关键词： Mobile edge computing   Nonorthogonal multiple access   Computation offloading   Resource allocation  

摘要： Due to explosive growth of computation-intensive applications, multi-access mobile edge computing (MEC) has been proposed as an efficient technology to relieve the burden on mobile users by offloading computation tasks to MEC servers. Meanwhile, nonorthogonal multiple access (NOMA) has been proposed to improve spectrum efficiency by allowing multiple users transmitting on the same time and spectrum resource. Thus, NOMA can be naturally adopted by the MEC systems to improve the offloading performance. In this paper, we consider a NOMA-based multi-access MEC system with multiple MEC servers and multiple users. It is assumed that each user has multiple computation tasks, where each task shall be offloaded to one MEC server for computing. NOMA is applied for uploading multiple tasks to MEC servers as well as downloading multiple task results. The problem of jointly optimizing the MEC server allocation, the transmit power allocation of the users, the transmit power allocation of the MEC servers, the computation resource allocation of the MEC servers, the time allocation and the channel allocation, for minimizing the overall delay of all tasks is investigated. We then propose a heuristic algorithm to solve the problem, where the computation resource allocation of the MEC servers and the time allocation are optimized by the bisection search method, the transmit power allocations are obtained in closed-form, and the MEC server allocation and the channel allocation are optimized by the swap matching algorithm. Extensive simulations demonstrate that the proposed algorithm significantly outperforms various benchmark algorithms.

关键词： Mobile edge computing  

摘要： Recently, the hybrid relay-reflecting intelligent surface (HRRIS) has been introduced as a spectral- and energyefficient architecture to assist wireless communication systems. In the HRRIS, a single or few active relay elements are deployed along with a large number of passive reflecting elements, allowing it to not only reflect but also amplify the incident signals. In this work, we investigate the potential of the HRRIS in aiding the computation offloading in a single-user mobile edge computing system. The objective is to minimize the offloading latency while ensuring the secrecy of user data against a malicious eavesdropper. We develop efficient solutions to this latency minimization problem based on alternating optimization. Through numerical results, we show that the deployment of the HRRIS can result in a considerable reduction in latency. Furthermore, the latency reduction gain offered by the HRRIS is much more significant than that of the conventional reconfigurable intelligent surface (RIS). © 2021, CC0.

关键词： Mobile edge computing  

摘要： To mitigate the computational power gap between the network core and edges, mobile edge computing (MEC) is poised to play a fundamental role in future generations of wireless networks. In this correspondence, we consider a non-orthogonal multiple access (NOMA) transmission model to maximize the worst task to be offloaded among all users to the network edge server. A provably convergent and efficient algorithm is developed to solve the considered non-convex optimization problem for maximizing the minimum number of offloaded bits in a multi-user NOMA-MEC system. Compared to the approach of optimized orthogonal multiple access (OMA), for given MEC delay, power and energy limits, the NOMA-based system considerably outperforms its OMA-based counterpart in MEC settings. Numerical results demonstrate that the proposed algorithm for NOMA-based MEC is particularly useful for delay sensitive applications. © 2021, CC BY.

关键词： Mobile edge computing   Service placement   Request routing   Linear programming   Heuristic algorithm  

摘要： Mobile edge computing (MEC) is envisioned as a prospective technology that supports latency-critical and computation-intensive applications by using storage and computation resources in network edges. The advantages of this technology are trapped in limited edge cloud resources, and one of the prime challenges is how to allocate available edge cloud resources to satisfy user requests. However, previous works usually optimize service (data&code) placement and request routing simultaneously within the same timescale, ignoring the fact that frequent service replacement will incur expensive operating expenses. In this paper, we jointly optimize service placement and request routing in the MEC network for data analysis applications, under the constraints of computation and storage resource. In particular, the Cloud Radio Access Network (C-RAN) architecture is applied to pool available resources and realize load balancing among edge clouds. In addition, we adopt a two timescale framework to reduce high operating expenses caused by frequent cross-cloud service replication and replica deletion. Then, we develop a greedy-based approximation algorithm for service placement subproblem and a linear programming (LP) relaxation-based heuristic algorithm for request routing subproblem, respectively. Finally, the numerical results demonstrate that our proposed solution reaches 90% of the optimal performance in services homogeneous case and 76% in services heterogeneous case.

关键词： Asynchronous federated learning   Internet of Things(IoT)   Mobile edge computing  

摘要： The advancement of the Internet of Things(IoT)brings new opportunities for collecting real-time data and deploying machine learning ***,an individual IoT device may not have adequate computing resources to train and deploy an entire learning *** the same time,transmitting continuous real-time data to a central server with high computing resource incurs enormous communication costs and raises issues in data security and *** learning,a distributed machine learning framework,is a promising solution to train machine learning models with resource-limited devices and edge ***,the majority of existing works assume an impractically synchronous parameter update manner with homogeneous IoT nodes under stable communication *** this paper,we develop an asynchronous federated learning scheme to improve training efficiency for heterogeneous IoT devices under unstable communication ***,we formulate an asynchronous federated learning model and develop a lightweight node selection algorithm to carry out learning tasks *** proposed algorithm iteratively selects heterogeneous IoT nodes to participate in the global learning aggregation while considering their local computing resource and communication *** experimental results demonstrate that our proposed asynchronous federated learning scheme outperforms the state-of-the-art schemes in various settings on independent and identically distributed(i.i.d.)and *** distribution.

