关键词： Mobile edge computing   Computation offloading   Heterogeneous network   Auction pricing  

摘要： Offloading Mobile Devices(MDs)computation tasks to Edge Nodes(ENs)is a promising solution to overcome computation and energy resources limitations of ***,there exists an unreasonable profit allocation problem between MDs and ENs caused by the excessive concern on MD *** this paper,we propose an auction-based computation offloading algorithm,inspiring ENs to provide high-quality service by maximizing the profit of ***,a novel cooperation auction framework is designed to avoid overall profit damage of ENs,which is derived from the high computation delay at the overloaded ***,the bidding willingness of each MD in every round of auction is determined to ensure MD ***,we put forward a payment rule for the pre-selected winner to effectively guarantee auction ***,the auction-based profit maximization offloading algorithm is proposed,and the MD is allowed to occupy the computation and spectrum resources of the EN for offloading if it wins the *** results verify the performance of the proposed *** with the VA algorithm,the ENs profit is increased by 23.8%,and the task discard ratio is decreased by 7.5%.

关键词： Cooperative relay   Device to device (D2D)   Genetic algorithm   Internet of things (IoT)   Mobile edge computing   Scheduling algorithms  

摘要： As a promising computing paradigm, dispersed computing uses all devices with computing, communication, and storage capabilities as ubiquitous network computing points (NCPs) to achieve low-latency, efficient collaborative computation for tasks, which greatly supports the newly arising computationally intensive applications. However, how to fully utilize the limited resources of NCPs to reduce task latency remains a challenging problem. Considering the heterogeneous communication modes and computing capabilities of NCPs, a distributed multi-hop computing task offloading framework based on an improved genetic algorithm is proposed to address this challenge, where tasks can be recursively offloaded among NCPs. The algorithm reduces the solution space dimension by designing filter chains to filter schedulable nodes before the initialization phase, and improves the population initialization, crossover operator to speed up the convergence, and avoids the risk of overconsumption of resources due to circular scheduling. Compared with the D2D-Fogging and Min-Min algorithms, the experimental results show that our scheduling algorithm improves the global resource utilization by 14% and 8%, and reduces the average task computation delay by 39.18% and 28.21%.

关键词： Task analysis   Servers   Delays   Computational modeling   Energy consumption   Resource management   Pricing   Deep neural networks   mobile edge computing   task partitioning and offloading   aggregative game  

摘要： Deep neural network (DNN)-task enabled mobile edge computing (MEC) is gaining ubiquity due to outstanding performance of artificial intelligence. By virtue of characteristics of DNN, this paper develops a joint design of task partitioning and offloading for a DNN-task enabled MEC network that consists of a single server and multiple mobile devices (MDs), where the server and each MD employ the well-trained DNNs for task computation. The main contributions of this paper are as follows: First, we propose a layer-level computation partitioning strategy for DNN to partition each MD's task into the subtasks that are either locally computed at the MD or offloaded to the server. Second, we develop a delay prediction model for DNN to characterize the computation delay of each subtask at the MD and the server. Third, we design a slot model and a dynamic pricing strategy for the server to efficiently schedule the offloaded subtasks. Fourth, we jointly optimize the design of task partitioning and offloading to minimize each MD's cost that includes the computation delay, the energy consumption, and the price paid to the server. In particular, we propose two distributed algorithms based on the aggregative game theory to solve the optimization problem. Finally, numerical results demonstrate that the proposed scheme is scalable to different types of DNNs and shows the superiority over the baseline schemes in terms of processing delay and energy consumption.

关键词： Mobile edge computing   cloud offloading   delay   task drop   reinforcement learning   mobile-X architecture  

摘要： Mobile Edge Computing(MEC)assists clouds to handle enormous tasks from mobile devices in close *** edge servers are not allocated efficiently according to the dynamic nature of the *** leads to processing delay,and the tasks are dropped due to time *** researchersfind it difficult and complex to determine the offloading decision because of uncertain load dynamic condition over the edge *** challenge relies on the offload-ing decision on selection of edge nodes for offloading in a centralized *** study focuses on minimizing task-processing time while simultaneously increasing the success rate of service provided by edge ***,a task-offloading problem needs to be formulated based on the communication and *** offloading decision problem is solved by deep analysis on taskflow in the network and feedback from the devices on edge *** significance of the model is improved with the modelling of Deep Mobile-X architecture and bi-directional Long Short Term Memory(b-LSTM).The simulation is done in the Edgecloudsim environment,and the outcomes show the significance of the proposed *** processing time of the anticipated model is 6.6 *** following perfor-mance metrics,improved server utilization,the ratio of the dropped task,and number of offloading tasks are evaluated and compared with existing learning *** proposed model shows a better trade-off compared to existing approaches.

关键词： Servers   NOMA   Resource management   Mobile handsets   Mobile computing   Interference   Intercell interference   Dynamic edge user allocation   online approach   primal-dual   mobile edge computing  

摘要： Mobile edge computing (MEC) raises a variety of new challenges for app vendors, including the Edge User Allocation (EUA) problem. EUA aims to allocate as many app users as possible in an MEC system to minimum edge servers in the system. In non-orthogonal multiple access (NOMA)-based MEC system, multiple app users can be allocated to the same subchannel on an edge server through transmit power allocation based on their intra-cell and inter-cell interference. However, allocating excessive app users to the same subchannel may result in severe interference and consequently impact app users' data rates. In addition, in an MEC system, app users join and depart randomly, and thus need to be allocated in an online manner. Existing EUA approaches suffer from poor performance in dynamic real-world NOMA-based MEC systems because they allocate app users in an offline manner and do not consider the complication caused by NOMA. In this paper, we propose OL-EUA, an OnLine approach for solving dynamic EUA problems in NOMA-based MEC systems. Its performance is theoretically analyzed and experimentally evaluated on a public dataset.

