of Things(IoT)devices to offload their computation-intensive tasks to idle peer devices in proximity.Different fr...Peer-to-peer computation offloading has been a promising approach that enables resourcelimited Internet of Things(IoT)devices to offload their computation-intensive tasks to idle peer devices in proximity.Different from dedicated servers,the spare computation resources offered by peer devices are random and intermittent,which affects the offloading performance.The mutual interference caused by multiple simultaneous offloading requestors that share the same wireless channel further complicates the offloading decisions.In this work,we investigate the opportunistic peer-to-peer task offloading problem by jointly considering the stochastic task arrivals,dynamic interuser interference,and opportunistic availability of peer devices.Each requestor makes decisions on both local computation frequency and offloading transmission power to minimize its own expected long-term cost on tasks completion,which takes into consideration its energy consumption,task delay,and task loss due to buffer overflow.The dynamic decision process among multiple requestors is formulated as a stochastic game.By constructing the post-decision states,a decentralized online offloading algorithm is proposed,where each requestor as an independent learning agent learns to approach the optimal strategies with its local observations.Simulation results under different system parameter configurations demonstrate the proposed online algorithm achieves a better performance compared with some existing algorithms,especially in the scenarios with large task arrival probability or small helper availability probability.展开更多
The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cess...The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.展开更多
In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from...In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and directly.In this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is uncertain.This uncertainty would be the main obstacle to assign the task accurately.Consequently,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be caused.In order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and migration.With the help of Stochastic Programming(SP),we use the posteriori recourse to compensate for inaccurate predictions.Meanwhile,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC selection.The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and delay.The results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.展开更多
This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)systems.Due to the presence of an eavesdropper(Eve),the system’s com-mu...This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)systems.Due to the presence of an eavesdropper(Eve),the system’s com-munication links may be insecure.This paper proposes deploying an intelligent reflecting surface(IRS)on the UAV to enhance the communication performance of mobile vehicles,improve system flexibility,and alleviate eavesdropping on communication links.The links for uploading task data from vehicles to a base station(BS)are protected by IRS-assisted physical layer security(PLS).Upon receiving task data,the computing resources provided by the edge computing servers(MEC)are allocated to vehicles for task execution.Existing blockchain-based computation offloading schemes typically focus on improving network performance,such as minimizing energy consumption or latency,while neglecting the Gas fees for computation offloading and the costs required for MEC computation,leading to an imbalance between service fees and resource allocation.This paper uses a utility-oriented computation offloading scheme to balance costs and resources.This paper proposes alternating phase optimization and power optimization to optimize the energy consumption,latency,and communication secrecy rate,thereby maximizing the weighted total utility of the system.Simulation results demonstrate a notable enhancement in the weighted total system utility and resource utilization,thereby corroborating the viability of our approach for practical applications.展开更多
Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively.The autonomous platooning task generally requires highly complex computations so ...Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively.The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing units.To solve this problem,there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes.However,the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity.They are also challenging to process computation tasks within 100 ms which is the time limit for driving safety.In this paper,we propose a novel offloading scheme that can support autonomous platooning tasks being processed within the limit and ensure driving safety.The proposed scheme can handle computation tasks by considering the communication bandwidth,delay,and amount of computation.We also conduct simulations in the highway environment to evaluate the existing scheme and the proposed scheme.The result shows that our proposed scheme improves the utilization of nearby computing nodes,and the offloading tasks can be processed within the time for driving safety.展开更多
After many years of exploitation,onshore oil and gas resources are about to enter a recession period.Oil and gas will mainly come from oceans in the future.Generally speaking,the exploration and production(E&P)cos...After many years of exploitation,onshore oil and gas resources are about to enter a recession period.Oil and gas will mainly come from oceans in the future.Generally speaking,the exploration and production(E&P)cost of oil from offshore is much higher than that of oil from onshore,so it is more sensitive to oil price.However,in recent years,oil price has been hovering at a low level for a long time,almost close to or even lower than the E&P cost of oil,which directly affects the development of oilfields.Besides the influence of oil price,some oilfields present the characteristics of marginal reserve scale,short peak production period and output rapidly declining.There leads to short economic life period and makes the economic benefit close to or lower than oilfield’s hurdle rate,which increases the difficulty of offshore oilfield development.As an important part of oilfield development,Floating Production Storage and Offloading unit,its investment mode and rent mode directly affect overall oilfield’s rate of return and the economic life.This paper chooses lease mode as the research object based on the analysis of investment mode,and further puts forward rent mode related with oil price through the analysis of traditional rent mode,and illustrates the advantages and disadvantages of various rent modes and their applicability so that the lessor chooses the right mode to achieve Win-Win with Oil Company and promotes the development of oilfields under low oil price.展开更多
