关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC), along with device-to-device (D2D) assisted MEC (D-MEC), are promising technologies that could improve the quality-of-experience for mobile devices (MDs) by offloading their tasks to edge servers or nearby idle MDs. There is a popular trend to develop distributed task offloading algorithms using multi-agent reinforcement learning (MARL), whose adoption of central critics during training makes the offloading still size-sensitive. Therefore, this paper proposes a Size-Insensitive Task Offloading (SITOff) algorithm for D-MEC based on fully-distributed offloading without maintaining any central venue. Specifically, taking advantage of the inherent graph-like structure of D-MEC, SITOff adopts graphs to represent MDs' states and relationships and form each MD's local knowledge about D-MEC through graph computation. Furthermore, considering the limitation of local knowledge in performing whole performance-oriented offloading, each MD utilizes D2D-transmitting to exchange knowledge with its neighbors and form a comprehensive knowledge about D-MEC to enhance the coordination of distributed offloading. Additionally, regarding the different impacts of neighbors' knowledge, each MD leverages attention mechanisms to selectively learn its neighbors' knowledge during knowledge-exchange. Extensive experimental results show the superiority of SITOff over state-of-the-art MARL-based offloading algorithms in D-MEC with various MDs, and the easy collaboration of SITOff with curriculum-learning for large-scale D-MEC offloading. © 2002-2012 IEEE.

关键词： Reconfigurable intelligence surface   mobile edge computing   spectral efficiency   spectral efficiency   energy efficiency   energy efficiency   penalty based approach   penalty based approach   semidefinite programming   semidefinite programming   semidefinite programming  

摘要： Mobile edge computing (MEC) is emerging as a critical technology for supporting latency-sensitive and computation-intensive services-however, random wireless channel fading limits offloading rates, posing a significant challenge to MEC performance. In MEC systems, effective energy management and high-speed communication links between user devices and MEC servers are essential for supporting services that require low latency and high computation power. Reconfigurable intelligent surfaces (RIS) have been proposed as a promising solution to enhance the quality of communication links between users and MEC servers by dynamically reconfiguring the wireless propagation environment to overcome these challenges. We formulate a trade-off optimization problem to balance SE and EE in RIS-aided MEC systems, which is crucial due to limited system resources and the need for dynamic adaptation to varying network requirements-aimed at joint optimization of transmission power, phase-shift matrix, and MEC offloading and computation delays. Given the problem's intractability, we develop an alternating optimization-based iterative algorithm incorporating quadratic transformation and successive convex approximation techniques to obtain sub-optimal solutions. Firstly, we address the minimum delay power allocation and task offloading by using quadratic transformations for fractional problems and closed-form solutions. Afterward, we optimize the phase shifts through semidefinite programming and a penalty-based approach. Simulation results validate the effectiveness of the proposed framework, demonstrating significant improvements in SE and EE compared to conventional systems without RIS or with static RIS configurations.

关键词： Mobile edge computing  

摘要： We study resource allocation and task scheduling for mobile Artificial Intelligence Generated Content (AIGC) in a three-layer cloud-edge-device network. Escalating industry demand for computational resources presents significant challenges in resource allocation and optimization, particularly for edge-side AIGC, which faces high computational costs and requires advanced techniques for efficient model deployment on mobile devices. Optimal resource allocation in mobile AIGC networks is naturally formulated into a 0-1 ILP, which is proven NP-hard. We reformulate the problem into both its Comp-Exp and dual forms. Then we design an online auction framework OATS to optimize decisions on instances and time schedules, maximizing social welfare for the AIGC ecosystem. Our analysis demonstrates that OATS achieves high social welfare through appropriate bid acceptance and resource allocation. Simulation results corroborate the theoretical analysis, showcasing the efficacy of our online algorithms. © 2024 IEEE.

