关键词： Mobile edge computing   convex optimization   D2D communication   relay selection   resource allocation  

摘要： In order to improve resource utilization and network capacity, we propose the Device-to-Device (D2D) enabled Mobile Edge Computing (MEC) system, where multiple Smart Devices (SDs) transmit the offloading data to the MEC server with the help of wireless access point (WAP) selected from multiple WAPs. The SD uses the chosen WAP as the communication relay between the MEC server and itself. Aimed to minimize the total energy consumption of the system and satisfy the SDs demand on delay, we jointly optimize relay selection and resource allocation in D2D-enabled MEC system. The problem is formulated as an integer-mixed non-convex optimization problem which is a NP-hard problem. We thus propose a two-phase optimization algorithm that jointly optimizes relay selection policy and resource allocation strategy. In first phase, the original problem is converted into a convex optimization problem by using convex optimization techniques, and the optimal relay selection policy can be achieved by solving the relay selection problem. After obtaining the relay selection policy, the original problem is transformed into a resource allocation problem solved by leveraging the Lagrange Method in the second phase. Furthermore, the proposed algorithm is a low-complexity algorithm which is associated with the root finding method. The optimal relay selection policy and resource allocation strategy can be found in polynomial time. The extensive simulation results are provided to indicate that the D2D-enabled MEC system achieves remarkable results in energy saving. Compared with other baseline methods, our proposed algorithm can not only achieve the optimal solution with less time cost, but also improve the energy efficiency and network capacity.

关键词： Mobile edge computing   Internet of Vehicles   task scheduling   deep deterministic policy gradient  

摘要： Mobile edge computing (MEC) is a promising technology to support mission-critical vehicular applications, such as intelligent path planning and safety applications. In this paper, a collaborative edge computing framework is developed to reduce the computing service latency and improve service reliability for vehicular networks. First, a task partition and scheduling algorithm (TPSA) is proposed to decide the workload allocation and schedule the execution order of the tasks offloaded to the edge servers given a computation offloading strategy. Second, an artificial intelligence (AI) based collaborative computing approach is developed to determine the task offloading, computing, and result delivery policy for vehicles. Specifically, the offloading and computing problem is formulated as a Markov decision process. A deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to find the optimal solution in a complex urban transportation network. By our approach, the service cost, which includes computing service latency and service failure penalty, can be minimized via the optimal workload assignment and server selection in collaborative computing. Simulation results show that the proposed AI-based collaborative computing approach can adapt to a highly dynamic environment with outstanding performance.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is a new technology that provides an IT service environment and cloud-computing resource at the edge of the mobile network, aiming to improve the user experience by reducing latency and delivering highly efficient services. A mobile health care system is a typical case of MEC. Recent advances in mobile health technology enable us to automatically record the states and behaviors of patients in hospitals by identifying their faces. Instead of using a centralized server or cloud to process these heavy loading analyses, MEC analyzes the patients' behavior locally in their rooms to enable quick response to their accidents and reduce the storage size of large-scale patient information. By using MEC, only one frontal face for each patient is available in the gallery to reduce the local computational power. Although a patient's face can be captured from any other pose, doing so challenges the face-identification algorithm. Several studies have been published to solve this problem, but most of them rely on pose estimation and landmark detection. In this paper, we propose a deeper convolutional neural network to extract the pose-invariant face features and synthesize the virtual poses simultaneously, aiming to develop a landmark-free and pose-estimation-free frontal-face synthesis system. The deeper network was divided into multiple overlapped local networks, each one trained to synthesize a small pose change, such as one larger than45 degrees to30 degrees. The local networks were jointly trained to synthesize the frontal face from a nonfrontal pose in a progressive manner. By stacking multiple local networks, we were able to extract more robust pose-invariant features, to generate multiple virtual poses before the frontal face was synthesized. The pose-invariant features and virtual poses were incorporated to identify the face across poses. The proposed method was evaluated on the CAS-PEAL dataset with 16 layers and fine-tuned on the FERET dataset

关键词： Mobile edge computing  

摘要： This paper extends the paradigm of "mobile edge learning (MEL)" by designing an optimal task allocation scheme for training a machine learning model in an asynchronous manner across mutiple edge nodes or learners connected via a resource-constrained wireless edge network. The optimization is done such that the portion of the task allotted to each learner is completed within a given global delay constraint and a local maximum energy consumption limit. The time and energy consumed are related directly to the heterogeneous communication and computational capabilities of the learners;i.e. the proposed model is heterogeneity aware (HA). Because the resulting optimization is an NP-hard quadratically-constrained integer linear program (QCILP), a two-step suggest-and-improve (SAI) solution is proposed based on using the solution of the relaxed synchronous problem to obtain the solution to the asynchronous problem. The proposed HA asynchronous (HA-Asyn) approach is compared against the HA synchronous (HA-Sync) scheme and the heterogeneity unaware (HU) equal batch allocation scheme. Results from a system of 20 learners tested for various completion time and energy consumption constraints show that the proposed HA-Asyn method works better than the HU synchronous/asynchronous (HU-Sync/Asyn) approach and can provide gains of up-to 25% compared to the HA-Sync scheme. © 2020, CC BY.