As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the...As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.展开更多
Recent advances in integrating Digital Twins(DTs)with Heterogeneous Vehicular Networks(HetVNets)enhance decision-making and improve network performance.Additionally,developments in Mobile Edge Computing(MEC)support th...Recent advances in integrating Digital Twins(DTs)with Heterogeneous Vehicular Networks(HetVNets)enhance decision-making and improve network performance.Additionally,developments in Mobile Edge Computing(MEC)support the computational demands of DTs.However,the decentralized nature of MEC systems introduces security challenges and traditional HetVNets fail to efficiently integrate diverse computing and network resources,limiting their ability to handle services for vehicles.This paper presents a novel service request offloading framework for DT-HetVNets to address these issues.In this framework,we design utility functions for vehicles and infrastructures to maximize satisfaction of their requirements through data synchronization and decision-making between DTs and entities.Furthermore,we propose a new honestly based distributed PoA(HDPoA)via scalable work.The interactions between infrastructures and vehicles are modeled as a multi-leader multi-follower(MLMF)game,and we develop a dynamic iterative algorithm to achieve the Nash equilibrium(NE)of the proposed game-theoretic model.Experimental results validate the effectiveness and accuracy of our scheme.展开更多
Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies dri...Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading strategies.However,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy performance.In order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy consumption.Firstly,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov property.Secondly,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission delays.Thirdly,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real ones.Numerical results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.展开更多
The advent of the internet-of-everything era has led to the increased use of mobile edge computing.The rise of artificial intelligence has provided many possibilities for the low-latency task-offloading demands of use...The advent of the internet-of-everything era has led to the increased use of mobile edge computing.The rise of artificial intelligence has provided many possibilities for the low-latency task-offloading demands of users,but existing technologies rigidly assume that there is only one task to be offloaded in each time slot at the terminal.In practical scenarios,there are often numerous computing tasks to be executed at the terminal,leading to a cumulative delay for subsequent task offloading.Therefore,the efficient processing of multiple computing tasks on the terminal has become highly challenging.To address the lowlatency offloading requirements for multiple computational tasks on terminal devices,we propose a terminal multitask parallel offloading algorithm based on deep reinforcement learning.Specifically,we first establish a mobile edge computing system model consisting of a single edge server and multiple terminal users.We then model the task offloading decision problem as a Markov decision process,and solve this problem using the Dueling Deep-Q Network algorithm to obtain the optimal offloading strategy.Experimental results demonstrate that,under the same constraints,our proposed algorithm reduces the average system latency.展开更多
Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it mus...Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions.We cast the offloading problem as aMarkov Decision Process(MDP)and solve it with Deep Reinforcement Learning(DRL).Specifically,we present a three-tier architecture—end devices,edge nodes,and a cloud server—and enhance Proximal Policy Optimization(PPO)to learn adaptive,energy-aware policies.A Convolutional Neural Network(CNN)extracts high-level features from system states,enabling the agent to respond continually to changing conditions.Extensive simulations show that the proposed method reduces task latency and energy consumption far more than several baseline algorithms,thereby improving overall system performance.These results demonstrate the effectiveness and robustness of the framework for real-time task offloading in dynamic smart-grid environments.展开更多
The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ap...The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.展开更多
Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G applications.Artificial Intelligence(AI)algorithms will ...Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G applications.Artificial Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability.In this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task completion.However,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource wastage.Additionally,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities problem.This paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH scenarios.Additionally,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH scenarios.The performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed approach.The simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.展开更多
文摘This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.
