关键词： Mobile edge computing  

摘要： To support emerging real-time monitoring and control applications, the timeliness of computation results is of critical importance to mobile-edge computing (MEC) systems. We propose a performance metric called age of task (AoT) based on the concept of age of information (AoI), to evaluate the temporal value of computation tasks. In this paper, we consider a system consisting of a single MEC server and one mobile device running several applications. We study an age minimization problem by jointly considering task scheduling, computation offloading and energy consumption. To solve the problem efficiently, we propose a light-weight task scheduling and computation offloading algorithm. Through performance evaluation, we show that our proposed age-based solution is competitive when compared with traditional strategies. Copyright © 2019, The Authors. All rights reserved.

关键词： 5G   Computation offloading   Mobile edge computing   Energy saving   Internet of Things  

摘要： Mobile edge computing (MEC), as the key technology to improve user experience in a 5G network, can effectively reduce network transmission delay. Task migration can migrate complex tasks to remote edge servers through wireless networks, solving the problems of insufficient computing capacity and limited battery capacity of mobile terminals. Therefore, in order to solve the problem of how to realize low-energy migration of complex dependent applications, a subtask partitioning model with minimum energy consumption is constructed based on the relationship between the subtasks. Aiming at the problem of execution time constraints, the genetic algorithm is used to find the optimal solution, and the migration decision results of each subtask are obtained. In addition, an improved algorithm based on a genetic algorithm is proposed to dynamically adjust the optimal solution obtained by genetic algorithm by determining the proportion of task energy consumption and mobile phone residual power. According to the experimental results, it can be concluded that the fine-grained task migration strategy combines the advantages of mobile edge computing, not only satisfies the smooth execution of tasks, but also reduces the energy consumption of terminal mobile devices. In addition, experiments show that the improved algorithm is more in line with users' expectations. When the residual power of mobile devices is reduced to a certain value, tasks are migrated to MEC server to prolong standby time.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is an emerging paradigm where users offload computationally intensive job s to the Access Point (AP). Given that the AP's resources are shared by selfish mobile users, pricing is a useful tool for incentivising users to internalize the negative externality of delay they cause to other users. Different users have different negative valuations towards delay as some are more delay sensitive. To serve heterogeneous users, we propose a priority pricing scheme where users can get served first for a higher price. Our goal is to find the prices such that in decision making, users will choose the class and the offloading frequency that jointly maximize social welfare. With the assumption that the AP knows users' profit functions, we derive in semi-closed form the optimal prices. However in practice, the reporting of users's profit information incurs a large signalling overhead. Besides, in reality users might falsely report their private profit information. To overcome this, we further propose learning-based pricing mechanisms wher e no knowledge of individual user profit functions is required. At equilibrium, the optimal prices and average edge delays are learnt, and users have chosen the correct priority class and offload at the socially optimal frequency. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Federated Learning (FL) is capable of leveraging massively distributed private data, e.g., on mobile phones and IoT devices, to collaboratively train a shared machine learning model with the help of a cloud server. However, its iterative training process results in intolerable communication latency, and causes huge burdens on the backbone network. Thus, reducing the communication overhead is critical to implement FL in practice. Meanwhile, the model performance degradation due to the unique non-IID data distribution at different devices is another big issue for FL. In this paper, by introducing the mobile edge computing platform as an intermediary structure, we propose a hierarchical FL architecture to reduce the communication rounds between users and the cloud. In particular, a Hierarchical Federated Averaging (HierFAVG) algorithm is proposed, which allows multiple local aggregations at each edge server before one global aggregation at the cloud. We establish the convergence of HierFAVG for both convex and non-convex objective functions with non-IID user data. It is demonstrated that HierFAVG can reach a desired model performance with less communication, and outperform the traditional Federated Averaging algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Congestion awareness   Dynamic offloading   Mobile edge computing  

摘要： In this paper, we focus on the problem of assigning resources for offloading the computationally intensive tasks of mobile applications from resource-scarce mobile devices to servers located in edge networks to minimize the average completion time of the applications. We consider a system model in which a set of mobile devices is connected to an edge server via a shared communication channel. In addition, we study for the first time the offloading problem for general applications by accounting for the capacity limitations of both the communication channel and the edge server. We first formulate a static offloading problem as a mixed-integer linear programming problem. Then, we extend the static problem to a dynamic offloading problem in which an application can be executed at any time. Due to the complexity of the problem, it is difficult to obtain a solution within a realistic time period. Therefore, we propose an efficient heuristic approach based on congestion awareness. We demonstrate that our proposed heuristic algorithm significantly outperforms previous offloading algorithms in terms of the average completion time.

