关键词： Task analysis   Containers   Image edge detection   Scheduling   Feature extraction   Heuristic algorithms   Scheduling algorithms   Mobile edge computing   dependency-aware scheduling   container   reinforcement learning  

摘要： Due to the features of lightweight and easy deployment, the use of containers has emerged as a promising approach for Mobile Edge Computing (MEC). Before running the container, an image composed of several layers must exist locally. However, it has been conspicuously neglected by existing work that task scheduling at the granularity of the layer instead of the image can significantly reduce the task completion time to further meet the real-time requirement and resource efficiency in resource-limited MEC. To bridge the gap, considering the complex dependency between layers and images, a novel layer dependency-aware container scheduling algorithm is proposed to reduce the total task completion time. Specifically: 1) We model the online layer dependency-aware scheduling problem for containers in a heterogeneous MEC, considering the layer download time and task computation time. 2) A policy gradient algorithm is proposed to solve this problem, and the high-dimensional and low-dimensional relations for layer dependencies are extracted with improved action selection. 3) Experiments based on the real-world data trace show that the proposed algorithm outperforms the image-based and layer-based baseline algorithms by 54% and 19% on average, respectively.

关键词： Mobile edge computing   Deep reinforcement learning   Edge intelligence   Server placement   Internet of things  

摘要： In the era of 5G and beyond, Mobile Edge Computing (MEC) has emerged as a technology that seamlessly integrates wireless networks and the Internet, enabling low-latency and high-reliability computing services for mobile users. A crucial prerequisite for deploying MEC is the strategic selection of edge server locations that can satisfy computing demands and improve resource utilization. In this paper, we study the problem of efficient and intelligent dynamic edge server placement considering time-varying network states and placement costs. We present a long-term dynamic decision-making process that models edge server placement as a Markovian decision process and dynamically adjusts server layout. To achieve intelligent decision-making, we propose two deep reinforcement learning-based algorithms. Namely, the DBPA algorithm based on D3QN and the PBPA algorithm based on PPO, which significantly improve the efficiency and performance of model training. We also propose a novel method for transforming network states into network inputs using heat map and grayscale map to enhance the agent's learning efficiency. Experimental results demonstrate that our proposed algorithms achieve intelligent and dynamic placement of edge servers, and outperform comparison algorithms by 13.20% to 61.84% and 23.09% to 66.32%, respectively.

关键词： mobile edge computing   Internet of Things   dynamic offloading   hybrid energy supply   stochastic optimization  

摘要： Mobile edge computing(MEC),as a new distributed computing model,satisfies the low energy consumption and low latency requirements of computation-intensive *** task offloading of MEC has become an important research hotspot,as it solves the problems of insufficient computing capability and battery capacity of Internet of things(IoT)*** study investigates task offloading scheduling in a dynamic MEC *** integrating energy harvesting technology into IoT devices,we propose a hybrid energy supply *** jointly optimize local computing,offloading duration,and edge computing decisions to minimize system *** the basis of stochastic optimization theory,we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called *** can make task offloading decisions by weighing system cost and queue *** quote dynamic programming theory to obtain the optimal task offloading *** results verify the effectiveness of DTOME,and show that DTOME entails lower system cost than two baseline task offloading strategies.

关键词： Mobile edge computing   Mobility management   Handover security   Deep reinforcement learning  

摘要： The experience of users in Mobile Edge Computing (MEC) highly depends on data offloading whose major bottle neck is the design of handover scheme. Existing research advocates that offloading security and quality of service (QoS) should be both considered for an optimal handover. However, recent studies concentrate on improving QoS while neglect security. Due to the complexity of MEC scenarios, generating an optimal handover scheme improving both QoS and security remains a challenge. To solve this problem, we propose SECHO, a secure handover scheme for blockchain-based single-user mobile edge computing system. Then, we design a security policy selection algorithm of offloading tasks to guarantees security performance and controls overhead. Finally, we customize the neural network for a deep reinforcement learning algorithm to realize optimal handover, where a security policy selection method is engaged to collaboratively enhance offloading QoS while decreasing underlying risks. Simulation results show that SECHO leads a 29.6%-302% advantage in overall performance than existing approaches. It proves that our proposal is effective to achieve secure handover while not degrading offloading QoS.