基金supported in part by the National Science Foundation of China(Grant No.62172450)the Key R&D Plan of Hunan Province(Grant No.2022GK2008)the Nature Science Foundation of Hunan Province(Grant No.2020JJ4756)。
文摘In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling.Then,a Bi-LSTM-based model is proposed to predict the trajectories of vehicles.The service area is divided into several equal-sized grids.If the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory prediction.Moreover,we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction.Considering the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading.Simulation results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.
基金supported by the Key Research and Development Program of Jiangsu Province(No.BE2020084-2)the National Key Research and Development Program of China(No.2020YFB1600104)。
文摘For better flexibility and greater coverage areas,Unmanned Aerial Vehicles(UAVs)have been applied in Flying Mobile Edge Computing(F-MEC)systems to offer offloading services for the User Equipment(UEs).This paper considers a disaster-affected scenario where UAVs undertake the role of MEC servers to provide computing resources for Disaster Relief Devices(DRDs).Considering the fairness of DRDs,a max-min problem is formulated to optimize the saved time by jointly designing the trajectory of the UAVs,the offloading policy and serving time under the constraint of the UAVs'energy capacity.To solve the above non-convex problem,we first model the service process as a Markov Decision Process(MDP)with the Reward Shaping(RS)technique,and then propose a Deep Reinforcement Learning(DRL)based algorithm to find the optimal solution for the MDP.Simulations show that the proposed RS-DRL algorithm is valid and effective,and has better performance than the baseline algorithms.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+4 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)the Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067)the Natural Science Foundation of Liaoning Province(2024-MS-113)the science and technology funds from Liaoning Education Department(LJKZ0242).
文摘In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.
基金supported by Youth Talent Project of Scientific Research Program of Hubei Provincial Department of Education under Grant Q20241809Doctoral Scientific Research Foundation of Hubei University of Automotive Technology under Grant 202404.
文摘As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.
基金the Artificial Intelligence Key Laboratory of Sichuan Province(Nos.2019RYJ05)National Natural Science Foundation of China(Nos.61971107).
文摘Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of image or video processing,which imposes enormous pressure on the UAV computation platform.To solve this issue,in this work,we propose an intelligent Task Offloading Algorithm(iTOA)for UAV edge computing network.Compared with existing methods,iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search(MCTS),the core algorithm of Alpha Go.MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward,such as lowest latency or power consumption.To accelerate the search convergence of MCTS,we also proposed a splitting Deep Neural Network(sDNN)to supply the prior probability for MCTS.The sDNN is trained by a self-supervised learning manager.Here,the training data set is obtained from iTOA itself as its own teacher.Compared with game theory and greedy search-based methods,the proposed iTOA improves service latency performance by 33%and 60%,respectively.
基金supported by National Natural Science Foundation of China (No. 62101601)
文摘Peer-to-peer computation offloading has been a promising approach that enables resourcelimited Internet of Things(IoT)devices to offload their computation-intensive tasks to idle peer devices in proximity.Different from dedicated servers,the spare computation resources offered by peer devices are random and intermittent,which affects the offloading performance.The mutual interference caused by multiple simultaneous offloading requestors that share the same wireless channel further complicates the offloading decisions.In this work,we investigate the opportunistic peer-to-peer task offloading problem by jointly considering the stochastic task arrivals,dynamic interuser interference,and opportunistic availability of peer devices.Each requestor makes decisions on both local computation frequency and offloading transmission power to minimize its own expected long-term cost on tasks completion,which takes into consideration its energy consumption,task delay,and task loss due to buffer overflow.The dynamic decision process among multiple requestors is formulated as a stochastic game.By constructing the post-decision states,a decentralized online offloading algorithm is proposed,where each requestor as an independent learning agent learns to approach the optimal strategies with its local observations.Simulation results under different system parameter configurations demonstrate the proposed online algorithm achieves a better performance compared with some existing algorithms,especially in the scenarios with large task arrival probability or small helper availability probability.
