关键词： Mobile edge computing   Computation offloading   Grey wolf optimizer   Whale optimization algorithm  

摘要： As the technology of the Internet of Things (IoT) and mobile edge computing (MEC) develops, more and more tasks are offloaded to the edge servers to be computed. The offloading strategy performs an essential role in the progress of computation offloading. In a general scenario, the offloading strategy should consider enough factors, and the strategy should be made as quickly as possible. While most of the existing model only considers one or two factors, we investigated a model considering three targets and improved it by normalizing each target in the model to eliminate the influence of dimensions. Then, grey wolf optimizer (GWO) is introduced to solve the improved model. To obtain better performance, we proposed an algorithm hybrid whale optimization algorithm (WOA) with GWO named GWO-WOA. And the improved algorithm is tested on our model. Finally, the results obtained by GWO-WOA, GWO, WOA, particle swarm optimization (PSO), and genetic algorithm (GA) are discussed. The results have shown the advantages of GWO-WOA.

关键词： Mobile edge computing  

摘要： Mobile video communication and Internet of Things are playing a more and more important role in our daily life. Mobile Edge Computing (MEC), as the essential network architecture for the Internet, can significantly improve the quality of video streaming applications. The mobile devices transferring video flow are often exposed to hostile environment, where they would be damaged by different attackers. Accordingly, Mobile Edge Computing Network is often vulnerable under disruptions, against either natural disasters or human intentional attacks. Therefore, research on secure hub location in MEC, which could obviously enhance the robustness of the network, is highly invaluable. At present, most of the attacks encountered by edge nodes in MEC in the IoT are random attacks or random failures. According to network science, scale-free networks are more robust than the other types of network under the random failures. In this paper, an optimization algorithm is proposed to reorganize the structure of the network according to the amount of information transmitted between edge nodes. BA networks are more robust under random attacks, while WS networks behave better under human intentional attacks. Therefore, we change the structure of the network accordingly, when the attack type is different. Besides, in the MEC networks for mobile video communication, the capacity of each device and the size of the video data influence the structure significantly. The algorithm sufficiently takes the capability of edge nodes and the amount of the information between them into consideration. In robustness test, we set the number of network nodes to be 200 and 500 and increase the attack scale from 0% to 100% to observe the behaviours of the size of the giant component and the robustness calculated for each attack method. Evaluation results show that the proposed algorithm can significantly improve the robustness of the MEC networks and has good potential to be applied in real-world MEC system

关键词： Mobile edge computing  

摘要： With the popularity of the Internet and the rapid development of e-commerce, online shopping has gradually become an indispensable part of people's lives. Among them, the rise of cross-border e-commerce has become a focus of attention. The operation traces left by visitors during shopping on the e-commerce platform are stored in the database of the system, and the platform holds such a large amount of valuable data resources. How to unearth valuable content from these resources and apply them becomes very important. This article mainly introduces the research on the visitor information analysis system of the cross-border e-commerce platform based on mobile edge computing. This article first establishes the mobile edge computing framework based on the advantages of the mobile edge computing method and uses it to visit visitors in the visitor information analysis system. In the data filtering, secondly, the requirements of the visitor information analysis system of the cross-border e-commerce platform are analyzed to provide a design basis for the design of the visitor information system. Finally, the visitor information analysis based on the mobile edge algorithm is designed through the demand analysis of the system that has also been tested for visitor information analysis. The test pass rate is as high as 98%, and the accuracy rate of visitor information analysis reaches 80%.

关键词： Online learning   Internet of Things   Task offloading   Energy efficiency   Mobile edge computing  

摘要： User equipment produces a series of tasks that are processed locally or remotely, falling into three categories: (i) local computing only, (ii) a fraction of the task is computed locally and the remaining task unprocessed is offloaded for remote computation, and (iii) the entire task is offloaded. Each case has attracted substantial attention in recent studies, where a delay-constrained non-linear optimization problem is often formulated. The solutions employed are either based on Lagrange duality, heuristic search, or dynamic programming. To our knowledge, there is no unifying task-processing orchestrator that is an online tailored solver for learning the model-free problems, encapsulating the three cases above. We fill this gap and present the first attempt on an innovative actor-critic reinforcement learning approach in consideration of the energy-efficiency, to compute the asymptotically optimal solutions via decomposing the comprehensive optimization into sub-problems. Rigorous theoretical analyses and experience-driven simulations demonstrate significant advantages over the benchmark approaches, in terms of task-processing delay, power efficiency, and convergence time.

关键词： Mobile edge computing  

摘要： Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server's capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into k subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all k number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with

关键词： Internet of things (IoT)   Reinforcement learning   Markov decision process   Computation offloading   Deep learning   Mobile edge computing  

摘要： Recently, in order to distribute computing, networking resources, services, near terminals, mobile fog is gradually becoming the mobile edge computing (MEC) paradigm. In a mobile fog environment, the quality of service affected by offloading speeds and the fog processing, however the traditional fog method to solve the problem of computation resources allocation is difficult because of the complex network states distribution environment (that is, F-AP states, AP states, mobile device states and code block states). In this paper, to improve the fog resource provisioning performance of mobile devices, the learning-based mobile fog scheme with deep deterministic policy gradient (DDPG) algorithm is proposed. An offloading block pulsating discrete event system is modeled as a Markov Decision Processes (MDPs), which can realize the offloading computing without knowing the transition probabilities among different network states. Furthermore, the DDPG algorithm is used to solve the issue of state spaces explosion and learn an optimal offloading policy on distributed mobile fog computing. The simulation results show that our proposed scheme achieves 20%, 37%, 46% improvement on related performance compared with the policy gradient (PG), deterministic policy gradient (DPG) and actor- critic (AC) methods. Besides, compared with the traditional fog provisioning scheme, our scheme shows better cost performance of fog resource provisioning under different locations number and different task arrival rates.

