关键词： Mobile edge computing   context-awareness   adaptive QoS prediction   case-based reasoning   support vector machine   artificial bee colony algorithm  

摘要： Mobile edge computing (MEC) allows the use of its services with low latency, location awareness and mobility support to make up for the disadvantages of cloud computing, and has gained a considerable momentum recently. However, the dynamically changing quality of service (QoS) may result in failures of QoS-aware recommendation and composition of MEC services, which significantly degrades users' satisfaction and negates the advantages of MEC. To address this issue, by considering user-related and service-related contextual factors and various MEC services scheduling scenarios, we propose two context-aware QoS prediction schemes for MEC services. The first scheme is designed for the situations when MEC services are scheduled in real-time, which contains two context-aware real-time QoS estimation methods. One method can estimate the real-time multi-QoS of MEC services and the other method can estimate the real-time fitted QoS of MEC services. The second scheme is designed for the situations when MEC services are scheduled in the future. This scheme includes two context-aware QoS prediction methods. One method can predict the multi-QoS of MEC services and the other method can predict the fitted QoS of MEC services. Finally, adaptive QoS prediction strategies are developed in the light of characteristics of the proposed QoS prediction methods. According to these strategies, the most appropriate QoS prediction method can be scheduled. Extensive experiments are conducted to validate our proposed approaches and to demonstrate their performance.

关键词： edge computing   cloud computing   industrial internet of things   mobile edge computing   hybrid edge-cloud  

摘要： Edge computing is a distributed computing paradigm such that client data are processed at the periphery of the network, as close as possible to the originating source. Since the 21st century has come to be known as the century of data due to the rapid increase in the quantity of exchanged data worldwide (especially in smart city applications such as autonomous vehicles), collecting and processing such data from sensors and Internet of Things devices operating in real time from remote locations and inhospitable operating environments almost anywhere in the world is a relevant emerging need. Indeed, edge computing is reshaping information technology and business computing. In this respect, the paper is aimed at providing a comprehensive overview of what edge computing is as well as the most relevant edge use cases, tradeoffs, and implementation considerations. In particular, this review article is focused on highlighting (i) the most recent trends relative to edge computing emerging in the research field and (ii) the main businesses that are taking operations at the edge as well as the most used edge computing platforms (both proprietary and open source). First, the paper summarizes the concept of edge computing and compares it with cloud computing. After that, we discuss the challenges of optimal server placement, data security in edge networks, hybrid edge-cloud computing, simulation platforms for edge computing, and state-of-the-art improved edge networks. Finally, we explain the edge computing applications to 5G/6G networks and industrial internet of things. Several studies review a set of attractive edge features, system architectures, and edge application platforms that impact different industry sectors. The experimental results achieved in the cited works are reported in order to prove how edge computing improves the efficiency of Internet of Things networks. On the other hand, the work highlights possible vulnerabilities and open issues emerging in the context

关键词： Task analysis   Wireless communication   Energy consumption   Trajectory   Optimization   Resource management   Dynamic scheduling   Unmanned aerial vehicle   mobile edge computing   computation offloading   task association   deep reinforcement learning  

摘要： Mobile edge computing (MEC) has been considered as a promising paradigm to support the growing popularity of mobile devices (MDs) with similar capabilities as cloud computing. Most existing research focuses on MEC enabled by terrestrial base stations (BSs), which is unable to work in certain scenarios, e.g., disaster rescue and field operation. Some researchers have been making efforts on studying MEC assisted by unmanned-aerial-vehicles (UAVs) and developed lots of efficient scheduling algorithms. However, MEC assisted only by UAVs has limited capability and is unsuitable for heavy-computation applications. To address the issue, this paper proposes a novel UAV-and-BS hybrid enabled MEC system, where multiple UAVs and one BS are deployed to facilitate the provisioning of MEC services either directly from UAVs or indirectly from the BS. Considering maximizing the lifetime of all MDs, the energy-efficient scheduling problem is formulated as minimizing the energy consumption of all MDs by jointly optimizing UAV trajectories, task associations, computing-and-transmitting resource allocations. The optimization problem is further decomposed into three sub-problems and solved by the proposed hybrid heuristic and learning based scheduling algorithms to reduce the complexity. Experimental results show that the proposed algorithm can achieve promising performance improvements over baseline algorithms, including local-computing, random-offloading and greedy-offloading.

