关键词： Reliability   NOMA   Servers   Delays   Resource management   Edge computing   Error probability   finite blocklength   NOMA   mobile edge computing   reliability  

摘要： In this paper, we consider mission-critical task offloading in the non-orthogonal multiple access (NOMA)-assisted mobile edge computing (MEC) networks, where local information collected from multiple local devices are processed at the MEC node. The process of the MEC service contains a NOMA-assisted communication phase and a computation phase, which is required to be reliable and low-latency. For such network, we derive the overall reliability of the service. On the one hand, we characterize the communication behavior in the finite blocklength (FBL) regime, where the impact of imperfect successive interference cancellation in the NOMA scheme is considered. On the other hand, we exploit the extreme value theory (EVT) to study the delay violation error of the computation phase. Following the characterizations, we provide an optimal design framework minimizing the overall error probability via a joint time and power allocation. To address the formulated non-convex problem, a modified block coordinate descent method is proposed, with which the original problem is decomposed into two sub-problems that can be solved optimally after conducting a set of analytical results. We validate our analytical model via simulations and demonstrate the proposed design's improved performance in comparison to benchmarks.

关键词： 360-degree mobile virtual reality video streaming   computation offloading   end-to-end delay   field-of-view prediction   mobile edge computing   proactive caching  

摘要： Recently, 360-degree mobile virtual reality video (MVRV) has become increasingly popular because it can provide users with an immersive experience. However, MVRV is usually recorded in a high resolution and is sensitive to latency, which indicates that broadband, ultra-reliable, and low-latency communication is necessary to guarantee the users' quality of experience. In this paper, we propose a mobile edge computing (MEC)-based 360-degree MVRV streaming scheme with field-of-view (FoV) prediction, which jointly considers video coding, proactive caching, computation offloading, and data transmission. To meet the requirement of stringent end-to-end (E2E) latency, the user's viewpoint prediction is utilized to cache video data proactively, and computing tasks are partially offloaded to the MEC server. In addition, we propose an analytical model based on diffusion process to study the packet transmission process of 360-degree MVRV in multihop wired/wireless networks and analyze the performance of the MEC-enabled scheme. The simulation results verify the accuracy of the analysis and the effectiveness of the proposed MVRV streaming scheme in reducing the E2E delay. Furthermore, the analytical framework sheds some light on the impacts of system parameters, e.g., FoV prediction accuracy and transmission rate, on the balance between computation delay and communication delay.

关键词： mobile edge computing   edge server placement   cost   access delay  

摘要： As an urgent problem to be solved, the placement of edge server is the first step in the implementation of mobile edge computing. In practice, service providers always hope to provide services to as many users as they can under the limited funds. In this paper, we aim to study the cost-aware server placement issue for a real-world scenario with access delay guarantee. Considering the network workload, we set an affordable upper limit on the maximum number of base stations. The server placement is then modelled as a mix integer nonlinear programming problem, which is proved to be NP-hard. To solve this problem with reduced computation complexity, we propose a biogeography-based optimisation algorithm. Extensive simulations on the real-world dataset from Shanghai Telecom show that the proposed algorithm outperforms other benchmarks in terms of the total placement cost and average user access delay.

关键词： Cloud computing   Mobile handsets   Edge computing   Load management   Edge computing   Bandwidth   Servers   Internet of Things   Mobile cloud computing   mobile edge computing   fog computing   cloudlet computing   Internet of things   cloud federation  

摘要： Mobile cloud computing and edge computing-based solutions provide means to offload tasks for resource-limited mobile devices. Mobile cloud computing provides remote cloud solutions while edge computing provides closer proximity-based solutions. Remote cloud solutions suffer from network latency and limited bandwidth challenges due to distance and dependency on the Internet. However, these challenges are addressed by edge-based solutions since the edge node is available in the same network. The use of Internet of Things-based solutions considering future Information Communication Technology infrastructure is on the rise resulting in the massive growth of digital equipment increasing the load at edge devices. Hence, some load balancing mechanism is required at the edge level to avoid resource congestion. The load balancing at the edge must consider the user's preferences about edge resources such as personal computers or mobile devices. A user must declare which resources can be spared for other devices to avoid overprovisioning essential resources. We present Load Balancing for Resource Optimization (LBRO), a collaborative cloudlet platform to address load balancing challenges in edge computing considering users' preferences. A comparative analysis of the proposed approach with the conventional edge-based approach yields that the proposed approach provides significantly improved results in terms of CPU, memory, and disk utilization.

