关键词： Task analysis   Resource management   Computational modeling   Wireless communication   Servers   Machine learning   Analytical models   Mobile edge learning   dynamic task allocation   distributed machine learning   mobile edge computing   federated learning   parallelized learning  

摘要： This paper introduces the new paradigm of Mobile Edge Learning "MEL" that enables the implementation of realistic distributed machine learning (DML) tasks on wireless edge nodes while taking into consideration the heterogeneous computing and networking environments. Therefore, a heterogeneity aware (HA) scheme is designed to solve the problem of dynamic task allocation for MEL in a way that maximizes the DML accuracy over wireless heterogeneous nodes or 'learners' while respecting the time constraints. The problem is first formulated as a quadratically-constrained integer linear program (QCILP). Being NP-hard, it is relaxed into a non-convex problem over real variables which can be solved using commercially available numerical solvers. The relaxation also allows us to propose a solution based on deriving the analytical upper bounds of the optimal solution using Lagrangian analysis and Karush-Kuhn-Tucker (KKT) conditions. The merits of the proposed analytical solution are demonstrated by comparing its performance to the numerical approaches and comparing the validation accuracy of the proposed HA scheme to the baseline heterogeneity unaware (HU) equal task allocation approach. Simulation results show that the HA schemes decrease convergence time up-to 56% and increase the final validation accuracy up-to 8%.

关键词： Joint communication and computing resource allocation (JCCRA)   mobile edge computing   cell-free massive MIMO   multi-agent reinforcement learning  

摘要： To support the newly introduced multimedia services with ultra-low latency and extensive computation requirements, resource-constrained end-user devices should utilize the ubiquitous computing resources available at network edge for augmenting on-board (local) processing with edge computing. In this regard, the capability of cell-free massive MIMO to provide reliable access links by guaranteeing uniform quality of service without cell edge can be exploited for a seamless parallel computing. Taking this into account, we formulate a joint communication and computing resource allocation (JCCRA) problem for a cell-free massive MIMO-enabled mobile edge computing (MEC) network with the objective of minimizing the total energy consumption of the users while meeting the ultra-low delay constraints. To derive efficient and adaptive JCCRA scheme robust to network dynamics, we present a distributed solution approach based on cooperative multi-agent reinforcement learning. The simulation results demonstrate that the proposed distributed approach can achieve comparable performance to a centralized deep deterministic policy gradient (DDPG)-based target benchmark, without incurring additional overhead and time cost. It is also shown that our approach significantly outperforms heuristic baselines in terms of energy efficiency, roughly up to 5 times less total energy consumption. Furthermore, we demonstrate substantial performance improvement compared to cellular MEC systems.

关键词： Healthcare monitoring   Software-defined networks   Mobile edge computing   Priority-aware semi-greedy   Task scheduling  

摘要： Health monitoring systems (HMS) with wearable IoT devices are constantly being developed and improved. But most of these gadgets have limited energy and processing power due to resource constraints. Mobile edge computing (MEC) must be used to analyze the HMS information to decrease bandwidth usage and increase reaction times for applications that depend on latency and require intense computation. To achieve these needs while considering emergencies under HMS, this work offers an effective task planning and allocation of resources mechanism in MEC. Utilizing the Software Denied Network (SDN) framework;we provide a priority-aware semi-greedy with genetic algorithm (PSG-GA) method. It prioritizes tasks differently by considering their emergencies, calculated concerning the data collected from a patient's smart wearable devices. The process can determine whether a job must be completed domestically at the hospital workstations (HW) or in the cloud. The goal is to minimize both the bandwidth cost and the overall task processing time. Existing techniques were compared to the proposed SD-PSGA regarding average latency, job scheduling effectiveness, execution duration, bandwidth consumption, CPU utilization, and power usage. The testing results are encouraging since SD-PSGA can handle emergencies and fulfill the task's latency-sensitive requirements at a lower bandwidth cost. The accuracy of testing model achieves 97 to 98% for nearly 200 tasks.

