关键词： Mobile edge computing   stochastic geometry   heterogeneous network   task offloading  

摘要： The stochastic geometry-based modeling and analysis of large-scale mobile edge computing (MEC) networks are vital for the effective configuration of MEC networks. In this paper, we develop a meta-analytical framework for MEC-enabled heterogeneous networks with the communication-computation-aware (CCA) user association mechanism. Compared with the communication-based user association mechanisms in most existing works, the CCA user association mechanism can capture the impacts of network computation capability on the association process between the user and MEC access point, at the expense of dealing with the more complex coupling of communication and computing. Given the need for interference characterization, we first derive the essential prerequisite quantities (i.e., per-tier association probability, link distance distribution, interferer process intensity, etc.) to represent the computation-dependent interference model. Further, the moment and the meta distribution of the task success offloading probability are derived, based on which we investigate the task execution latency performance, including the communication latency, local computing latency, and edge computing latency. By theoretical analysis and simulation results, it is demonstrated that the proposed analytical framework can provide accurate fine-grained network information for the MEC-enabled HetNets. Moreover, we elaborate on the impacts of the edge computation capability on the network performance and reveal important tradeoffs of the performance metrics.

关键词： Mobile edge computing   Internet of Vehicles   29 road side units   task migration offloading   load balancing  

摘要： Mobile Edge Computing (MEC) has attracted attention for its short-range and low-latency computing services for the Internet of Vehicles (IoV). However, in the Vehicle-RSU-MEC environment, incomplete task migration is a problem when Vehicle Users(VUs) are moving at high speeds around the Road Side Units (RSUs). Additionally, during peak traffic periods or road congestion, completed tasks compete for MEC server computing resources, leading to load imbalance. Therefore, making reasonable migration and offloading decisions is an important challenge. To address this challenge, the paper proposes a Cooperative Offloading strategy to jointly optimize offloading Decisions and Allocation of computing resources (CODA) step by step. First, to solve the problem of incomplete task migration, CODA proposed a Greedy-Based Task Completion Migration (GBTCM) algorithm. The algorithm calculates the required RSU set for each task to achieve complete migration, and greedily searches for the optimal migration target in the corresponding set to reduce task transmission latency. Second, after completing the task migration, CODA proposed a Distance-Based Computing Resource Allocation (DBCRA) algorithm to achieve load balancing for MEC servers. The algorithm prioritizes distance and finds MEC servers with sufficient computing resources to achieve better load balancing performance. Experimental results have shown that CODA is a low-complexity algorithm applicable to IoV, which can make reasonable and rapid decisions for task migration and offloading. Compared to three other benchmarks, CODA exhibits higher effectiveness and superiority.

关键词： MBMS   multimedia services   computation offloading   mobile edge computing   resource allocation  

摘要： With the advancement of multimedia technology, 5G MBMS has become a promising technology that makes full and effective use of network resources to provide services. Mobile edge computing (MEC) is a key technology that brings important improvements to MBMS by utilizing contextual information such as multimedia, virtual reality, etc. However, the deployment of MEC may bring the latency to the network, which requires the deployment consideration of computing nodes. Moreover, mobile network operators need to consider the number of base stations required to accommodate the upcoming MBMS data streams. In this paper, we propose a computational offloading strategy and a co-optimization mechanism of video resource assignment in an edge cloud server enabled MBMS service system. Our approach aims to maximize computation speed while satisfying limited resource constraints, constrained energy consumption, and minimum latency time requirements, in order to provide better MBMS services. Additionally, our approach offers an advanced solution for achieving optimal computational offloading and resource allocation in edge cloud server enabled MBMS service systems. Extensive simulation results demonstrate that the proposed mechanism outperforms current state-of-the-art schemes. Specifically, the total calculation rate of the large-scale cloud server is significantly reduced under energy constraints, total delay requirements, and different network parameters.