关键词： Edge computing   Internet of Things   5G mobile communication   Deep learning   Servers   Computational modeling   Reinforcement learning   Deep learning   deep reinforcement learning   Internet of Things   mobile edge computing   task offloading  

摘要： Computation offloading has proven to be an effective method for facilitating resource-intensive tasks on IoT mobile edge nodes with limited processing capabilities. Additionally, in the context of Mobile Edge Computing (MEC) systems, edge nodes can offload its computation-intensive tasks to a suitable edge server. Hence, they can reduce energy cost and speed up processing. Despite the numerous accomplished efforts in task offloading problems on the Internet of Things (IoT), this problem remains a research gap mainly because of its NP-hardness in addition to the unrealistic assumptions in many proposed solutions. In order to accurately extract information from raw sensor data from IoT devices deployed in complicated contexts, Deep Learning (DL) is a potential method. Therefore, in this paper, an approach based on Deep Reinforcement Learning (DRL) will be presented to optimize the offloading process for IoT in MEC environments. This approach can achieve the optimal offloading decision. A Markov Decision Problem (MDP) is used to formulate the offloading problem. Delay time and consumed energy are the main optimization targets in this work. The proposed approach has been verified using extensive simulations. Simulation results demonstrate that the proposed model can effectively improve the MEC system latency, energy consumption, and significantly outperforms the Deep Q Networks (DQNs) and Actor Critic (AC) approaches.

关键词： deep learning   artificial neural network   mobile edge computing   music recommendation model  

摘要： The purpose is to better develop China's music industry, facilitate users' online music works queries, and encourage the development of excellent music works. The music recommendation system is analysed, and a new music recommendation system is constructed based on the combination of the hybrid Deep Learning (DL) Artificial Neural Network (ANN) and Mobile Edge Computing (MEC) technologies. First, the principle of MEC is analysed. Then, the combination of Deep Learning Artificial Neural Network (DLANN) technology and MEC technology is discussed. Afterward, DLANN and MEC are fused to implement the music recommendation model, and the proposed model is evaluated through experiment. The results show that the combination of DLANN and MEC technologies can increase the DL efficiency of the computer, the storage capacity, and the overall efficiency of the servers. This proves the feasibility of the proposed music recommendation system. The user satisfaction of the proposed music recommendation system exceeds all mainstream music recommendation algorithms on the market. Thus, the research can provide a reference for the improvement of the music recommendation algorithm in the future and is of great significance. The proposed music recommendation system outperforms most of the current music recommendation systems on the market.

关键词： mobile edge computing   MEC   computation offloading   MOEA/D   invasive weed optimisation   IWO   offloading decision  

摘要： Mobile edge computing (MEC) is able to provide cloud computing capabilities at network edges by offloading computation tasks to MEC servers deployed in proximity of edge nodes. Therefore, how to make offloading decision for mobile users has become a critical issue. In this paper, we propose a multi-objective computation offloading algorithm combining multi-objective evolutionary algorithm based on decomposition (MOEA/D) with invasive weed optimisation (IWO) and differential evolution (DE). Considering that IWO is a numerical stochastic optimisation method imitating weeds, behaviour in nature and enjoys great robustness, we further improve its searching abilities. In order to reduce computing time, single-object problems can be clustered into several groups in which only one problem can be optimised by IWO and others are optimised by DE. Experimental results show the competitive performance of our proposed algorithm for computation offloading in MEC environments.

