关键词： Mobile edge computing   Service caching   Task offloading   Resource allocation   Deep reinforcement learning  

摘要： Multiple-Services Mobile Edge Computing enables task-relate services cached in edge server to be dynamically updated, and thus provides great opportunities to offload tasks to edge server for execution. However, the requirements and popularity of services, the computing requirement and the amount of data transferred from users to edge server are dynamic with time. How to adaptively adjust the subset of total service types in the resource-limited edge server and determine the task offloading destination and resource allocation decisions to improve the overall system performance is a challenge problem. To solve this challenge, we firstly convert it into a Markov decision process, then propose a soft actor-critic deep reinforcement learning-based algorithm, called DSOR, to jointly determine not only the discrete decisions of service caching and task offloading but also the continuous allocation of bandwidth and computing resource. To improve the accuracy of our algorithm, we employ an efficient trick of converting the discrete action selection into a continuous space to deal with the key design challenge that arises from continuous-discrete hybrid action space. Additionally, to improve resource utilization, a novel reward function is integrated to our algorithm to speed up the convergence of training while making full use of system resources. Extensive numerical results show that compared with other baseline algorithms, our algorithm can effectively reduce the long-term average completion delay of tasks while accessing excellent performance in terms of stability.

关键词： Data caching   mobile edge computing   online algorithm   cost-effective  

摘要： Mobile Edge Computing (MEC) has emerged to overcome the inability of cloud computing to offer low latency services. It allows popular data to be cached on edge servers deployed within users' geographic proximity. However, the storage resources on edge servers are constrained due to their limited physical sizes. Existing studies of edge caching have predominantly focused on maximizing caching performance from the mobile network operator's perspective, e.g., maximizing data retrieval success rate, minimizing system energy consumption, balancing the overall caching workload, etc. App vendors, as key stakeholders in MEC systems, need to maximize the caching revenue, considering the cost incurred and the benefit produced. We investigate this novel Mobile Edge Data Caching (MEDC) problem from the app vendor's perspective, and prove its $\mathcal {NP}$NP-hardness. We then propose Online MEDC (OL-MEDC), an approach that formulates MEDC strategies for app vendors, without requiring future information about data demands. Its performance is theoretically analyzed and experimentally evaluated. The experimental results demonstrate that OL-MEDC outperforms state-of-the-art approaches by at least 20.41% on average.

关键词： Edge service placement   mobile edge computing   contextual multi-armed bandit   online learning  

摘要： Mobile Edge Computing (MEC), envisioned as a cloud extension, pushes cloud resource from the network core to the network edge, thereby meeting the stringent service requirements of many emerging computation-intensive mobile applications. Many existing works have focused on studying the system-wide MEC service placement issues, personalized service performance optimization yet receives much less attention. As motivated, in this paper we propose a novel adaptive user-managed service placement mechanism, which jointly optimizes a user's perceived-latency and service migration cost, weighted by user-specific preferences. We first formulate the user-managed dynamic service placement process with limited system information as a contextual multi-armed bandit learning problem. In particular, we investigate both cases without and with neighboring edge feedbacks, where the later considers edge information sharing for more informed decision making. For both cases, we design lightweight Thompson-sampling based online learning algorithms, which can efficiently assist the user to make adaptive service placement decisions. We further conduct a novel information-directed theoretical analysis on the regret bound of the proposed online learning algorithms and reveal the structural impact of edge information sharing. Extensive evaluations demonstrate the superior performance gain of the proposed adaptive user-managed service placement mechanism over existing learning schemes.

关键词： mobile edge computing   serial task of-floading   computation partitioning   6G edge intelli-gence   I/O interference  

摘要： Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge *** this paper,we consider a serial task model and design a quality of service(QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems,which can mitigate the I/O interference brought by resource reuse among virtual *** we construct the system utility measuring QoS based on application latency and user devices’energy *** also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O *** results demonstrate the proposed algorithm’s significant advantages in terms of task completion time,terminal energy consumption and system resource utilization.