关键词： Risk prediction   Mobile edge computing   Intelligent system   Financial risk prediction  

摘要： The financial system has reached its pinnacle because of economic and social growth, which has propelled the financial sector into another era. Public and corporate financial investment operations have significantly risen in this climate, and they now play a significant part in and impact the efficient use of market money. This finance sector will be affected by high-risk occurrences because of the cohabitation of dangers and passions, which will cause order to become unstable and definite financial losses. An organization's operational risk is a significant barrier to its growth. A bit of negligence could cause the business's standing to erode rapidly. Increasing funding management and forecasting risks is essential for the successful development of companies, enhancing their competitiveness in the marketplace and minimizing negative effects. As a result, this study takes the idea of mobile edge computing. It creates an intelligent system that can forecast different risks throughout the financial investment process based on the operational knowledge of important investment platforms. The CNN-LSTM approach, based on knowledge graphs, is then used to forecast financial risks. The results are then thoroughly examined through tests, demonstrating that the methodology can accurately estimate the risk associated with financial investments. Finally, a plan for improving the system for predicting financial risk is put out.

关键词： Mobile edge computing   Task offloading   BBO   Multi-objective  

摘要： The task offloading of mobile edge computing (MEC) is to find proper edge or cloud resources for the execution tasks to efficiently utilize resources and meet different user's requirements. However, it is difficult for task offloading when the number of tasks and resources providers increases and to optimize multiple objectives while satisfying users' requirements. In this paper, a new multi-objective strategy based on the biogeography-based optimization (BBO) algorithm is proposed for MEC offloading to satisfied users' multiple requirements (the execution time, energy consumption and cost). In this strategy, a time-energy consumption model and a cost model are constructed for task offloading firstly. Based on these models, the BBO algorithm is introduced into task offloading for MEC to solve the problem of multi-objective optimization. Compared with the traditional strategies, the offloading strategy based on BBO decreases the average task completion time by an average of 25.03%, and compared with the technique for order preference by similarity to an ideal solution (TOPSIS) strategy, the BBO offloading strategy proposed in this paper reduces energy consumption 75% and cost by 36.9%. The proposed strategy can well solve the problem of multi-objective optimization in the task offloading for MEC.

关键词： Mobile edge computing   service caching   latency   task offloading   stochastic geometry   queuing theory  

摘要： In this paper, we consider the service caching and the computing resource allocation in edge computing (EC) enabled networks. We introduce a random service caching design considering multiple types of latency sensitive services and the base stations (BSs)' service caching storage. We then derive a successful service probability (SSP). We also formulate a SSP maximization problem subject to the service caching distribution and the computing resource allocation. Then, we show that the optimization problem is nonconvex and develop a novel algorithm to obtain the stationary point of the SSP maximization problem by adopting the parallel successive convex approximation (SCA). Moreover, to further reduce the computational complexity, we also provide a low complex algorithm that can obtain the near-optimal solution of the SSP maximization problem in high computing capability region. Finally, from numerical simulations, we show that proposed solutions achieve higher SSP than baseline schemes. Moreover, we show that the near-optimal solution achieves reliable performance in the high computing capability region. We also explore the impacts of target delays, a BSs' service cache size, and an EC servers' computing capability on the SSP.

关键词： Mobile edge computing   reliability   ADMM   computation offloading   compression  

摘要： With the growing popularity of mobile edge computing (MEC), a number of data-intensive applications have been deployed. Quality of Service (QoS), as one of the most important requirements in MEC, has attracted significant attention of both academia and industry. There are two challenges for ensuring the Qos of the data-intensive services. Firstly, besides performance, reliability is another important concern especially for some critical applications, which remains unexplored. Secondly, data compression has to be involved for reducing the heavy workload of wireless communications at the edge of the network. To overcome these challenges, this paper jointly studies the reliability-aware data compression and task offloading for data-intensive applications in MEC. Markov models are constructed to capture the dynamics of the state transitions of MEC systems, and quantitative analyses are conducted for performance and reliability evaluation. For fully taking advantages of data compression in QoS guarantee, we formulate an optimization problem with the objective of minimizing latency while satisfying constraints on reliability and energy consumption. After describing the NP-Hardness of the problem, we apply the techniques of problem linearization and constraint relaxation, transform the original problem into a convex optimization problem. Then, the problem can be solved in a parallel way by introducing the Alternating Direction Multiplier Method (ADMM), and a distributed Reliability-aware Task Processing and Offloading (RTPO) algorithm is presented. Finally, extensive simulation experiments are conducted to validate the efficacy of our approach, the experimental results illustrate the superiority of our approach over both baseline and state-of-the-art algorithm.