关键词： Mobile edge computing   collaborative service placement and task scheduling   decentralized algorithm   stochastic optimization  

摘要： Mobile edge computing provides the opportunity for edge systems to underpin a variety of computation-intensive yet delay-sensitive applications. We acknowledge that the limited computing and storage resource of an individual edge server allows only a subset of services to be placed at a time. This raises the question of service placement, which refers to where to place each type of service. Moreover, which service to place allows which type of tasks to be processed, and thereby affects task scheduling performance, which refers to where/whether to schedule each task and task splitting among edge and cloud. Respect of the interactions between service placement and task scheduling (SPTS), a joint optimized design of SPTS is investigated in this article. Our main contribution is a novel spatial-temporal collaboration of SPTS to minimize the overall cost of edge systems. Spatial collaboration is explored to enable edge servers within specific geographic regions collaboratively serve users' demands, while temporal collaboration is explored to optimize the SPTS in multiple time slots and pursue a long-term performance. We first propose an online and decentralized algorithm for spatial-temporal collaborative of SPTS. We then extend the proposed single-timescale of the proposed algorithm into multiple timescales to have service (re)placed at a larger timescale and hence alleviate service interruption. While proved to preserve the asymptotic optimality, the larger timescale slow down the optimal service placement decision. A learn-and-adapt strategy is further designed to speed up the service placement. Theoretical analysis and simulations are performed to validate the efficiency of the proposed algorithm.

关键词： Deep reinforcement learning   energy minimization   mobile edge computing   multiuser noncompletely overlapping   nonorthogonal multiple-access   time varying channel  

摘要： This article investigates the long-term energy minimization of mobile edge computing (MEC) systems with multiuser noncompletely overlapping nonorthogonal multiple access (NOMA) when time varying channel is considered. Different from the existing work with multiuser noncompletely overlapping NOMA that adopted the delay-based successive interference cancellation (SIC) decoding order, we adopt the channel gain-based SIC decoding order, which is widely used and has been proved to be more energy-efficient. We formulate the problem as a joint transmit power, transmission time, and computing resource optimization problem with multiple deeply coupled variables. To ensure the real-time decision-making, the considered problem is characterized as a deep reinforcement learning (DRL) problem by introducing penalty mechanism into the immediate reward function when the constraints are violated, and we propose a soft actor-critic (SAC) algorithm to solve it. Simulations show that the proposed algorithm can significantly improve the system's performance, and, compared with the noncompletely overlapping NOMA mechanism with delay-based SIC decoding order, the completely overlapping NOMA mechanism and time division multiple access (TDMA) mechanism, the noncompletely overlapping NOMA mechanism with channel gain-based SIC decoding order can achieve an average energy consumption reduction of 32.4%, 49.9%, and 74.4%, respectively.

关键词： Mobile edge computing   resource management   task offloading   machine learning   data-intensive applications  

摘要： With the evolution of 5G and Internet of Things technologies, Mobile Edge Computing (MEC) has emerged as a major computing paradigm. Compared to cloud computing, MEC integrates network control, computing, and storage to customizable, fast, reliable, and secure distributed services that are closer to the user and data site. Although a popular research topic, MEC resource management comes in many forms due to its emerging nature and there exists little consensus in the community. In this survey, we present a comprehensive review of existing research problems and relevant solutions within MEC resource management. We first describe the major problems in MEC resource allocation when the user applications have diverse performance requirements. We discuss the unique challenges caused by the dynamic nature of the environments and use cases where MEC is adopted. We also explore and categorize existing solutions that address such challenges. We particularly explore traditional optimization-based methods and deep learning-based approaches. In addition, we take a deeper dive into the most popular applications and use cases that adopt MEC paradigm and how MEC provides customized solutions for each use cases, in particular, video analytics applications. Finally, we outline the open research challenges and future directions.(1)

关键词： maritime Internet of things   mobile edge computing   resource allocation   UAV communication  

摘要： Recently, unmanned aerial vehicle (UAV)-assisted maritime communication systems have drawn considerable attention due to their potential for broadband maritime communication applications. However, their limited energy resources remains a critical issue in providing long-term data transmission support for maritime applications. In this study, an integrated sea-air-terrestrial communication system was constructed for marine data collection, where several unmanned surface vessels (USVs) are deployed to collect marine data from underwater sensors (UWSs), and a UAV hovers above these USVs as a relay node, transmitting marine data from USVs to an onshore base station (BS). To prolong the lifetime of the UAV relay, mobile edge computing technology is applied in USVs for partial data computing, which reduces the to-be-relayed data volume from UAVs to USVs to onshore BS as well as the relay energy consumption of UAV. A parallel data computing and transmission scheme was developed for simultaneous local data computing and relaying in the proposed system. Accordingly, a UAV energy consumption minimization problem was formulated with constraints on the USV's computational ability, the USV's transmission power budget, the UAV transmission power budget, and the maximum system latency. To effectively solve this nonconvex optimal problem, an energy optimal partial data computing and relaying strategy was constructed by successively optimizing the data partial computational offloading ratio, USV transmit power allocation, and UAV transmit power. Numerical simulations were used to verify the effectiveness of the proposed strategy.