关键词： NOMA   Three-dimensional displays   Servers   Approximation algorithms   Optimization   Mobile computing   Resource management   3D UAV placement   mobile edge computing   MIMO   machine learning  

摘要： This paper investigates an Unmanned Aerial Vehicle (UAV)-enabled network consisting of smart mobile devices and multiple UAVs as aerial base stations in a Multiple-Input Multiple-Output (MIMO) architecture. Mobile devices are partitioned into several clusters and offload their tasks to the UAV servers via the Non-Orthogonal Multiple Access (NOMA) protocol. The main goal of the paper is to jointly maximize the number of served terrestrial users and their scheduling. Moreover, the number of UAV servers and their 3D placement are optimized. To this end, we formulate an optimization problem subject to some Quality of Service (QoS) constraints. The resulting problem is non-convex and intractable to solve. Therefore, we break the problem into two subproblems. We propose an efficient algorithm based on machine learning to solve the first subproblem, i.e., optimizing the number of UAVs and their 3D placements, and the user association. Different from existing literature, our proposed algorithm can achieve low computational complexity and fast convergence. The second subproblem, the user scheduling, is non-convex too. We utilize the l(p)-norm concept to find a convex upper bound for the subproblem and optimize the user scheduling by applying the Successive Convex Approximation (SCA) algorithm. The aforementioned process is performed iteratively until the overall algorithm converges and a near-optimal solution is achieved for the optimization problem. Moreover, the computational complexity of the proposed scheme is analyzed. Finally, we evaluate the performance of our proposed algorithm via the simulation results. Regarding fast convergence and low computational complexity of the proposed algorithm, its superior performance is confirmed through numerical results.

关键词： Demand response   mobile edge computing   energy consumption   game theory   potential game  

摘要： Mobile edge computing (MEC), as a key technology that facilitates 5G networks, provides a new and prospective mobile computing paradigm that allows the deployment of edge servers at base stations geographically close to mobile users to reduce their end-to-end network latency. Similar to cloud servers, edge servers running 24/7 in an MEC system consume a large amount of energy, contribute a significant proportion of global carbon emissions, and thus require demand response management. Demand response has been widely employed to reduce energy consumption at data centers. However, existing demand response approaches for data centers are rendered obsolete by the new and unique characteristics of MEC systems: 1) proximity constraint - mobile users can be served by neighbor edge servers only;2) latency constraint - mobile users' workloads should be processed by their neighbor edge servers to ensure low latency;and 3) capacity constraint - edge servers have limited computing and communication resources to serve mobile users. Demand response for MEC is further complicated by the non-orthogonal multiple access (NOMA) scheme - the emerging radio access scheme for 5G. Communication resources like channels and transmit power in the NOMA-based MEC system must be systematically considered with computing resources like CPU, memory and storage to fulfill mobile users' resource demands. This paper makes the first attempt to tackle this Edge Demand Response (EDR) problem. We first formulate this problem and prove its $\mathcal {NP}$NP-hardness. Then, we propose a two-phase game-theoretical approach, named EDRGame, to solve the EDR problem. Its performance is theoretically analyzed and experimentally evaluated against three baseline approaches and two state-of-the-art approaches on a widely-used real-world dataset. The results show that it solves the EDR problem effectively and efficiently.

关键词： Mobile edge computing   internet of things   UAVs   ground mobile users  

摘要： Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things(IoT)devices to nearby mobile edge servers,thereby lowering energy consumption and response time for ground mobile users or IoT *** of Unmanned Aerial Vehicles(UAVs)and the mobile edge computing(MEC)server will significantly benefit small,battery-powered,and energy-constrained devices in 5G and future wireless *** address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator(OI),the computational capacity(CC),the power consumption,the time duration,and the optimal location planning *** is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users(MUs)*** paper utilizes the k-means clustering algorithm,the interior point method,and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation *** to simulation results,both local and offloading schemes give optimal solution.

