关键词： Learning-based network optimization   Network slicing   Content Delivery Network   Mobile Edge Computing   Uncertain programming  

摘要： Network Slicing (NS) and Mobile Edge Computing (MEC) are recognized as a promising paradigm for mobile Content Delivery Network (CDN) by instantiating Virtual Network Functions (VNFs) on MEC platforms. Mobile network exhibits intrinsic dynamics, where user requests patterns vary in time and space due to human behaviors. The requirements and distribution of users are arbitrary spatio-temporal stochastic variables, which makes the deployment and reconfiguration of a CDN slice a challenging task. In this work, by adopting a periodical reconfiguration strategy, we consider a CDN slice system which operates in discrete time intervals. Within each time interval, we formalize the CDN-MEC-VNFs Planning (CMVP) problem based on uncertain programming to tackle the dynamic and uncertain mobile network environment, where user requests are treated as random variables with arbitrary pattern. Then, we propose a data-driven and learning-based algorithm which integrates Stochastic Simulation (SS), Deep Neural Networks (DNN) and meta-heuristic algorithm to determine the provisioning of the CDN slice from historical user requests information. The practicability of the proposed mechanism in offline training and online running are also discussed. Finally, we conducted intensive real-trace driven simulations to demonstrate the effectiveness of our approach on planning CDN slice with higher QoS under dynamic system environment and arbitrary user requests by comparing towards several baselines.

关键词： Clustering   mobile edge computing   multi-objective optimization   multi-workflow offloading   SPEA2  

摘要： To cope with the rapid development of the Internet of Things (IoT) and the increasing demand for real-time services, mobile edge computing (MEC) has become a promising solution which extends centralised cloud computing, to provision computing resources, storage and network services closer to the mobile device from the network edge. While computation offloading is a key feature in MEC to enable real-time services, offloading workflow tasks in MEC is an NP-hard problem. Typically, the problem of multi-workflow offloading with multi-objective optimization is still an open and challenging issue. Therefore, this article proposes a multi-objective clustering evolutionary algorithm called MCEA to minimize the cost and energy consumption of multi-workflow execution under the deadline constraint. First, the sub-deadline constraint is added during initialization to generate more initial solutions that satisfy the deadline constraint. Then an adaptive clustering method is adopted to guide individuals to find a suitable mate during crossover operation. Finally, the probabilities of crossover and mutation are dynamically adjusted based on the historical information to control the evolution direction and convergence speed of algorithm. Comprehensive experiments are carried out for complex workflow applications on FogWorkflowSim, which demonstrate that MCEA can achieve better performance than four representative algorithms in three evaluation metrics.

关键词： Automatic speech analysis   deep learning   edge-cloud computing   mobile edge computing   task offloading  

摘要： With the explosive growth of mobile multimedia services and artificial intelligence applications involving automatic speech analysis (ASA), mobile devices are increasingly unable to handle these computation-intensive tasks generated by users due to the limited computing resource. Besides, the existing cloud computing paradigm is not capable of processing such real-time and delaysensitive ASA tasks. In this paper, by leveraging mobile edge computing and deep learning (DL), we investigate task offloading for DLempowered ASA in mobile edge-cloud computing networks to minimize the total time for processing ASA tasks, thereby providing an agile service response. Specifically, to accelerate the processing of ASA tasks, we decompose a convolutional neural network based encoder-decoder model and deploy the encoder at edge servers to extract the features of ASA tasks. Moreover, edge servers derive the user tolerance limit by using a linear regression model for further enhancing the quality of experience of users. Based on some certain network constraints (i.e., user association and edge servers' storage/computing capacity), we propose a low-complexity and distributed offloading framework to solve the formulated complex problem. Evaluation results demonstrate the effectiveness of the proposed framework on reducing the total time and improving the satisfaction rate of users.