关键词： Resource management   Decoding   Task analysis   Signal processing algorithms   Time division multiple access   Performance evaluation   NOMA   Mobile edge computing   computation offloading   multiple access   resource allocation  

摘要： When multiple devices seek to offload computational tasks to their access point, the nature of the multiple access scheme plays a critical role in the system performance. For a system with heterogeneous tasks, we adopt a time-slotted signaling architecture in which different numbers of devices transmit in each slot, subject to individual power constraints. We consider the problem of jointly selecting the devices that will offload, along with optimizing their communication resources (their powers and rates in each time slot, and the time slot lengths) so as to minimize the a weighted sum of the energy expended by the devices. We employ a customized tree search algorithm for the offloading decisions in which a resource allocation problem is solved at each node. For time-division multiple access (TDMA) and "rate optimal" multiple access, we obtain reduced-dimension convex formulations of the resource allocation problem. For non-orthogonal multiple access (NOMA) with independent decoding (ID) or fixed-order sequential decoding (FOSD) we show that the resource allocation problem has a difference-of-convex structure and we develop a successive convex approximation algorithm with feasible point pursuit. Furthermore, for the FOSD scheme we obtain a closed-form expression that provides the optimal decoding order when it is feasible, and efficient algorithms for finding a good decoding order when it is not. Our results capture the inherent tradeoffs between the complexity of a multiple access scheme (and its resource allocation algorithm), and its performance in the computation offloading application.

关键词： Convolutional neural network   cyber physical system   firefly algorithm   intelligent transportation system   mobile edge computing  

摘要： In the present scenario, the transportation Cyber Physical System (CPS) improves the reliability and efficiency of the transportation systems by enhancing the interactions between the physical and cyber systems. With the provision of better storage ability and enhanced computing, cloud computing extends transportation CPS in Mobile Edge Computing (MEC). By inspecting the existing literatures, the cloud computing cannot fulfill the requirements in transportation CPS like lower context-awareness and latency. For enhancing the context-awareness and reducing the latency in a realistic MEC environment, an efficient portable deep learning model: Convolutional Neural Network (CNN) with Chaotic Levy Flight based Firefly Algorithm (CLFFA) is implemented in this article. In the CNN model, the CLFFA selects the appropriate hyper-parameters or reduces the redundant parameters that results in minimal model size and inference latency than the traditional CNN models. Additionally, the CNN-CLFFA model significantly outperformed the existing models by means of recall, accuracy, F1-score, and precision on the benchmark datasets like German Traffic Sign Recognition Benchmark (GTSRB), MIOvision Traffic Camera Dataset (MIO-TCD) classification, and VCifar-100 datasets. The numerical analysis demonstrates that the CNN-CLFFA model obtained maximum accuracy of 99.02%, 99.11%, and 99.03% on the VCifar-100, MIO-TCD, and GTSRB-T datasets, which are superior to the traditional models.

关键词： Mobile edge computing  

摘要： Mobile edge computing offers a promising approach for providing computation and storage services to user terminals. However, the computational resources deployed on the base stations or fixed locations are insufficient for temporary emergency scenarios. To expand mobile edge computing capacity, an air-ground cooperation architecture that leverages the low cost, rapid deployment, and mobility of low-altitude platform is proposed. A joint optimization strategy for air-ground cooperation caching and content delivery is introduced to reduce delays caused by limited wireless backhaul capacity, energy constraints of edge nodes in air (ENAs), and repeated content delivery. This strategy incorporates trajectory planning of UAVs, transmission power allocation, downlink bandwidth allocation, content caching, and user association. Content popularity is predicted using an LSTM network based on historical data. We employ the block coordinate descent (BCD) method to address the optimization problem and design the popularity prediction-based air-ground cooperation caching and content delivery (PP-AG3C) algorithm. Numerical simulations show that our algorithm outperforms benchmark algorithms in average delivery delay, data transmission energy, and cache hit rate. When the number of user terminals is 60, compared with PP-AG3C algorithm, The average data transmission energy consumption of TPCU-AG3C algorithm, TCU-AG3C algorithm, TC-AG3C algorithm, RT-AG3C algorithm and FT-AG3C algorithm increased by 30.79%, 49.41%, 76.70%, 152.85% and 51%, respectively. © 2014 IEEE.