关键词： Multi-access edge computing   Downlink   Servers   Uplink   Resource management   Task analysis   Computational modeling   Heterogeneous cellular networks (HCNs)   mobile device (MD) association   mobile edge caching   mobile edge computing   resource allocation  

摘要： To meet the need of computation-sensitive (CS) and high-rate (HR) communications, the mobile edge computing and caching framework has been widely regarded as a promising solution. When such a framework is implemented in heterogeneous cellular networks (HCNs), it is a key and open topic how to assign mobile edge computing and caching servers to mobile devices (MDs) with CS and HR communications. In this article, under the resource partitioning and resource constraints, joint MD association and resource allocation is performed to minimize the total delay of MDs. In such a formulated problem, the MD association and resource allocation parameters are in a coupling form. In addition, it is also in nonlinear mixed-integer one and hard to tackle. To solve it, we try to develop an alternating optimization algorithm according to the coalitional game and convex optimization theorems. As for the designed algorithm, we provide some detailed convergence, stability, parallel implement, and computation complexity analyses. At last, in the simulation, we introduce other association algorithms for comparison, and investigate the impacts of MD density, time, and frequency partitioning factors on the delay of MDs. Simulation results show that the designed algorithm can achieve lower network delay than existing algorithms in general.

关键词： Bidding auctions   Mobile edge computing   Resource allocation   Social welfare maximization   Vickrey-Clarke-Groves  

摘要： 5G NR-Lite technology still suffers from inadequate response to low latency, high speed and mass connectivity scenarios in high mobility conditions. Building on 4G and 5G, 6G networks will work to more fully enable the Internet of Things (IoT) paradigm, working to provide secure wireless computing for the digital world. In order to ensure the resource utilization of edge computing, it is usually necessary to allocate resources among mobile devices. Many scholars have applied auction theory to resource allocation in mobile edge networks and achieved specific results in recent years. However, most existing studies unilaterally consider the utility of users or edge servers, and few jointly consider social welfare and user selection priorities, which cannot maximize the overall utility of mobile edge networks. This paper proposes a multi-round auction algorithm that pursues maximizing social welfare in a multi-user-multi-server network. The algorithm combines combinatorial auction with double auction, allowing users and servers to adjust their bids and asking prices in each auction round. To enhance the generalizability of the study, the algorithm takes full advantage of the flexibility of the auction mechanism and considers the users' demand for different servers, the variability of bidding strategies, and the variability of the total resources of edge servers so that the users can obtain resources with higher priority and improve social welfare. To ensure that social welfare is maximized, we adopt the Vickrey-Clarke-Groves (VCG) payment mechanism and consider the effect of network externalities. Extensive comparative experiments show that the algorithm can effectively improve social welfare and maximize the overall benefits of the mobile edge network.(c) 2022 Elsevier B.V. All rights reserved.

关键词： neural network model   indoor autonomous driving   LiDAR sensor   resource usage   mobile edge computing  

摘要： Mobile edge computing has been proposed as a solution for solving the latency problem of traditional cloud computing. In particular, mobile edge computing is needed in areas such as autonomous driving, which requires large amounts of data to be processed without latency for safety. Indoor autonomous driving is attracting attention as one of the mobile edge computing services. Furthermore, it relies on its sensors for location recognition because indoor autonomous driving cannot use a GPS device, as is the case with outdoor driving. However, while the autonomous vehicle is being driven, the real-time processing of external events and the correction of errors are required for safety. Furthermore, an efficient autonomous driving system is required because it is a mobile environment with resource constraints. This study proposes neural network models as a machine-learning method for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the range data measured with the LiDAR sensor. We designed six neural network models to be evaluated according to the number of input data points. In addition, we made an autonomous vehicle based on the Raspberry Pi for driving and learning and an indoor circular driving track for collecting data and performance evaluation. Finally, we evaluated six neural network models in terms of confusion matrix, response time, battery consumption, and driving command accuracy. In addition, when neural network learning was applied, the effect of the number of inputs was confirmed in the usage of resources. The result will influence the choice of an appropriate neural network model for an indoor autonomous vehicle.