关键词： Task analysis   Servers   Energy consumption   Computational modeling   Delays   Optimization   Cloud computing   USV   mobile edge computing   offloading delay   energy consumption   DRL  

摘要： Unmanned Surface Vehicles (USVs) generate a large amount of data that needs to be processed in real time when they work, but they are usually limited by computational and battery resources, so they need to offload their tasks to the edge for processing. However, when numerous USVs offload their tasks to the edge nodes, some offloaded tasks may be thrown due to queuing timeouts. Existing task offloading methods generally consider the latency or the overall system energy consumption caused by the collaborative processing at the edge and end layers, and do not consider the wasted energy when the tasks are thrown. Therefore, to address the above situation, this paper establishes a task offloading model to minimize long-term task latency and energy consumption by jointly considering the requirments of latency and energy-sensitive tasks and the overall load dynamics in the cloud, edge, and end layers. A deep reinforcement learning (DRL)-based Task Offloading with Cloud Edge Jointly Load Balance Optimization algorithm (TOLBO) is proposed to select the best edge server or cloud server for offloading. Simulation results show that the algorithm can improve the utilization of energy consumption of the cloud edge nodes compared with other algorithms. At the same time, it can significantly reduce the task throw rate, average latency, and energy consumption of end devices.

关键词： Cloud computing   Servers   Vehicle dynamics   Resource management   Wireless fidelity   Taxonomy   Task analysis   Mobile edge computing   decentralised cloud   MEC server   service caching   service offloading   computational offloading   service deployment   resource management   service orchestration  

摘要： The advent of new cloud-based applications such as mixed reality, online gaming, autonomous driving, and healthcare has introduced infrastructure management challenges to the underlying service network. Multi-access edge computing (MEC) extends the cloud computing paradigm and leverages servers near end-users at the network edge to provide a cloud-like environment. The optimum placement of services on edge servers plays a crucial role in the performance of such service-based applications. Dynamic service placement problem addresses the adaptive configuration of application services at edge servers to facilitate end-users and those devices that need to offload computation tasks. While reported approaches in the literature shed light on this problem from a particular perspective, a panoramic study of this problem reveals the research gaps in the big picture. This paper introduces the dynamic service placement problem and outline its relations with other problems such as task scheduling, resource management, and caching at the edge. We also present a systematic literature review of existing dynamic service placement methods for MEC environments from networking, middleware, applications, and evaluation perspectives. In the first step, we review different MEC architectures and their enabling technologies from a networking point of view. We also introduce different cache deployment solutions in network architectures and discuss their design considerations. The second step investigates dynamic service placement methods from a middleware viewpoint. We review different service packaging technologies and discuss their trade-offs. We also survey the methods and identify eight research directions that researchers follow. Our study categorises the research objectives into six main classes, proposing a taxonomy of design objectives for the dynamic service placement problem. We also investigate the reported methods and devise a solutions taxonomy comprising six criteria. In the th

关键词： Servers   Costs   Resource management   Optimization   Heuristic algorithms   Quality of service   Dynamic scheduling   Cost efficiency   configuration scheme   edge server   mobile edge computing   placement scheme  

摘要： Computing resource configuration and site selection of edge servers (ESs) are two critical steps to build up a mobile edge computing (MEC) platform. In this paper, the joint optimization problem of configuration and placement for ES in the MEC environment is investigated. First, we treat each ES as an M/G/m queueing model, and establish mathematical models to characterize the MEC environment, such that the performance and operational expenditures (OPEX) of the system can be calculated analytically. Then, we design a two-stage method and develop a series of algorithms based on bisection algorithm and genetic algorithm (GA) to obtain the optimal configuration scheme and sub-optimal placement scheme (including the deployment quantity) of ESs, with the goal of minimizing OPEX while maintaining system performance at a predetermined level. Finally, we conduct experiments based on a real base station dataset provided by Shanghai Telecom to show the effectiveness of the proposed algorithms. To the best of our knowledge, this work is the first research of the joint optimization problem of configuration and placement for ES in the MEC environment, where the main objective is to increase the cost efficiency.