关键词： C-RAN   Mobile cloud computing   Mobile Edge Computing   Fine-grain offloading  

摘要： Cloud Radio Access Network (C-RAN) and Mobile Edge Computing (MEC) have recently emerged as promising leading technologies for next generation mobile networks. Due to its low access latency, MEC is not only a convenient candidate for deployment of C-RAN, but it can also be served by Mobile Users (MUs) to offload their computation-intensive applications. This convergence can facilitate the utilization of knowledge acquired through inter-BBU information sharing to improve the quality of offloading decision. In this paper, we propose an end-to-end communication and computation offloading architecture which takes the full advantage of C-RAN to solve the MEC offloading problem with regard to both partitioning as well as sending and return RRH assignment problems. Based on the proposed architecture, we model the end-to-end offloading problem as an ILP with the objective of minimizing the cost of offloading considering the intra and inter cluster handover costs besides other factors. Due to the complexity of the end-to-end offloading problem, we propose a combination of utility functions and modified min-cut algorithms to solve the aforementioned problems in a timely manner. Simulation results demonstrate that the proposed approach outperforms significantly other alternatives in terms of execution time, energy consumption and aggregated cost under scenarios with different amounts of normalized throughput, invocation data and workload.

关键词： Task analysis   Reinforcement learning   Quality of service   Energy consumption   Optimization   Markov processes   Computational modeling   Energy   mobile edge computing   mobility   multi-agent reinforcement learning   task migration  

摘要： Closer to mobile users geographically, mobile edge computing (MEC) can provide some cloud-like capabilities to users more efficiently. This enables it possible for resource-limited mobile users to offload their computation-intensive and latency-sensitive tasks to MEC nodes. For its great benefits, MEC has drawn wide attention and extensive works have been done. However, few of them address task migration problem caused by distributed user mobility, which can't be ignored with quality of service (QoS) consideration. In this article, we study task migration problem and try to minimize the average completion time of tasks under migration energy budget. There are multiple independent users and the movement of each mobile user is memoryless with a sequential decision-making process, thus reinforcement learning algorithm based on Markov chain model is applied with low computation complexity. To further facilitate cooperation among users, we devise a distributed task migration algorithm based on counterfactual multi-agent (COMA) reinforcement learning approach to solve this problem. Extensive experiments are carried out to assess the performance of this distributed task migration algorithm. Compared with no migrating (NM) and single-agent actor-critic (AC) algorithms, the proposed distributed task migration algorithm can achieve up 30-50 percent reduction about average completion time.

关键词： Protocols   Data models   Training   Cloud computing   Performance evaluation   Distributed databases   Computer architecture   Federated learning   mobile edge computing   distributed computing   machine learning  

摘要： Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes, and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile, Federated learning (FL) has emerged as a promising privacy-preserving approach to facilitating AI applications. However, it remains a big challenge to optimize the efficiency and effectiveness of FL when it is integrated with the MEC architecture. Moreover, the unreliable nature (e.g., stragglers and intermittent drop-out) of end devices significantly slows down the FL process and affects the global model's quality in such circumstances. In this article, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture. HybridFL adopts two levels (the edge level and the cloud level) of model aggregation enacting different aggregation strategies. Moreover, in order to mitigate stragglers and end device drop-out, we introduce regional slack factors into the stage of client selection performed at the edge nodes using a probabilistic approach without identifying or probing the state of end devices (whose reliability is agnostic). We demonstrate the effectiveness of our method in modulating the proportion of clients selected and present the convergence analysis for our protocol. We have conducted extensive experiments with machine learning tasks in different scales of MEC system. The results show that HybridFL improves the FL training process significantly in terms of shortening the federated round length, speeding up the global model's convergence (by up to 12x) and reducing end device energy consumption (by up to 58 percent).

关键词： Task analysis   Servers   Pricing   Bayes methods   Wireless communication   Games   Edge computing   Mobile edge computing   service caching and pricing   computation offloading   dynamic game under incomplete information  

摘要： Provided with mobile edge computing (MEC) services, wireless devices (WDs) no longer have to experience long latency in running their desired programs locally, but can pay to offload computation tasks to the edge server. Given its limited storage space, it is important for the edge server at the base station (BS) to determine which service programs to cache by meeting and guiding WDs' offloading decisions. In this article, we propose an MEC service pricing scheme to coordinate with the service caching decisions and control WDs' task offloading behavior in a cellular network. We propose a two-stage dynamic game of incomplete information to model and analyze the two-stage interaction between the BS and multiple associated WDs. Specifically, in Stage I, the BS determines the MEC service caching and announces the service program prices to the WDs, with the objective to maximize its expected profit under both storage and computation resource constraints. In Stage II, given the prices of different service programs, each WD selfishly decides its offloading decision to minimize individual service delay and cost, without knowing the other WDs' desired program types or local execution delays. Despite the lack of WD's information and the coupling of all the WDs' offloading decisions, we derive the optimal threshold-based offloading policy that can be easily adopted by the WDs in Stage II at the Bayesian equilibrium. In particular, a WD is more likely to offload when there are fewer WDs competing for the edge server's computation resource, or when it perceives a good channel condition or low MEC service price. Then, by predicting the WDs' offloading equilibrium, we jointly optimize the BS' pricing and service caching in Stage I via a low-complexity algorithm. In particular, we first study the differentiated pricing scheme and prove that the same price should be charged to the cached programs of the same workload. Motivated by this analysis, we further propose a low-complexity u