关键词： Mobile edge computing   Dynamic pricing   Deep reinforcement learning   Computation offloading  

摘要： The research on Mobile Edge Computing (MEC) has attracted considerable attention in recent years. However, previous studies often confined themselves to exploring the issue of task resource scheduling from a single perspective of mobile terminals or edge servers, or failed to take into account the growth of users, thus unable to face complex and diverse practical scenarios and effectively address various challenges in task offloading. In view of this, this paper adopts a more comprehensive and in-depth perspective, considering not only the user's performance benefits and the service provider's economic benefits, but also conducting a thorough analysis of the potential continuous growth of user numbers in actual scenarios. To effectively address the rapid increase in user numbers within large-scale networks, we employ a decentralized approach where each user independently makes decisions regarding their own offloading strategies, rather than relying on centralized decision-making by the server. Based on this, we propose an innovative edge computing task offloading scheme that is particularly suitable for large-scale network environments. Specifically, we combine the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and innovatively propose a decentralized task offloading scheme called DSMECO-DP. In this scheme, considering the dynamic nature of networks and user volatility, the server aims to maximize profit through coarse time scale dynamic pricing, while the mobile terminal reduces its own costs by bidding on a narrow time scale considering multiple objectives such as time delay, energy consumption, and payment cost. Simulation results demonstrate the effectiveness of the TD3 algorithm compared to other reinforcement learning algorithms, as well as the effectiveness of the dynamic pricing mechanism. Furthermore, the processing time of this algorithm is significantly reduced compared to other centralized algorithms.

关键词： Mobile edge computing   Multi-agent deep reinforcement learning   Computation offloading  

摘要： Mobile Edge Computing (MEC) has emerged as an effective paradigm for reducing latency and enhancing computational efficiency. However, the rapid proliferation of edge servers and user devices has significantly increased the complexity of task processing and resource management. Traditional task offloading approaches often rely on centralized decision-making, resulting in high computational complexity and time costs. To address these challenges, this paper introduces a dynamic collaborative framework involving multiple users and edge servers. We formulate the problem of resource allocation and task offloading as a multi-objective Markov Decision Process (MDP) with a mixed action space. To solve this, we propose a novel algorithm called Multi-Agent Mobile Edge Computing (MA-MEC), which leverages multi-agent reinforcement learning. In MA-MEC, each mobile edge server (MES) operates as an independent learning agent. Through centralized training and decentralized execution, these agents collaborate to develop efficient task offloading strategies in complex and dynamic edge environments. Simulation results demonstrate the effectiveness of our approach. MES agents learn to execute tasks more efficiently, increasing the number of processed tasks by 12.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, while task offloading rates rise by 17%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, and time costs are reduced by 53%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepa

关键词： Optimization   Servers   Resource management   Measurement   Genetic algorithms   5G mobile communication   Particle swarm optimization   Internet of Things   Heuristic algorithms   Delays   5G   mobile edge computing   task offloading   mixed integer (MILP) linear optimization   latency   dropped task ratio   task prioritization  

摘要： Multi-Access Edge Computing (MEC) is widely recognized as an essential enabler for applications that necessitate minimal latency. However, the dropped task ratio metric has not been studied thoroughly in literature. Neglecting this metric can potentially reduce the system's capability to effectively manage tasks, leading to an increase in the number of eliminated or unprocessed tasks. This paper presents a 5G-MEC task offloading scenario with a focus on minimizing the dropped task ratio, computational latency, and communication latency. We employ Mixed Integer Linear Programming (MILP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to optimize the latency and dropped task ratio. We conduct an analysis on how the quantity of tasks and User Equipment (UE) impacts the ratio of dropped tasks and the latency. The tasks that are generated by UEs are classified into two categories: urgent tasks and non-urgent tasks. The UEs with urgent tasks are prioritized in processing to ensure a zero-dropped task ratio. Our proposed method improves the performance of the baseline methods, First Come First Serve (FCFS) and Shortest Task First (STF), in the context of 5G-MEC task offloading. Under the MILP-based approach, the latency is reduced by approximately 55% compared to GA and 35% compared to PSO. The dropped task ratio under the MILP-based approach is reduced by approximately 70% compared to GA and by 40% compared to PSO.