关键词： Mobile edge computing  

摘要： Network softwarization triggered a new wave of innovation in modern network design. The next generation of mobile networks embraces this trend. Mobile-edge computing (MEC) is a key part of emerging mobile networks that enables ultra-low latency mission-critical application such as vehicle-to vehicle communication. MEC aims at bringing delay-sensitive applications closer to the radio access network to enable ultra-low latency for users and decrease the back-haul pressure on mobile service providers. However, there are no practical solutions to enable mobility at MEC where connections are no longer anchored to the core network and serving applications are supposed to move as their users move. We propose the mobile-edge gateway (MEGW) to address this gap. MEGW enables mobility for MEC applications transparently and without requiring any modifications to existing protocols and applications. MEGW supports mobility by reconstructing mobile users' location via listening to LTE control plane in addition to using two-stage location-dependent traffic steering for edge connections. Networks can incrementally upgrade to support MEC by upgrading some IP router to programmable switches that run MEGW. We have implemented MEGW using P4 language and verified its compatibility with existing LTE networks in a testbed running reference LTE protocol stack. Furthermore, using packet-level simulations we show that the two-stage traffic steering algorithm reduces the number of application migrations and simplifies service provisioning. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing   Smart city edge server placement   Workload balancing   Access delay  

摘要： With the rapid increase in the development of the Internet of Things and 5G networks in the smart city context, a large amount of data (i.e., big data) is expected to be generated, resulting in increased latency for the traditional cloud computing paradigm. To reduce the latency, mobile edge computing has been considered for offloading a part of the workload from mobile devices to nearby edge servers that have sufficient computation resources. Although there has been significant research in the field of mobile edge computing, little attention has been given to understanding the placement of edge servers in smart cities to optimize the mobile edge computing network performance. In this paper, we study the edge server placement problem in mobile edge computing environments for smart cities. First, we formulate the problem as a multi-objective constraint optimization problem that places edge servers in some strategic locations with the objective to make balance the workloads of edge servers and minimize the access delay between the mobile user and edge server. Then, we adopt mixed integer programming to find the optimal solution. Experimental results based on Shanghai Telecom's base station dataset show that our approach outperforms several representative approaches in terms of access delay and workload balancing. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Mobile edge computing   QoS   Service recommendation   Edge server similarity  

摘要： Mobile edge computing is an emerging technology that provides services within the close proximity of mobile subscribers by edge servers that are deployed in each edge server. Mobile edge computing platform enables application developers and content providers to serve context-aware services (such as service recommendation) by using real time radio access network information. In service recommendation system, quality of service (QoS) prediction plays an important role when mobile devices or users want to invoke services that can satisfy user QoS requirements. However, user mobility (e.g., from one edge server to another) often makes service QoS prediction values deviate from actual values in traditional mobile networks. Unfortunately, many existing service recommendation approaches fail to consider user mobility. In this paper, we propose a service recommendation approach based on collaborative filtering and make QoS prediction based on user mobility. This approach initially calculates user or edge server similarity and selects the Top-K most-similar neighbors, predicts service QoS, and then makes service recommendation. We have implemented our proposed approach with experiments based on Shanghai Telecom datasets. Experimental results show that our approach can significantly improve on the accuracy of service recommendation in mobile edge computing. (C) 2017 Elsevier Inc. All rights reserved.

关键词： software defined networks   mobile edge computing   reinforcement learning   stochastic learning automata   game theory   data offloading   pricing   optimization  

摘要： Software Defined Networks (SDN) and Mobile Edge Computing (MEC), capable of dynamically managing and satisfying the end-users computing demands, have emerged as key enabling technologies of 5G networks. In this paper, the joint problem of MEC server selection by the end-users and their optimal data offloading, as well as the optimal price setting by the MEC servers is studied in a multiple MEC servers and multiple end-users environment. The flexibility and programmability offered by the SDN technology enables the realistic implementation of the proposed framework. Initially, an SDN controller executes a reinforcement learning framework based on the theory of stochastic learning automata towards enabling the end-users to select a MEC server to offload their data. The discount offered by the MEC server, its congestion and its penetration in terms of serving end-users' computing tasks, and its announced pricing for its computing services are considered in the overall MEC selection process. To determine the end-users' data offloading portion to the selected MEC server, a non-cooperative game among the end-users of each server is formulated and the existence and uniqueness of the corresponding Nash Equilibrium is shown. An optimization problem of maximizing the MEC servers' profit is formulated and solved to determine the MEC servers' optimal pricing with respect to their offered computing services and the received offloaded data. To realize the proposed framework, an iterative and low-complexity algorithm is introduced and designed. The performance of the proposed approach was evaluated through modeling and simulation under several scenarios, with both homogeneous and heterogeneous end-users.

关键词： Mobile edge computing   computation offloading problem   non-cooperative game   pure-strategies   unmanned areal vehicles (UAVs)  

摘要： Recently, solutions based on mobile edge computing paradigm have been widely discussed in academia and industry. This paradigm offers solutions to address limitations, in terms of battery lifetime and processing power, of mobile and constrained devices. Despite the ever-increasing capabilities of these devices, resource requirements of applications can often transcend what is available within a single device. Offloading intensive computation tasks to a distant server can help applications reach their desired performances. In this work, we tackle the problem of offloading heavy computation tasks of unmanned aerial vehicles (UAVs) while achieving the best possible tradeoff between energy consumption, time delay, and computation cost. We focus on a scenario of a fleet of small UAVs performing an exploration mission. During their mission, these constrained devices have to carry-out highly intensive computation tasks such as pattern recognition and video preprocessing. We formulate the problem using a non-cooperative theoretical game with N players and three pure strategies. We provide a comprehensive proof for the existence of a Nash equilibrium and implement accordingly a distributed algorithm that converges to such an equilibrium. Extensive simulations are performed in order to provide thorough results and assess the performances of the approach compared to three other models. Results show that our algorithm outperforms all the three approaches. Our approach achieved in average about 19%, 58%, and 55% better results compared to local computing, offloading to the edge server, and offloading to base station, respectively.