关键词： Task analysis   Servers   Energy consumption   Vehicle dynamics   Internet of Things   Benchmark testing   Wireless communication   Mobile edge computing   proactive cache placement   computation offloading   branch-and-bound   optimization  

摘要： This letter investigates a cache-enabled multiuser mobile edge computing (MEC) system with dynamic task arrivals, taking into account the impact of proactive cache placement on the system's overall energy consumption. We consider that an access point (AP) schedules a wireless device (WD) to offload computational tasks while executing the tasks of a finite library in the task caching phase, such that the nearby WDs with the same task request arriving later can directly download the task results in the task arrival and execution phase. We aim for minimizing the system's weighted-sum energy over a finite-time horizon, by jointly optimizing the task caching decision and the MEC execution of the AP, and local computing as well as task offloading of the WDs at each time slot, subject to caching capacity, task causality, and completion deadline constraints. The formulated design problem is a mixed-integer nonlinear program. Under the assumption of fully predicable task arrivals, we first propose a branch-and-bound (BnB) based method to obtain the optimal offline solution. Next, we propose two low-complexity schemes based on convex relaxation and task-popularity, respectively. Finally, numerical results show the benefit of the proposed schemes over existing benchmark schemes.

关键词： Wavelet analysis   Multiscale model   Medical image   Image denoising   Mobile edge computing   Autonomous unmanned analysis  

摘要： Image is the basis of human vision and the most commonly used information carrier in social activities. In the field of medicine, medical imaging obtains the image of a certain part of tissue in the body. There are many ways of medical imaging and acquisition systems, such as X-ray, magnetic resonance imaging, computed tomography, endoscopy, ultrasound and positron emission tomography and microscopic imaging. Medical image provides a more intuitive visual means, which provides a reliable basis for disease diagnosis, clinical treatment and teaching research. In real life, many different kinds of noise will affect the image collection process, these factors will make the quality of the image to a large extent. In the process of image denoising, white noise is a very common kind of noise. Even a high-resolution image will have some noise, but some simple denoising algorithms can be used for these noises. However, in this era of high resolution, the real-time performance of denoising is not good, and the real-time denoising is still the subject of many studies. In order to enhance the visual sense of the image by filtering out the noise in the image, we need to filter the two-dimensional image, and then make the operation of the image easier. In this paper, we study the real-time medical image denoising and information hiding model based on deep wavelet multiscale autonomous unmanned analysis. The experimental results show that this method can effectively deal with the noise points in the medical image, and the result graph is better.

关键词： Mobile IoT   Mobile edge computing   Perceptual task offloading   Population optimizing of quantum   behavior particle   Chaos perturbation strategy  

摘要： To solve the problem of high time consumption and energy loss of edge computing perceptual task offloading in the mobile IoT (Internet of things), it is necessary to increase the particle population diversity and the performance of escaping the local optimum solution in the intelligent optimization algorithm. For the offloading scenario of multi edge center servers and smart devices of multi IoT users, the NQPSOOM method (A new method of user perceptual task offloading in the IoT based on optimizing strategy of quantum behavior particle population) was proposed. The adaptability variance of the quantum behavior particle population was designed to measure the convergence of the particle population. The logistic chaos perturbation strategy was used to improve the performance of the intelligent optimization algorithm of escaping the local optimum solution. The exchange and mutation operations of fast elite non-dominated sorting genetic algorithm based on crowding coefficient were designed to select the particle population with a high diversity of quantum behavior particle population. Compared with the other five perceptual task computing offloading methods in recent three years, considering the convergence of the perceptual offloading model, the impact of the number of smart devices of IoT users, the number of edge center servers and the perceptual task data size on the optimal offloading method, the NQPSOOM method improves the performance of the perceptual task offloading model, including reducing the adaptability of the intelligent optimization algorithm by 4.89% on average, that is, facilitating the convergence speed of the intelligent optimization algorithm by 4.89%, and lowering the time consumption of the algorithm by 4.08% and energy loss by 3.91% on average.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   relay   intelligent reflecting surface   non-orthogonal multiple access   orthogonal frequency division multiplexing  