关键词： Internet of Vehicles   mobile edge computing   task offloading   Stackelberg game   reinforcement learning  

摘要： Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.

关键词： Satellites   Task analysis   Resource management   Satellite broadcasting   Edge computing   Processor scheduling   Voltage control   Deep reinforcement learning (DRL)   mobile edge computing   resource allocation   satellite communication   vehicle-to-vehicle (V2V)  

摘要： Satellite assisted vehicle-to-vehicle (V2V) communication can provide services for vehicles in depopulated areas, and it can be employed as an effective complementary component for terrestrial vehicular networks. Since the available communication and computing resource for satellites are scarce, task offloading, computing and communication resource allocation, which are coupled with each other, are critical issues for satellite assisted V2V communication. To tackle these problems, we formulate the joint offloading decision, computing and communication resource allocation problem for satellite assisted V2V communication as a mixed-integer nonlinear programming problem with minimum weighted-sum end-to-end latency, and we decouple it into two subproblems. First, the Lagrange multiplier method is adopted to obtain the optimal computing and communication resource allocation with fixed offloading decision. Then, the results of the resource allocation subproblem are fed into the offloading decision problem, which is formulated as a Markov decision process. To maximize the long-term reward of offloading decision, a deep reinforcement learning based method is adopted to learn the optimal offloading decision. Finally, the simulation results show that the proposed joint task offloading and resource allocation approach has superior performance compared with other schemes.

关键词： Task analysis   Batteries   Servers   Uplink   Delays   Neural networks   Energy harvesting   Computation offloading   deep deterministic policy gradient   deep reinforcement learning   mobile edge computing   resource allocation  

摘要： We study a joint partial offloading and resource allocations (JPORA) problem for the mobile edge computing (MEC) with energy harvesting (EH), where the number of MDs, computation task, and harvested energy of mobile device (MD) are highly dynamic. It is critical to acquire an algorithm that optimally adapts JPORA decisions. Recent studies employ deep deterministic policy gradient (DDPG) agent to tackle the JPORA problem. However, traditional DDPG cannot generalize well to different MEC network scale, due to the deep neural networks in DDPG can only extract latent representation from Euclidean data, with the characteristics of MEC network structural information ignored. To this end, by taking advantage of the graph-based relationship deduction ability form graph convolutional networks (GCN) and the self-evolution ability from the experience training of DDPG, we propose a centralized GCN-DDPG agent to learn making decisions for MDs, including offloading ratio, local computation capacity, and uplink transmission power. Experimental results show that the proposed GCN-DDPG provides significant performance improvement over a number of state-of-the-art DRL agents.

关键词： Mobile edge computing  

摘要： In recent years, deep learning models have become ubiquitous in industry and academia alike. Modern deep neural networks can solve one of the most complex problems today, but coming with the price of massive compute and storage requirements. This makes deploying such massive neural networks challenging in the mobile edge computing paradigm, where edge nodes are resource-constrained, hence limiting the input analysis power of such frameworks. Semantic and layer-wise splitting of neural networks for distributed processing show some hope in this direction. However, there are no intelligent algorithms that place such modular splits to edge nodes for optimal performance. This work proposes a novel placement policy, SplitPlace, for the placement of such neural network split fragments on mobile edge hosts for efficient and scalable computing. © 2022 is held by the owner/author(s).

关键词： Multi-access edge computing   Task offloading   Mobile edge computing   Survey  

摘要： With the advent of new technologies in both hardware and software, we are in the need of a new type of application that requires huge computation power and minimal delay. Applications such as face recognition, augmented reality, virtual reality, automated vehicles, industrial IoT, etc. belong to this category. Cloud computing technology is one of the candidates to satisfy the computation requirement of resource-intensive applications running in UEs (User Equipment) as it has ample computational capacity, but the latency requirement for these applications cannot be satisfied by the cloud due to the propagation delay between UEs and the cloud. To solve the latency issues for the delay-sensitive applications a new network paradigm has emerged recently known as Multi-Access Edge Computing (MEC) (also known as mobile edge computing) in which computation can be done at the network edge of UE devices. To execute the resource-intensive tasks of UEs in the MEC servers hosted in the network edge, a UE device has to offload some of the tasks to MEC servers. Few survey papers talk about task offloading in MEC, but most of them do not have in-depth analysis and classification exclusive to MEC task offloading. In this paper, we are providing a comprehensive survey on the task offloading scheme for MEC proposed by many researchers. We will also discuss issues, challenges, and future research direction in the area of task offloading to MEC servers.