关键词： Mobile edge computing  

摘要： We consider distributed machine learning at the wireless edge, where a parameter server builds a global model with the help of multiple wireless edge devices that perform computations on local dataset partitions. Edge devices transmit the result of their computations (updates of current global model) to the server using a fixed rate and orthogonal multiple access over an error prone wireless channel. In case of a transmission error, the undelivered packet is retransmitted until successfully decoded at the receiver. Leveraging on the fundamental tradeoff between computation and communication in distributed systems, our aim is to derive how many edge devices are needed to minimize the average completion time while guaranteeing convergence. We provide upper and lower bounds for the average completion and we find a necessary condition for adding edge devices in two asymptotic regimes, namely the large dataset and the high accuracy regime. Conducted experiments on real datasets and numerical results confirm our analysis and substantiate our claim that the number of edge devices should be carefully selected for timely distributed edge learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Smart device users spend most of the fragmentation time in the entertainment applications such as videos and films. The migration and reconstruction of video copies can improve the storage efficiency in distributed mobile edge computing, and the prediction of video hits is the premise for migrating video copies. This paper proposes a new prediction approach for video hits based on the combination of correlation analysis and wavelet neural network (WNN). This is achieved by establishing a video index quantification system and analyzing the correlation between the video to be predicted and already online videos. Then, the similar videos are selected as the influencing factors of video hits. Compared with the autoregressive integrated moving average (ARIMA) and gray prediction, the proposed approach has a higher prediction accuracy and a broader application scope.

关键词： Mobile edge computing   Computation offloading   Non-convex optimization  

摘要： Mobile terminals (MTs) within the coverage of both the WiFi and cellular network can offload computation tasks via the WiFi access point to efficiently release the cellular network congestion and reduce the load on base stations. In this paper, a novel scheme of joint WiFi and cellular offloading is proposed to optimally reduce the latency and energy consumption of MTs in task processing. Based on the statistical characteristics of MTs' task generation, our scheme serves as strategic guidance for computation offloading without frequent execution of the optimization algorithm. MTs with the different channel access priorities are also considered in our scheme. A balance factor is introduced to flexibly adjust the minimization between the energy consumption of MT and the processing latency of its tasks. We design an iterative algorithm combining the linear programming with the Alternating Optimization technique to efficiently solve the non-convex problem for computation tasks offloading decision. The numerical results demonstrate that our scheme can largely improve the system performance, and the superiority of our scheme is shown in all scenarios.

关键词： Mobile edge computing  

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 paper, 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 study both the uniform and differentiated pricing schemes. For differentiated pricing, we prove that the same price should be charged to the cached programs of the same workload. Copyright © 2020, The Autho

关键词： Mobile edge computing   optimisation   heuristic   server assignment   geo-aware   cloud computing  

摘要： As mobile devices have become the preferred tool for communication, work, and entertainment, traffic at the edge of the network is growing more rapidly than ever. To improve user experience, commodity servers are deployed in the edge to form a decentralised network of mini datacenters each serving a localised region. A challenge is how to place these servers geographically to maximise the offloading benefit and be close to the users they respectively serve. We introduce a formulation for this problem to serve applications that involve pairwise communication between mobile devices at different geolocations. We explore several heuristic solutions and compare them in an evaluation using both real-world and synthetic datasets. [GRAPHICS] .

关键词： Reliability   Cloud computing   Approximation algorithms   Heuristic algorithms   Mobile computing   Edge computing   Network function virtualization   Reliability-aware VNF instances provisioning   VNF instance placements   virtualized network function implementation   approximation algorithms   generalized assignment problems   dynamic programming   mobile edge computing   combinatorial optimization problems  

摘要： Along with Network Function Virtualization (NFV), Mobile Edge Computing (MEC) is becoming a new computing paradigm that enables accommodating innovative applications and services with stringent response delay and resource requirements, including autonomous vehicles and augmented reality. Provisioning reliable network services for users is the top priority of most network service providers, as unreliable services or severe service failures can result in tremendous losses of users, particularly for their mission-critical applications. In this paper, we study reliability-aware VNF instances provisioning in an MEC, where different users request different network services with different reliability requirements through paying their requested services with the aim to maximize the network throughput. To this end, we first formulate a novel reliability-aware VNF instance placement problem by provisioning primary and secondary VNF instances at different cloudlets in MEC for each user while meeting the specified reliability requirement of the user request. We then show that the problem is NP-hard and formulate an Integer Linear Programming (ILP) solution. Due to the NP-hardness of the problem, we instead devise an approximation algorithm with a logarithmic approximation ratio for the problem. Moreover, we also consider two special cases of the problem. For one special case where each request only requests one primary and one secondary VNF instances, the problem is still NP-hard, and we devise a constant approximation algorithm for it. For another special case where different VNFs have the same amounts of computing resource demands, we show that it is polynomial-time solvable by developing a dynamic programming solution for it. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and the empirical results of the algorithms outperform their analytical counter