关键词： Mobile edge computing  

摘要： As mobile edge computing (MEC) finds widespread use for relieving the computational burden of compute- and interaction-intensive applications on end user devices, understanding the resulting delay and cost performance is drawing significant attention. While most existing works focus on single-task offloading in single-hop MEC networks, next generation applications (e.g., industrial automation, augmented/virtual reality) require advance models and algorithms for dynamic configuration of multitask services over multi-hop MEC networks. In this work, we leverage recent advances in dynamic cloud network control to provide a comprehensive study of the performance of multi-hop MEC networks, addressing the key problems of multi-task offloading, timely packet scheduling, and joint computation and communication resource allocation. We present a fully distributed algorithm based on Lyapunov control theory that achieves throughput optimal performance with delay and cost guarantees. Simulation results validate our theoretical analysis and provide insightful guidelines on overall system design and the interplay between communication and computation resources. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Multi-access Edge Computing (MEC) facilitates the deployment of critical applications with stringent QoS requirements, latency in particular. This paper considers the problem of jointly planning the availability of computational resources at the edge, the slicing of mobile network and edge computation resources, and the routing of heterogeneous traffic types to the various slices. These aspects are intertwined and must be addressed together to provide the desired QoS to all mobile users and traffic types still keeping costs under control. We formulate our problem as a mixed-integer nonlinear program (MINLP) and we define a heuristic, named Neighbor Exploration and Sequential Fixing (NESF), to facilitate the solution of the problem. The approach allows network operators to fine tune the network operation cost and the total latency experienced by users. We evaluate the performance of the proposed model and heuristic against two natural greedy approaches. We show the impact of the variation of all the considered parameters (viz., different types of traffic, tolerable latency, network topology and bandwidth, computation and link capacity) on the defined model. Numerical results demonstrate that NESF is very effective, achieving near-optimal planning and resource allocation solutions in a very short computing time even for large-scale network scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Online job dispatching   Serverless computing   Computation offloading   Performance evaluation   Distributed cloud   Internet of Things   Mobile Edge Computing  

摘要： Edge computing is a promising solution to enable low-latency Internet of Things (IoT) applications, by shifting computation from remote data centers to local devices, less powerful but closer to the end user devices. However, this creates the challenge on how to best assign clients to edge nodes offering compute capabilities. So far, two antithetical architectures are proposed: centralized resource orchestration or distributed overlay. In this work we explore a third way, called uncoordinated access, which consists in letting every device exploring multiple opportunities, to opportunistically embrace the heterogeneity of network and load conditions towards diverse edge nodes. In particular, our contribution is intended for emerging serverless IoT applications, which do not have a state on the edge nodes executing tasks. We model the proposed system as a set of M/M/1 queues and show that it achieves a smaller delay than single edge node allocation. Furthermore, we compare uncoordinated access with state-of-the-art centralized and distributed alternatives in testbed experiments under more realistic conditions. Based on the results, our proposed approach, which requires a tiny fraction of the complexity of the alternatives in both the device and network components, is very effective in using the network resources, while incurring only a small penalty in terms of increased compute load and high percentiles of delay.

关键词： 5G   Collision avoidance   Multiple access edge computing   Mobile edge computing   Network function virtualization   Trajectory prediction  

摘要： Automotive is considered one of the driving use cases for the 5th Generation (5G) systems, which currently formulates numerous scenarios and Key Performance Indicators (KPIs), via advanced Vehicle-to-everything (V2X) services and applications. Minimum end-to-end delay, as well as advanced contextual awareness requirements, pose novel architectural and functional challenges. This paper exploits two key enablers, namely Multiple Access/Mobile Edge Computing (MEC) and Network Function Virtualization (NFV), and acts in a two-fold manner: Firstly, it proposes a hybrid architecture for 5G systems, which exploits the afore-mentioned technologies, and performs computing resources' selection among MEC and/or centralized, cloud-based resources (as VNFs), towards efficient service orchestration. The second contribution of this paper is a novel V2X service and algorithm, namely VRU-safe, that operates on top of the proposed architecture. VRU-Safe is an efficient, lightweight, low time complexity scheme, capable of identifying and predicting potential imminent road hazards between moving vehicles and Vulnerable Road Users (VRUs). The performance and viability of the proposed solutions are evaluated in a real-world 5G testbed in Europe.