关键词： Mobile edge computing   Computation offloading   Adaptive neighborhood search   Simulated annealing algorithm   Multiuser collaborative model  

摘要： The combination of idle computing resources in mobile devices and the computing capacity of mobile edge servers enables all available devices in an edge network to complete all computing tasks in coordination to effectively improve the computing capacity of the edge network. This is a research hotspot for 5G technology applications and integrating collaborative computing techniques into edge computing. Previous research has focused on the minimum energy consumption and/or delay to determine the formulation of the computational offloading strategy but neglected the cost required for the computation of collaborative devices (mobile devices, mobile edge servers, etc.);therefore, we propose a cost-based collaborative computation offloading model. In this model, when a task requests these devices' assistance in computing, it needs to pay the corresponding calculation cost;and on this basis, the task is offloaded and computed. In addition, for the model, we propose an adaptive neighborhood search based on simulated annealing algorithm (ANSSA) to jointly optimize the offloading decision and resource allocation with the goal of minimizing the sum of both the energy consumption and calculation cost. The adaptive mechanism enables different operators to update the probability of selection according to historical experience and environmental perception, which makes the individual evolution have certain autonomy. A large number of experiments conducted on different scales of mobile user instances show that the ANSSA can obtain satisfactory time performance with guaranteed solution quality. The experimental results demonstrate the superiority of the mobile edge computing (MEC) offloading system. It is of great significance to strike a balance between maintaining the life cycle of smart mobile devices and breaking the performance bottleneck of MEC servers.

关键词： Mobile edge computing   Task offloading   Diverse tasks   Performance measures   Improved PHR algorithm  

摘要： With the rapid development for the technology of Mobile edge computing (MEC), tasks tend to be more diversified and personalized, but fewer scholars considered differentiated quality of service requirements from diverse tasks in task offloading studies. In order to guarantee the real-time performance of latency-sensitive tasks and the throughput of latency-tolerant tasks, we propose a task offloading strategy in a MEC system with a two-tier edge structure. We establish a system model composed of a local model and an edge model to capture the workflow of tasks based on our proposed task offloading strategy, and we derive the key performance measures in terms of the average delay of a latency-sensitive task, the average delay of a latency-tolerant task, the utility of MBS Cluster I and the average power of the MEC system. We carry out experiments with analysis and simulation out to evaluate the long-term performance and validate the effectiveness of our proposed task offloading strategy. Finally, by trading off the average delay of a task and the average power of the MEC system, we formulate an optimization problem with inequality constraints for the average delay of a latency-sensitive task and the stability conditions of the MEC system. Furthermore, we develop an improved Powell-Hestenes-Rockafellar algorithm based on Lagrangian multiplier method to jointly optimize the offloading probabilities of the two types of tasks.

关键词： Task offloading   Queuing theory   Industrial Internet of things   Multi -agent deep reinforcement learning   Mobile edge computing  