关键词： Computational modeling   Stochastic processes   Digital twin   Task analysis   Stability analysis   Vehicle dynamics   Optimization   Mobile edge computing   digital twin   stochastic demand response   congestion control  

摘要： The emergence of Digital Twin Edge Networks (DTENs) achieves the mapping of real physical entities to digital models of cyberspace. By offloading real-time mobile data to Mobile Edge Computing (MEC) servers for processing and modeling, communication-efficient Digital Twin (DT) services could be achieved. However, the spatio-temporal dynamic DT service demand stochastically generated by mobile users easily causes service congestion, which challenges the long-term DT service stability. Meanwhile, current DT services still lack long-term effective incentive designs for participants. To solve these issues, we design an incentive-based congestion control scheme for stochastic demand response in DTENs. First, we adopt the Lyapunov optimization theory to decompose the long-term congestion control decision into a sequence of online edge association decisions, with no need for future system information. We then present a contract-based incentive design to optimize the long-term profit of the DT service provider, comprehensively considering the delay sensitivity, incentive compatibility, and individual rationality. Finally, experimental simulations are carried out to verify the superiority of the proposed scheme with the base station dataset of Shanghai Telecom. Theoretical and simulation analysis demonstrates that compared with benchmarks, our scheme could effectively avoid long-term service congestion with an arbitrarily near-optimal profit.

关键词： Task analysis   Computational modeling   Mobile handsets   Optimization   Servers   Minimization   Bandwidth   Cloud computing   mobile edge computing   caching   latency minimization  

摘要： The proliferation of data-intensive mobile applications is causing latency to become an issue in mobile edge computing (MEC) systems. In this work, we propose a novel methodology that optimizes communication, computation, and caching configurations in MEC to minimize the mean latency experienced by mobile devices. Transmission and computation processes are modeled using M/G/1 queues to account for service rates and warm-up times. Our caching scheme includes time variables for each file at each edge server in determining when to discard files from storage. We theoretically analyze the latency experienced by mobile devices due to communication, computation, and caching, showing how MEC system latency depends on the offloading decisions of mobile devices, bandwidth and CPU resources, and expiration times of files in the storage of edge servers. Our method for solving the latency minimization problem consists of two main components: iNner cOnVex Approximation (NOVA) to deal with non-convexity in the optimization, and an online algorithm for preventing cache storage violations as new tasks arrive and are serviced by the MEC system. Simulation results show that our algorithm outperforms several baselines in minimizing latency, and verify the benefit of including different resource allocation variables in our optimization.

关键词： Mobile edge computing   Task offloading   QoS   Deep reinforcement learning   Federated learning  

摘要： With the arrival of 5G,latency-sensitive applications are becoming increasingly *** Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much attention among *** improve the Quality of Service(QoS),this study focuses on computation offloading in *** consider the QoS from the perspective of computational cost,dimensional disaster,user privacy and catastrophic forgetting of new *** QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning(FL)adaptive task offloading algorithm in *** proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing *** solve the problems of privacy and catastrophic forgetting,we use FL to make distributed use of multiple users’data to obtain the decision model,protect data privacy and improve the model *** the process of FL iteration,the communication delay of individual devices is too large,which affects the overall delay ***,we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of *** simulation results indicate that compared with existing schemes,the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.

关键词： Task analysis   Blockchains   Data mining   Resource management   Security   Servers   Peer-to-peer computing   Blockchain   mobile edge computing   task offloading   block mining   deep reinforcement learning  

摘要： The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme for a blockchain-based MEC system where each edge device not only handles data tasks but also deals with block mining for improving the system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. A multi-objective function is then formulated to maximize the system utility of the blockchain-based MEC system, by jointly optimizing offloading decision, channel selection, transmit power allocation, and computational resource allocation. We propose a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. We then develop a game-theoretic solution to model the offloading and mining competition among edge devices as a potential game, and prove the existence of a pure Nash equilibrium. Simulation results demonstrate the significant system utility improvements of our proposed scheme over baseline approaches.

关键词： mobile edge computing   Industrial Internet   resource allocation   virtual network function   multi-UAVs   deep reinforcement learning  

摘要： This paper focuses on reducing execution delays of dynamic computing tasks in UAV-assisted fault-prone mobile edge computing (FP-MEC) systems, which combine mobile edge computing (MEC) and network function virtualization (NFV) technologies. FP-MEC is suited to meet Industrial Internet (IIN) requirements such as data privacy, low latency, and low-cost industrial scalability in specific scenarios. However, the reliability of virtual network functions (VNFs) deployed on UAVs could impact system performance. Thus, this paper proposes the dynamic task scheduling optimization algorithm (DTSOA) based on deep reinforcement learning (DRL) for resource allocation design. The formulated execution delay optimization problem is described as an integer linear programming problem and it is an NP-hard problem. To overcome the intractable problem, this paper discretizes it into a series of single-time slot optimization problems. Furthermore, the experimental rigor is improved by constructing a real-time server state update system to calculate the real-time server load situation and crash probability. Theoretical analysis and experiments show that the DTSOA has better application prospects than Q-learning and the recent search method (RSM), and it is closer to the traversal search method (TSM).