关键词： Mobile edge computing  

摘要： We consider the problem of intelligent and efficient task allocation mechanism in large-scale mobile edge computing (MEC), which can reduce delay and energy consumption in a parallel and distributed optimization. In this paper, we study the joint optimization model to consider cooperative task management mechanism among mobile terminals (MT), macro cell base station (MBS), and multiple small cell base station (SBS) for large-scale MEC applications. We propose a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of low delay and low energy consumption in the MEC system which formulates the task allocation under those requirements as a nonlinear 0-1 integer programming problem. To solve the optimization problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing (for global variables updating) and the Cyclic Block coordinate Gradient Projection (CBGP, for local variables updating) methods, which can guarantee convergence and reduce computational complexity with a good scalability. Numerical results demonstrate the effectiveness of the proposed mechanism and it can effectively reduce delay and energy consumption for a large-scale MEC system. Copyright © 2020, The Authors. All rights reserved.

关键词： mobile edge computing   heterogeneous clouds   energy saving   delay bounds   dynamic programming  

摘要： By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.

关键词： mobile edge computing   peer-to-peer   task offloading   internet of vehicles   fault-tolerance  

摘要： Intelligent vehicles and their applications increasingly demand high computing power and low task delays, which poses significant challenges for providing reliable and efficient vehicle services. Mobile edge computing (MEC) is a new model that reduces the completion time of tasks and improves vehicle service by performing computation offloading near the moving vehicles. Considering the high-speed mobility of the vehicles and the unstable connection of the wireless cellular network, symmetric and geographically distributed edge servers are regarded as peers in a peer-to-peer (P2P) network, and a P2P-based vehicle edge offloading model is proposed in this paper to determine the optimal offloading server for the vehicle and the offloading ratio of tasks to achieve the goal of minimizing execution time. Because the edge computing infrastructure is deployed at the edge of the network, the data in the edge nodes are easily damaged or lost. Therefore, a P2P-based edge node fault tolerance mechanism is proposed to improve the reliability and fault tolerance of the system. The feasibility and effectiveness of our proposed system have been verified through simulation experiments, which greatly reduces the task completion delay.

关键词： Deep learning   Computational modeling   Edge computing   Data models   Informatics   Robustness   5G mobile communication   Adversarial examples   deep learning   generative adversarial networks (GAN)   industrial artificial intelligence systems (IAISs)   mobile edge computing  

摘要： Individually reinforcing the robustness of a single deep learning model only gives limited security guarantees especially when facing adversarial examples. In this article, we propose DeSVig, a decentralized swift vigilance framework to identify adversarial attacks in an industrial artificial intelligence systems (IAISs), which enables IAISs to correct the mistake in a few seconds. The DeSVig is highly decentralized, which improves the effectiveness of recognizing abnormal inputs. We try to overcome the challenges on ultralow latency caused by dynamics in industries using peculiarly designated mobile edge computing and generative adversarial networks. The most important advantage of our work is that it can significantly reduce the failure risks of being deceived by adversarial examples, which is critical for safety-prioritized and delay-sensitive environments. In our experiments, adversarial examples of industrial electronic components are generated by several classical attacking models. Experimental results demonstrate that the DeSVig is more robust, efficient, and scalable than some state-of-art defenses.

关键词： Internet of Things   Computation offloading   Mobile edge computing   Evolutionary game theory   Reinforcement learning  

摘要： Due to the limited computing resources and energy of IoT devices, complex computing tasks are offloaded to sufficient computing servers, such as Cloud Center. However, offloading may increase the latency and congestion of the IoT network. Mobile Edge Computing (MEC) is a promising approach, which can decrease the delay and energy consumption of IoT devices significantly. In this paper, we investigate the problem of multi-user computation offloading under dynamic environment. Considering the channel interference when multiple IoT devices offload computing tasks via wireless channels at the same time, we formulate the computation offloading as an evolutionary game model. We use the replicator dynamics to analyze the evolutionary process of IoT devices and prove that multi-user computation offloading exists unique Evolutionary Stability Strategy (ESS). Finally, we design an evolutionary game algorithm based on reinforcement learning in practical application scenarios. Experiments can verify the convergence and performance of the proposed algorithm in multi-user scenarios. (C) 2020 Elsevier GmbH. All rights reserved.

关键词： edge cloud placement   mixed-integer quadratic programming   mobile edge computing   user allocation   workload balance  

摘要： Mobile edge computing is emerging as a novel ubiquitous computing platform to overcome the limit resources of mobile devices and bandwidth bottleneck of the core network in mobile cloud computing. In mobile edge computing, it is a significant issue for cost reduction and QoS improvement to place edge clouds at the edge network as a small data center to serve users. In this paper, we study the edge cloud placement problem, which is to place the edge clouds at the candidate locations and allocate the mobile users to the edge clouds. Specifically, we formulate it as a multiobjective optimization problem with objective to balance the workload between edge clouds and minimize the service communication delay of mobile users. To this end, we propose an approximate approach that adopted the K-means and mixed-integer quadratic programming. Furthermore, we conduct experiments based on Shanghai Telecom's base station data set and compare our approach with other representative approaches. The results show that our approach performs better to some extent in terms of workload balance and communication delay and validate the proposed approach.