关键词： Optimization   Heuristic algorithms   Resource management   Processor scheduling   Dynamic scheduling   System performance   Transforms   Routing   Multi-access edge computing   Iterative methods   Mobile edge computing   service caching and workload scheduling   Lyapunov optimization   cloud load  

摘要： Joint service caching and workload scheduling plays an important role in ultra-dense mobile edge computing (MEC) networks to satisfy the stringent requirements of latency-critical services by leveraging the aggregated edge resources (e.g., storage and computing resources) located near the users. However, most of the existing methods incorporating popularity-based and/or size-aware caching strategies fail to match the resource demands of user requests with the heterogeneous resources of edge nodes, leading to heavier cloud load and higher latency. It becomes even worse when user requests exhibit dynamic variations over time. To address these issues, we propose an online approach for resource-efficient joint service caching and workload scheduling in ultra-dense MEC networks, called CoShare. The core idea is to fully utilize the heterogeneous resources of the edge layer to further reduce the cloud load and thus the service latency. First, we formulate the joint service caching and workload scheduling problem as a mixed integer nonlinear programming problem with the goal of minimizing the cloud load. Then, an online algorithm is developed to transform this optimization problem into a series of per-slot sub-problems by leveraging Lyapunov optimization. Next, to solve these sub-problems, we design a cacheability-based alternating iterative algorithm utilizing Gibbs sampling, in which the cacheability indicator considers both service resource demands and service popularity. Finally, simulation results show that CoShare can effectively exploit available edge resources to achieve lower cloud loads compared to other strategies.

关键词： Mobile edge computing  

摘要： Video analytics demand substantial computing resources, posing significant challenges in computing resource-constrained environment. In this paper, to achieve high accuracy with acceptable computational workload, we propose a cost-effective regions of interest (RoIs) extraction and adaptive inference scheme based on the informative encoding metadata. Specifically, to achieve efficient RoI-based analytics, we explore motion vectors from encoding metadata to identify RoIs in non-reference frames through morphological opening operation. Furthermore, considering the content variation of RoIs, which calls for inference by models with distinct size, we measure RoI complexity based on the bitrate allocation information from encoding metadata. Finally, we design an algorithm that prioritizes scheduling RoIs to models of the appropriate complexity, balancing accuracy and latency. Extensive experimental results show that our proposed scheme reduces latency by nearly 40% and improves 2.2% on average in accuracy, outperforming the latest benchmarks. Copyright © 2025, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： The advent of edge intelligence and escalating concerns for data privacy protection have sparked a surge of interest in device-cloud collaborative computing. Large-scale device deployments to validate prototype solutions are often prohibitively expensive and practically challenging, resulting in a pronounced demand for simulation tools that can emulate real-world scenarios. However, existing simulators predominantly rely solely on high-performance servers to emulate edge computing devices, overlooking (1) the discrepancies between virtual computing units and actual heterogeneous computing devices and (2) the simulation of device behaviors in real-world environments. In this paper, we propose a high-fidelity device simulation platform, called SimDC, which uses a hybrid heterogeneous resource and integrates high-performance servers and physical mobile phones. Utilizing this platform, developers can simulate numerous devices for functional testing cost-effectively and capture precise operational responses from varied real devices. To simulate real behaviors of heterogeneous devices, we offer a configurable device behavior traffic controller that dispatches results on devices to the cloud using a user-defined operation strategy. Comprehensive experiments on the public dataset show the effectiveness of our simulation platform and its great potential for application. The code is available at https://***/opas-lab/olearning-sim. © 2025, CC BY.

关键词： Mobile edge computing  

摘要： Nowadays, the convergence of Mobile Edge Computing (MEC) and vehicular networks has emerged as a vital facilitator for the ever-increasing intelligent onboard applications. This paper introduces a multi-tier task offloading mechanism for MEC-enabled vehicular networks leveraging vehicle-to-everything (V2X) communications. The study focuses on applications with sequential subtasks and explores two tiers of collaboration. In the vehicle tier, we design a needing vehicle (NV)-helping vehicle (HV) matching scheme and inter-vehicle collaborative computation is studied, with joint optimization of task offloading decision, communication, and computation resource allocation to minimize energy consumption and meet latency requirements. In the roadside unit (RSU) tier, collaboration among RSUs is investigated to address multi-access issues of bandwidth and computation resources for multiple vehicles. A two-step method is proposed to solve the subchannel allocation problem. Detailed experiments are conducted to demonstrate the effectiveness of the proposed method and assess the impact of different parameters on system energy consumption. Copyright © 2025, The Authors. All rights reserved.