基金supported in part by the National Natural Science Foundation of China under Grant 61901128,62273109the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(21KJB510032).
文摘The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.
基金This work was supported in part by the Zhejiang Lab under Grant 20210AB02in part by the Sichuan International Science and Technology Innovation Cooperation/Hong Kong,Macao and Taiwan Science and Technology Innovation Cooperation Project under Grant 2019YFH0163in part by the Key Research and Development Project of Sichuan Provincial Department of Science and Technology under Grant 2018JZ0071.
文摘In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and directly.In this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is uncertain.This uncertainty would be the main obstacle to assign the task accurately.Consequently,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be caused.In order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and migration.With the help of Stochastic Programming(SP),we use the posteriori recourse to compensate for inaccurate predictions.Meanwhile,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC selection.The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and delay.The results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.
基金supported in part by the National Key R&D Program of China under Grant 2022YFB3104503in part by the China Postdoctoral Science Foundation under Grant 2024M750199+1 种基金in part by the National Natural Science Foundation of China under Grant 62202054,Grant 62002022 and Grant 62472251in part by the Fundamental Research Funds for the Central Universities under Grant BLX202360.
文摘This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)systems.Due to the presence of an eavesdropper(Eve),the system’s com-munication links may be insecure.This paper proposes deploying an intelligent reflecting surface(IRS)on the UAV to enhance the communication performance of mobile vehicles,improve system flexibility,and alleviate eavesdropping on communication links.The links for uploading task data from vehicles to a base station(BS)are protected by IRS-assisted physical layer security(PLS).Upon receiving task data,the computing resources provided by the edge computing servers(MEC)are allocated to vehicles for task execution.Existing blockchain-based computation offloading schemes typically focus on improving network performance,such as minimizing energy consumption or latency,while neglecting the Gas fees for computation offloading and the costs required for MEC computation,leading to an imbalance between service fees and resource allocation.This paper uses a utility-oriented computation offloading scheme to balance costs and resources.This paper proposes alternating phase optimization and power optimization to optimize the energy consumption,latency,and communication secrecy rate,thereby maximizing the weighted total utility of the system.Simulation results demonstrate a notable enhancement in the weighted total system utility and resource utilization,thereby corroborating the viability of our approach for practical applications.
基金This work was supported in part by the Chung-Ang University Research Scholarship Grants in 2021,and in part by R&D Program for Forest Science Technology(Project No.“2021338B10-2223-CD02)provided by Korea Forest Service(Korea Forestry Promotion Institute).
文摘Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively.The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing units.To solve this problem,there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes.However,the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity.They are also challenging to process computation tasks within 100 ms which is the time limit for driving safety.In this paper,we propose a novel offloading scheme that can support autonomous platooning tasks being processed within the limit and ensure driving safety.The proposed scheme can handle computation tasks by considering the communication bandwidth,delay,and amount of computation.We also conduct simulations in the highway environment to evaluate the existing scheme and the proposed scheme.The result shows that our proposed scheme improves the utilization of nearby computing nodes,and the offloading tasks can be processed within the time for driving safety.
文摘After many years of exploitation,onshore oil and gas resources are about to enter a recession period.Oil and gas will mainly come from oceans in the future.Generally speaking,the exploration and production(E&P)cost of oil from offshore is much higher than that of oil from onshore,so it is more sensitive to oil price.However,in recent years,oil price has been hovering at a low level for a long time,almost close to or even lower than the E&P cost of oil,which directly affects the development of oilfields.Besides the influence of oil price,some oilfields present the characteristics of marginal reserve scale,short peak production period and output rapidly declining.There leads to short economic life period and makes the economic benefit close to or lower than oilfield’s hurdle rate,which increases the difficulty of offshore oilfield development.As an important part of oilfield development,Floating Production Storage and Offloading unit,its investment mode and rent mode directly affect overall oilfield’s rate of return and the economic life.This paper chooses lease mode as the research object based on the analysis of investment mode,and further puts forward rent mode related with oil price through the analysis of traditional rent mode,and illustrates the advantages and disadvantages of various rent modes and their applicability so that the lessor chooses the right mode to achieve Win-Win with Oil Company and promotes the development of oilfields under low oil price.