摘要： In the Industrial Internet of Things (IIoT), there exist numerous sensor devices with weak computing power and small energy storage. To meet the real-time and big data computing requirements of industrial production, EIIoT (Edge computing-based IIoT) that combines mobile edge computing with IIoT has emerged. It is necessary to offload computing tasks to nearby edge servers for data storage and processing in EIIoT, thus inevitably causing the edge servers to overload. To this end, we propose a jointly constrained optimization model of delay and energy consumption based on queuing theory;this model can effectively solve the task offloading problem in EIIoT. Subsequently, to satisfy the unique offloading requirements of EIIoT, we improve the MAPPO (multi agent proximal policy optimization) algorithm structure to form a lightweight optimal task offloading algorithm called Multi-Agent Deep Reinforcement Learning based on Queuing theory (MAQDRL), which is more suitable for EIIoT. In the algorithm, we systematically integrate queuing theory and use Multi-Agent Deep Reinforcement Learning (MADRL) to obtain the optimal offloading strategy in dynamic and random multiuser offloading environments. We also improve the structure of neural networks of MADRL by analyzing the structural characteristics of the input data. As a result, the algorithm that we proposed exhibits good convergence and exceptional performance in terms of the task arrival rate, bandwidth, energy consumption, latency and other indicators. The simulation results indicate that compared with other classical algorithms, MAQDRL is effective for solving the EIIoT offloading problem. (c) 2023 Elsevier Inc. All rights reserved.

关键词： Task analysis   Servers   Manganese   NOMA   Resource management   Optimization   Downlink   Mobile edge computing   non-orthogonal multiple access   latency-minimization  

摘要： In this letter, we consider a multi-server mobile edge computing (MEC) network where a user with limited computation ability aims to complete an intensive computation task in a short time under the help of multiple edge servers. We study the delay-minimization offloading schemes with and without user's energy constraint, which involve the joint optimization of task allocation ratio for local computation, the wireless offloading manner (i.e., non-orthogonal multiple access (NOMA) offloading or single-server offloading mode), and the server pair selection. Specifically, the joint optimal solutions are derived in semi-closed forms and the critical conditions of switching offloading manners are derived in closed form. Numerical results corroborate the superiority of our proposed offloading scheme in terms of overall latency under various settings.

关键词： internet of vehicles (IoV)   vehicle to everything (V2X)   computing offloading   mobile edge computing   game theory  

摘要： Due to the rocketing development of the Internet of Vehicles (IoV), the growth of computing-intensive and latency-sensitive applications brings a challenge to individual vehicles with limited computing resources. The computation offloading technology provides a feasible solution to this issue. In this paper, a multi-tier symmetric Vehicle-to-Everything (V2X) network framework is proposed, which consists of vehicle nodes (VNs), mobile edge computing (MEC) servers and a cloud server to provide computation offloading services for user vehicles. In this symmetric system, besides local computation, tasks can be offloaded to VNs or MEC servers or cloud servers for processing. The computation offloading problem in this network framework is considered as a game based on game theory. Then, in order to achieve the Nash equilibrium (NE) in this game, a joint optimization of computation offloading and resource allocation (JOCORA) algorithm is proposed. The numerical simulations show that the JOCORA algorithm can improve the success probability of offloading and reduce the total latency. The JOCORA algorithm has a better performance compared to other methods.

关键词： mobile edge computing   network slicing   in-vehicle task offloading   deep reinforcement learning  

摘要： With the surge in tasks for in-vehicle terminals, the resulting network congestion and time delay cannot meet the service needs of users. Offloading algorithms are introduced to handle vehicular tasks, which will greatly improve the above problems. In this paper, the dependencies of vehicular tasks are represented as directed acyclic graphs, and network slices are integrated within the edge server. The Dynamic Selection Slicing-based Offloading Algorithm for in-vehicle tasks in MEC (DSSO) is proposed. First, a computational offloading model for vehicular tasks is established based on available resources, wireless channel state, and vehicle loading level. Second, the solution of the model is transformed into a Markov decision process, and the combination of the DQN algorithm and Dueling Network from deep reinforcement learning is used to select the appropriate slices and dynamically update the optimal offloading strategy for in-vehicle tasks in the effective interval. Finally, an experimental environment is set up to compare the DSSO algorithm with LOCAL, MINCO, and DJROM, the results show that the system energy consumption of DSSO algorithm resources is reduced by 10.31%, the time latency is decreased by 22.75%, and the ratio of dropped tasks is decreased by 28.71%.