关键词： Mobile edge computing   Unmanned aerial vehicle deployment   Computation offloading   Particle swarm optimization   Genetic algorithm   Greedy algorithm  

摘要： The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) technology breaks through the limitations of traditional terrestrial communications. The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices (MDs). To further enhance the Quality-of-Service (QoS) of MEC systems, a multi-UAV-enabled MEC system model is designed. In the proposed model, UAVs are regarded as edge servers to offer computing services for MDs, aiming to minimize the average task response time by jointly optimizing UAV deployment and computation offloading. Based on the problem definition, a two-layer joint optimization method (PSO-GA-G) is proposed. First, the outer layer utilizes a Particle Swarm Optimization algorithm combined with Genetic Algorithm operators (PSO-GA) to optimize UAV deployment. Next, the inner layer adopts a greedy algorithm to optimize computation offloading. The extensive simulation experiments verify the feasibility and effectiveness of the proposed PSO-GA-G. The results show that the PSO-GA-G can achieve a lower average task response time than the other three baselines.

关键词： reinforcement learning   offloading decision   Industrial Internet of Things   cooperative computing   mobile edge computing  

摘要： In the Industrial Internet of Things (IIoT), various tasks are created dynamically because of the small quantity batch production. Hence, it is difficult to execute tasks only with devices that have limited battery lives and computation capabilities. To solve this problem, we adopted the mobile edge computing (MEC) paradigm. However, if there are numerous tasks to be processed on the MEC server (MECS), it may not be suitable to deal with all tasks in the server within a delay constraint owing to the limited computational capability and high network overhead. Therefore, among cooperative computing techniques, we focus on task offloading to nearby devices using device-to-device (D2D) communication. Consequently, we propose a method that determines the optimal offloading strategy in an MEC environment with D2D communication. We aim to minimize the energy consumption of the devices and task execution delay under certain delay constraints. To solve this problem, we adopt a Q-learning algorithm that is part of reinforcement learning (RL). However, if one learning agent determines whether to offload tasks from all devices, the computing complexity of that agent increases tremendously. Thus, we cluster the nearby devices that comprise the job shop, where each cluster's head determines the optimal offloading strategy for the tasks that occur within its cluster. Simulation results show that the proposed algorithm outperforms the compared methods in terms of device energy consumption, task completion rate, task blocking rate, and throughput.

关键词： Relay-remote Selection   Sequential discovery   Computation offloading   Mobile edge computing   Dynamic programming  

摘要： In this paper, we investigate a MEC relay-assisted system with multiple relay nodes (RNs) and multiple remote servers (RSs), where both the selections of best RN and RS are considered. In order to explore the diversity of both RNs and RSs, we propose a sequential relay-remote selection and offloading strategy by taking the local computing, relay computing and remote computing into consideration. The sequential relay-remote selection strategy specifies when to stop server discovery and carry out computation offloading. The offloading strategy tells what are the optimal portions of task executed locally, at RN and at RS. Under such framework, we seek to minimize the total energy consumption coming from server detection, data transmission and data execution. We formulate this problem as a stochastic sequential decision-making problem and dynamic programming is applied to obtain the optimal strategy. The performance of our proposed strategy is evaluated using simulation results. It is found that the energy consumption can be reduced by jointly considering the design of sequential relay-remote selection and offloading ratio optimization.

关键词： Task analysis   Wireless communication   Energy consumption   Computational modeling   Interference   Probability   Power system reliability   Secure communication   mobile edge computing   user selection   latency   energy consumption   secrecy outage probability  

摘要： In this paper, we investigate a secure mobile edge computing (MEC) network in the presence of multiple eavesdroppers, where multiple users can offload parts of their tasks to the computational access point (CAP). The multiple eavesdroppers may overhear the confidential task offloading, which leads to information leakage. In order to address this issue, we present the minimization problem of the secrecy outage probability (SOP), by jointly taking into account the constraints from the latency and energy consumption. With the aim to improve the system secrecy performance, we then introduce three user selection criteria to choose the best user among multiple ones. Specifically, criterion I maximizes the locally computational capacity, while criterion II and III maximize the secrecy capacity and data rate of main links, respectively. For these criteria, we further analyze the system secrecy performance by deriving analytical and asymptotic expressions for the SOP, from which we can conclude important insights for the system design. Finally, simulation and analytical results are provided to verify the proposed analysis. The results show that the three criteria can efficiently safeguard the MEC networks, compared to the traditional local computing and fully offloading, especially with a large value of user number.