基金funded by the Fundamental Research Funds for the Central Universities(J2023-024,J2023-027).
文摘As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.
基金supported by the National Natural Science Foundation of China(No 62371250)the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu(No BK20212001)the Jiangsu Natural Science Foundation for Distinguished Young Scholars(No BK20220054).
文摘Recent advances in integrating Digital Twins(DTs)with Heterogeneous Vehicular Networks(HetVNets)enhance decision-making and improve network performance.Additionally,developments in Mobile Edge Computing(MEC)support the computational demands of DTs.However,the decentralized nature of MEC systems introduces security challenges and traditional HetVNets fail to efficiently integrate diverse computing and network resources,limiting their ability to handle services for vehicles.This paper presents a novel service request offloading framework for DT-HetVNets to address these issues.In this framework,we design utility functions for vehicles and infrastructures to maximize satisfaction of their requirements through data synchronization and decision-making between DTs and entities.Furthermore,we propose a new honestly based distributed PoA(HDPoA)via scalable work.The interactions between infrastructures and vehicles are modeled as a multi-leader multi-follower(MLMF)game,and we develop a dynamic iterative algorithm to achieve the Nash equilibrium(NE)of the proposed game-theoretic model.Experimental results validate the effectiveness and accuracy of our scheme.
基金funded by the National Key Research and Development Program of China under Grant 2019YFB1803301Beijing Natural Science Foundation (L202002)。
文摘Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading strategies.However,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy performance.In order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy consumption.Firstly,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov property.Secondly,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission delays.Thirdly,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real ones.Numerical results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+2 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)the Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067).
文摘The advent of the internet-of-everything era has led to the increased use of mobile edge computing.The rise of artificial intelligence has provided many possibilities for the low-latency task-offloading demands of users,but existing technologies rigidly assume that there is only one task to be offloaded in each time slot at the terminal.In practical scenarios,there are often numerous computing tasks to be executed at the terminal,leading to a cumulative delay for subsequent task offloading.Therefore,the efficient processing of multiple computing tasks on the terminal has become highly challenging.To address the lowlatency offloading requirements for multiple computational tasks on terminal devices,we propose a terminal multitask parallel offloading algorithm based on deep reinforcement learning.Specifically,we first establish a mobile edge computing system model consisting of a single edge server and multiple terminal users.We then model the task offloading decision problem as a Markov decision process,and solve this problem using the Dueling Deep-Q Network algorithm to obtain the optimal offloading strategy.Experimental results demonstrate that,under the same constraints,our proposed algorithm reduces the average system latency.
基金supported by the National Natural Science Foundation of China(Grant No.62103349)the Henan Province Science and Technology Research Project(Grant No.232102210104).
文摘Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions.We cast the offloading problem as aMarkov Decision Process(MDP)and solve it with Deep Reinforcement Learning(DRL).Specifically,we present a three-tier architecture—end devices,edge nodes,and a cloud server—and enhance Proximal Policy Optimization(PPO)to learn adaptive,energy-aware policies.A Convolutional Neural Network(CNN)extracts high-level features from system states,enabling the agent to respond continually to changing conditions.Extensive simulations show that the proposed method reduces task latency and energy consumption far more than several baseline algorithms,thereby improving overall system performance.These results demonstrate the effectiveness and robustness of the framework for real-time task offloading in dynamic smart-grid environments.
文摘The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-RP23082).
文摘Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G applications.Artificial Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability.In this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task completion.However,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource wastage.Additionally,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities problem.This paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH scenarios.Additionally,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH scenarios.The performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed approach.The simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.