关键词： Delays   Resource management   Servers   Federated learning   Virtual machining   Multi-access edge computing   Optimization   DQN   federated learning   cooperative resource allocation   mobile edge computing  

摘要： This paper studies the cooperative resource allocation problem for multi-level services in mobile edge Computing (MEC) networks. The problem is formulated as an optimization problem with the objective of maximizing the successful processing ratio of users' service requests while meeting users' service delay requirements. To solve the formulated problem, we propose a federated learning and deep Q-network (DQN) based cooperative resource allocation (FD-CRA) algorithm. The proposed FD-CRA algorithm uses a couple of DQNs at each edge node to perform spectrum resource pre-allocation, and offloading node selection and computing resource pre-allocation, respectively, for each user's service request. Meanwhile, it uses horizontal federated learning as the basic framework to consistently update all DQNs at each edge node. Simulation results show that the FD-CRA algorithm can significantly improve the successful processing ratio of users' service requests while meeting users' delay requirements.

关键词： Mobile edge computing   Unmanned aerial vehicle   Intelligent reflecting surfaces   Deep reinforcement learning   Physical layer security  

摘要： The deployment of intelligent reflecting surfaces (IRS) on dynamically moving unmanned aerial vehicles (UAVs) can enhance the communication performance of mobile edge computing (MEC), improve the system flexibility, and alleviate eavesdropping on air-ground channels. In this paper, an IRS-equipped unmanned aerial vehicle (UAV)-assisted secure MEC network is proposed. By jointly optimizing the Relay-UAV stopping point, IRS-UAV stopping point, IRS reflection coefficients and the task offloading ratio, the objective of our proposed optimization scheme is to minimize the transmission delay and computing delay while considering the secure transmission performance. To solve this non-convex optimization problem with coupled variables, we propose an intelligent optimization algorithm based on dueling double deep Q networks (D3QN)-deep deterministic policy gradient (DDPG) that can efficiently explore the trajectories and a great number of the IRS reflection elements. Simulation results demonstrate that the intelligent algorithm exhibits good convergence and our proposed scheme can achieve a good balance between system consumption and secrecy rate.

关键词： Mobile edge computing   inference acceleration   DNN partition   task offloading   distributed SAC  

摘要： In mobile edge computing environment, intelligent inference services driven by DNN are highly sensitive to latency. Recently, collaborative inference between User Devices and Edge Servers (ESs) based on Deep Neural Networks (DNN) partition has achieved success in service acceleration. However, most of the existing collaborative acceleration schemes are partitioned for a single DNN inference task, which cannot quickly make partition decisions for a set of concurrent inference tasks, and often sacrifice inference accuracy. In addition, due to the limited resources of ESs, there is resource competition among concurrent requests, which makes the partitioned tasks cannot be offloaded to ESs in time for processing. Therefore, designing an efficient offloading scheme becomes essential. The task offloading schemes based on deep reinforcement learning can solve complex decision-making problems in high-dimensional state space, but they have problems such as insufficient sample diversity and easily falling into local optimum. In this paper, a Collaborative Inference Acceleration Scheme integrating DNN Partitioning and Task Offloading (CIAS-PnO) is proposed. First, while ensuring inference accuracy, the Collaborative DNN Layer Partitioning (CDLP) algorithm is designed with the goal of optimal latency. CDLP can reduce the problem scale of concurrent inference tasks partition by pruning operation and determine the partition decisions in time. Then, the Distributed Soft Actor-Critic (SAC)-based Partition Task Offloading algorithm (DSACO) is designed. DSACO supports SAC Agents to explore samples in parallel and share learning experiences, and uses the automatic entropy adjustment mechanism to improve the exploration efficiency of Agents, so as to avoid falling into local optimum and achieve efficient offloading of partition tasks. Experimental results on DNN benchmarks show that compared with the baseline acceleration schemes, CIAS-PnO achieves more than 19.8% acceleration performa