关键词： Mobile edge computing  

摘要： The surge in mobile vehicles and data traffic in Vehicular Edge Computing and Networks (VECONs) requires innovative approaches for low latency, stable connectivity, and efficient resource usage in fast-moving vehicles. Existing studies have identified that utilizing digital twins (DTs) can effectively improve service quality in VECONs. However, it still faces substantial challenges posed by large-scale complex DT communications in sustaining real-time collaborative endeavors. In particular, within the dynamic VECONs, the decision regarding DT migration plays a pivotal role in sustaining the quality of services. In this paper, we propose an adaptive DT migration (ADM) algorithm to minimize the overall migration costs when DTs deliver services. Specifically, 1) We formulate ADM as a combinatorial optimization problem in VECONs, comprehensively considering communication latency and migration latency under complex DT communications, vehicular mobilities, and dynamic states of edges;2) An ADM algorithm based on off-policy actor-critic reinforcement learning is proposed to make migration decisions. Moreover, the ADM agent employs warm-up policies to address exploration challenges in sparse state spaces;3) Simulations based on real-world, large-scale urban vehicular mobility datasets demonstrate that our method outperforms existing algorithms by approximately 39% on average, and it can achieve results close to the optimal. © 1967-2012 IEEE.

关键词： Computation offloading   Genetic algorithm   Mobile edge computing   Particle swarm optimization  

摘要： In recent years, edge computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited devices. The main feature of edge computing is to push computation, networking, and storage facilities closer to the network edge. This enables user equipment (UE) to profit from the edge computing paradigm by mainly offloading their intensive computation tasks to edge resources. Because of some rising problems such as inherent software and hardware heterogeneity, restrictions, dynamism, and stochastic behaviour of the ecosystem, the computation offloading issues consider as the essential challenging problems in the MEC environment. However, to the best of the author's knowledge, in spite of its significance, in Metaheuristic-based computation offloading mechanisms, there is not any systematic, comprehensive, and detailed survey in the MEC environment. In this paper, we provide a review on the Metaheuristic-based computation offloading mechanisms in the MEC environment in the form of a classical taxonomy to identify the contemporary mechanisms on this crucial topic and to offer open issues as well. The proposed taxonomy is classified into three main fields: genetic algorithm (GA)-based mechanisms, Partical Swarm Optimization (PSO)-based mechanisms, and Hybrid GA with PSO-based mechanisms. Next, these classes are compared with each other based on the essential features such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and weaknesses are discussed, as well. Finally, open issues and uncovered or inadequately covered future research challenges are argued, and the survey is concluded. © 2024, Semarak Ilmu Publishing. All rights reserved.

关键词： Task analysis   Computational modeling   Stability analysis   Optimization   Matrix decomposition   Heuristic algorithms   Feature extraction   Computation rate   data offloading   deep reinforcement learning (DRL)   mobile edge computing   orthogonal non-negative matrix factorization (NMF)   queue stability  

摘要： Data-intensive applications coupled with limited mobile resources make opportunistic computation offloading imperative. Therefore, efficient and reliable offloading strategies are crucial for achieving optimal performance in terms of stabilizing data queues at various data rates, efficient task management and reduced waiting times for users. This paper proposes a novel algorithm, Matrix Factorization-Based Deep Reinforcement Learning Approach for Stable Online Offloading in Mobile Edge Networks (MFDROO), that combines Orthogonal Non-negative Matrix Factorization (ONMF) and a Deep Reinforcement Learning approach (DRL) to address the problem of stable online offloading in large networks. The proposed algorithm utilizes ONMF to model network resource heterogeneity by decomposing it into a set of features, capturing the underlying structure of data traffic flows by removing irrelevant information from data. This reduces computational overhead and improves performance. Additionally, MFDROO incorporates a DRL agent to learn optimal offloading decisions over time. By utilizing ONMF in conjunction with DRL, MFDROO overcomes online offloading challenges. It optimizes user computation rates and enhances system performance. Additionally, it maintains data-queue stability for large networks or higher data rates. Large-scale network simulations were extensively conducted to demonstrate MFDROO effectiveness. Maintaining stability while scaling up the users number is a challenge in network computation. Our results demonstrate that MFDROO tackles this challenge and ensures that even with a 3.3% increase in user numbers (up to 100 users), computation networks remain stable and outperform other existing algorithms in terms of utility maximization and stability. This improvement ensures optimal performance and provides scalability and efficiency for various applications.

关键词： Mobile edge computing  

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. © 2004-2012 IEEE.