摘要： In this paper, the design of task offloading and computing strategy in a mobile edge computing (MEC) system deployed over a reconfigurable intelligent surface (RIS)-aided orthogonal frequency division multiplexing based non-orthogonal multiple access (OFDM-NOMA) relay network is studied, where both a relay node and an MEC server located at an access point help a user terminal accomplish task computing. Our objective is to minimize energy consumption for communication and computation at the user terminal and the relay subject to a given task executing delay constraint. To this end, a novel protocol is first proposed to enable the user terminal and the relay to fully utilize the high passive beamforming gain provided by RIS and the high spectral efficiency offered by OFDM and NOMA techniques for task offloading. Then, under the newly proposed protocol, a resource allocation optimization problem is formulated, where RIS reflection coefficients, task offloading and computing time allocation, subcarrier and power allocation, task partition, and CPU frequency of the MEC server are jointly optimized. Because the numerous optimization variables are coupled with each other and integer programming is involved for subcarrier allocation optimization, the formulated problem is a mixed integer programming and highly non-convex problem that is hard to tackle. To address this problem, an efficient algorithm is devised to achieve its solution by employing alternating optimization, layer optimization, big-M formulation and successive convex approximation techniques. Simulation results validate that substantial energy savings can be achieved by employing RIS and OFDM-NOMA techniques in wireless relaying-based MEC systems.

关键词： Mobile edge computing   Stackelberg game   Computing offload   Pricing  

摘要： Mobile edge server (MES) can meet the low latency requirements of End user (EU) tasks, but the MES computing resources are limited. This paper proposes an IDs-assisted MES (MES-IDs) computing model to expand MES. However, how to find a task offload strategy to minimize EU calculation costs and improve MES and ID benefits on a task completion basis. This paper proposes a three-way optimization calculation method based on the Stackelberg game to reduce the problem complexity, which decomposes into EUs-BS and BS-IDs problems. The EUs-BS problem design pricing scheme obtains the EU task optimal offload rate. The BS-IDs problem uses nonlinear pricing methods to find the optimal task offload problem between MES and ID on the resource unit price, a multi-constrained nonlinear convex optimization problem. We propose an immune Metropolis-based particle swarm loss optimization algorithm (PIMA) to obtain an approximate optimal solution for the nonlinear convex optimization problem by adjusting the resource price. Numerical simulation results show that compared with GSP and PSI algorithms, the PIMA algorithm shows more robust stability and can effectively reduce task completion delay and computational cost.

关键词： Mobile edge computing   incentive mechanism   truthful   auction mechanism  

摘要： Mobile edge computing (MEC) shows prominent application prospects in the Industrial Internet of Things (IIoT) by allowing resource-restricted IIoTmobile devices (MDs) to offload their tasks to geographical proximity edge clouds. An efficient incentive mechanism should be designed jointly addressing resource allocation and pricing to incentivizeMDs (i.e., buyers) and edge clouds (i.e., sellers) to participate in offloading service trading. This article aims to solve the socialwelfaremaximization problemof a personalizedMEC computation offloading service marketwhere each edge cloud can allocate different computing and wireless resources to eachMD according to theMDs' delay and energy consumption constraints, and eachMD submits bids to edge clouds differently based on the resource allocation of the edge clouds. We propose a truthful combinatorial auction (TCA) mechanismwhich involves three phases of resource allocation, buyer-seller matching, and payment determination. It should be highlighted that our proposed buyer-seller matching algorithmcombines optimal matching and heuristicmatching, so it greatly improves the auction effect while ensuring computational efficiency. Considerable theoretical analysis and experimental results prove that the performance of the proposed TCAmechanismis significantly superior to that of other auctionmechanismswhile holding the desirable properties.

关键词： cooperative beamforming   intelligent reflecting surface   mobile edge computing   partial computation offloading   resource allocation  

摘要： In order to improve the performance of the mobile edge computing (MEC) system, the intelligent reflecting surface (IRS) has recently been included. This paper investigates the computation performance of an IRS-aided MEC system, where an access point services multi-MEC devices by utilizing two distributed IRSs to operate a partial offloading strategy. Through collaboratively constructing the cooperative passive beamforming at the two IRSs, the central processing unit (CPU) frequency, the offloading time allocation, and the transmit power of users, an optimization problem is constructed to maximize the sum computation rate of the proposed system. To tackle this non-convex issue, the authors first illustrate that the design of passive beamforming is independent to optimize other variables and propose to solve it alternatively. Then the joint optimization problem of other parameters is proved to be a convex problem and is resolvable by adopting the Lagrange dual approach. Compared with the benchmark schemes, the simulation outcomes demonstrated that the performance of the proposed system is vastly improved by deploying two distributed IRSs.