关键词： Servers   Reinforcement learning   Deep learning   Correlation   Training   Multi-access edge computing   IEEE transactions   Mobile edge computing   file caching strategy   multi-agent deep reinforcement learning  

摘要： Mobile edge computing (MEC) reduces data service latency by pushing data to the network edge. However, due to the dynamic and diverse requests of mobile users, the problem of mobile edge caching is more complex than cloud caching. Therefore, the existing model-based caching strategies cannot be directly used in the mobile edge caching environment. Besides, when taking the cooperative storage relationship between neighbor edge servers into consideration, the caching problem becomes more difficult. To this end, we formulate an mobile edge caching problem to minimize the total latency in mobile edge computing. Firstly, a heuristic caching strategy is proposed to solve the mobile edge caching problem in the single-time-slot scenario. Then, with the consideration of users' mobility and the correlation of files, we propose a caching strategy for the multiple-time-slot scenario based on multi-agent deep reinforcement learning. To address the cold start problem in deep reinforcement learning, we adopt the proposed heuristic caching strategy used in the single-time-slot scenario to further optimize the training results. Extensive experiments on generated data and real-world datasets are conducted to verify that the proposed edge caching strategies can achieve the minimum latency compared with the state-of-the-art strategies.

关键词： Deep belief network   Markov approximation   Mobile edge computing   Energy efficiency and entropy encoding  

摘要： The rising popularity of health-related wearables has increased the demand for computational offloading. Latency, battery life, and compute capabilities are all areas that could use significant improvement despite the massive expansion of wearable devices and offloading techniques. There are a number of issues caused by the limited battery life of mobile devices, so it is essential to conserve power by moving responsibilities for running applications to the cloud. This is especially crucial in situations where scheduling is being done in a dynamic mobile cloud computing environment. In this piece, we highlight the fact that most smart wearable devices can pair with smartphones via Bluetooth, and that this connection is significantly more power-efficient than 3G/LTE or Wi-Fi communication. To be more precise, we investigate the origins of this phenomenon. New computing technology, Mobile Edge Computing (MEC), may be the answer to capacity and performance problems in older systems like Mobile Cloud Computing (MCC). Some examples of these problems include excessive latency, a clogged core network, a lack of quality of experience (QoE), and the wasteful consumption of expensive resources like power and data transfer. To address these issues in the MEC setting, we introduced a Deep Belief Network (DBN) equipped with a Markov Approximation Algorithm (MAA). Because of this, a choice could be made that takes into account energy use, time constraints, and system load. AFCFS, MINET, and trade-off judgments for code offloading are all examples of state-of-the-art techniques that can be outperformed by this approach (TRADEOFF). Using entropy encoding as a post-processing step after quantization allows for lossless compression of an image. It allows for a picture to be shown in a way that is both more effective and requires less space for storage or transmission. Our proposed DBN-MAA reduces energy consumption while simultaneously increasing the number of completed jobs, as show

关键词： mobile edge computing   wireless energy transfer   reconfigurable intelligent surfaces   offloading   unmanned aerial vehicle   rate-splitting multiple access  

摘要： In this study, a reconfigurable intelligent surface (RIS)-assisted wireless-powered mobile edge computing (WP-MEC) system is proposed, where a single-antenna unmanned aerial vehicle (UAV)-mounted cloudlet provides offloading opportunities to K user equipments (UEs) with a single antenna, and the K UEs can harvest the energy from the broadcast radio-frequency signals of the UAV. In addition, rate-splitting multiple access is used to provide offloading opportunities to multiple UEs for effective power control and high spectral efficiency. The aim of this paper is to minimize the total energy consumption by jointly optimizing the resource allocation in terms of time, power, computing frequency, and task load, along with the UAV trajectory and RIS phase-shift matrix. Since coupling issues between optimization variable designs are caused, however, an alternating optimization-based algorithm is developed. The performance of the proposed algorithm is verified via simulations and compared with the benchmark schemes of partial optimizations of resource allocation, path planning, and RIS phase design. The proposed algorithm exhibits high performance in WP-MEC systems with insufficient resources, e.g., achieving up to 40% energy reduction for a UAV with eight elements of RIS.