关键词： Mobile edge computing   Fog computing   machine learning   Game theory   Resource provisioning   Elasticity  

摘要： Close computing paradigms such as fog and edge have become promising technologies for mobile applications running on pervasive mobile equipment utilized by a wide range of users to remove such types of equipment' inherent limitations. In such environments, competition is a severe challenge to gain computation and communication resources' capabilities. Therefore, resource allocation in the mentioned environments are becoming a requirement, which is an essential challenging issue addressed by different approaches, including resource provisioning. However, to the best of the authors' knowledge, any systematic, comprehensive, and detailed survey related to resource provisioning has not been applied in computation environments despite its importance. This paper provides a review of the resource provisioning approaches in computation paradigms in the form of a standard classification to identify the existing approaches on this critical topic and offer open issues. The proposed classification can be organized into five main fields: framework-based, heuristic/meta-heuristicbased, model-based, machine learning-based, and game theoretic-based mechanisms. Next, these classes are compared based on some essential features such as performance metrics, case studies, utilized techniques, and evaluation tools. Finally, open issues and uncovered or insufficiently covered future research challenges, including resource performance, resource location, uncertainties, resource elasticity, and resource migration are discussed, and the survey is concluded.

关键词： Bit rate   Streaming media   Transformers   Transcoding   Quality of experience   Wireless communication   Energy consumption   Adaptive video streaming   mobile edge computing   intelligent resource allocation   transformer   deep reinforcement learning  

摘要： The emerging mobile edge computing (MEC) technology has been recently applied to improve the Quality of Experience (QoE) of network services, such as live video streaming. In this paper, we study an energy-aware adaptive live streaming scheme in wireless edge networks. In particular, we aim to design a joint uplink transmission and edge transcoding algorithm maximizing the video followers' QoE, while minimizing the energy consumption of the video streamer. We formulate the problem as a Markov decision process (MDP), and propose a deep reinforcement learning (DRL) based framework, named SACCT, to determine the streamer's encoding bitrate, the uploading power as well as the edge transcoding bitrates and frequency. We decompose the MDP problem into inter-frame and intra-frame problems to address the key design challenges that arise from continuous-discrete hybrid action space, time-varying state and action spaces, and unknown network variation. By doing so, SACCT integrates model-based optimization and model-free DRL to determine the intra-frame continuous resource allocation decisions and the inter-frame discrete bitrate adaptation decisions, respectively. To integrate both the numerical features (e.g., channel gain) and the categorical features (e.g., bitrate), we propose a communication Transformer (CT) as a backbone of SACCT by representing network states as communication tokens and running Transformers to model multi-scale dependencies. Extensive simulations manifest that compared with state-of-the-art approaches, SACCT can provide 128.23% (on average) extra reward. As such, by leveraging joint uplink adaption and edge transcoding, the proposed scheme enables an intelligent wireless network edge with QoE-assured and energy-aware live streaming services.

关键词： Mobile edge computing   Cloudlet deployment   Computation offloading   Wireless metropolitan area network  

摘要： With the rapid development of mobile technology, the demands of mobile applications for computational resources are increasing. Limited by some factors, the computational capacities of mobile devices (MDs) cannot meet mobile application requirements. Mobile edge computing(MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By deploying edge servers at the network edge, the computation resources of cloud center are sinking, and the enrich computational resources of edge servers make up for the lack of MDs. As a specific form of edge server, cloudlet has been widely concerned by academia and industry in recent years. However, the existing works mainly focus on the computation offloading of simple tasks under the condition of fixed cloudlet positions and ignore the impact of cloudlet deployment scheme and data dependence among components of workflow applications (WAs) on the result of computation offloading. In this paper, the cloudlet deployment for WAs in a MEC-wireless metropolitan area network (WMAN) is formulated. We propose a encode library-enabled particle swarm optimization using genetic algorithm operators (EL-PSOGA) algorithm to optimize the execution end time of all WAs. Numerical results show that our algorithm can effectively reduce the execution end time of all WAs in the system compared with several benchmark algorithms.