关键词： Mobile edge computing   Blockchain   Matching theory   Contract theory   Information scenario  

摘要： The application of blockchain technology on mobile devices requires significant computing resources. However, the limited capacity of mobile devices cannot meet the high computing power requirements of blockchain technology, which limits the development and implementation of blockchain application. Mobile Edge Computing (MEC) is capable of alleviate the limited computing capacity of IoT terminals. This paper introduces a MEC-enabled blockchain system consisting of an edge computing allocation center (ECAC), multiple edge service providers (ESPs), and multiple miners. To efficiently allocate resources between these ESPs and miners, a hierarchical computation offloading strategy based on contract theory and matching game is proposed. During contract design phase, ECAC makes a contribution-reward contract to attract ESPs to join the trading market, and provide services to miners. By analyzing the attributes and conditions of feasible contract, the optimal contract is devised using Lagrange multiplication. During matching phase, an iterative matching algorithm (IMA) is proposed to achieve the matching between ESPs and miners by constructing preference sets. Both the stability and convergence of IMA have been proved by theoretical analysis. Finally, we conduct experiments to validate the feasibility and effectiveness of the design contract, and the stability of IMA has also demonstrated by experimental results.

关键词： Mobile edge computing   Computation offloading   Task migration   Deep reinforcement learning  

摘要： The space-air-ground-sea integrated network (SAGSIN) integrates diverse heterogeneous networks, which makes the pervasive communication and computing service of maritime users possible. In SAGSIN, communication nodes are scarce and computing resources are tight. Tasks of users can be offloaded to the network edge, such as at surface ship, buoy and drone. However, some traditional task offloading decisions become inapplicable as users move or the topology of networks changes. Therefore, in order to ensure the continued effectiveness of the task offloading strategy in dynamic ocean scenarios, we need to design an efficient offloading method to improve the overall network performance. To address this issue, we propose a resource-aware deep reinforcement learning method to optimize task offloading decisions. In this scheme, the available resources of mobile users are perceived in real time, and efficient computation offloading and task migration decisions are made based on the results. Simulation results validate the effectiveness of the proposed algorithm. In addition, the simulation results show that the proposed algorithm can effectively reduce the system delay and energy consumption as the number of tasks increases, and reduce task migration.

关键词： Mobile edge computing   Internet of things   Deep reinforcement learning   Task offloading   Low carbon emission  

摘要： Mobile edge computing is an emerging computing paradigm in the Internet of Things. Task offloading is a critical method in mobile edge computing to alleviate computational resource constraints. Nowadays, the rising number of tasks is placing greater demands on computing resources. The increasing consumption of computing resources leads to high carbon emission. Achieving environmentally friendly mobile edge computing while effectively managing low-carbon task offloading poses a significant challenge. Recently, deep reinforcement learning has made certain progress in many research fields. However, there are few deep reinforcement learning methods that consider the carbon emission in task offloading. In this paper, we propose a deep reinforcement learning based low carbon emission task offloading algorithm for minimizing carbon emission in mobile edge computing. Firstly, since different base stations exist in the mobile edge computing environment, we consider the mobile edge computing environment with multiple heterogeneous agents. Secondly, to minimize carbon emission, we consider the carbon intensity of the base station as an optimization factor. We conclude the task offloading strategy to minimize carbon emission, consequently achieving the minimization of carbon emission. Moreover, our proposed algorithm allows user devices to decide their own preference for task offloading. Based on the specific requirements and preferences of user devices, our proposed algorithm can dynamically adjust the weights of delay, energy consumption, and carbon emission, respectively. Experiments indicate that the proposed algorithm can accurately and quickly conclude the task offloading strategy to minimize carbon emission.