关键词： Containers   Cloud computing   Costs   Quality of service   Licenses   Virtualization   Real-time systems   Quality of experience (QoE)   container migration   mobile edge computing   virtualization   real-time applications   edge cloud   migration cost   inter-edge cloud bandwidth  

摘要： Mobile Edge Cloud (MEC) technology is envisioned to play a key role in next generation mobile networks by supporting low-latency applications using geographically distributed local cloud clusters. However, MEC faces challenges of resource assignment and load balancing to support user mobility and latency-sensitive applications. Virtualized resource reallocation techniques including dynamic service migration are evolving to achieve load balance, fault tolerance and system maintenance objectives for resource constrained edge nodes. In this work, a compute and network-aware lightweight resource sharing framework with dynamic container migration, ShareOn, is proposed. The migration framework is validated using a set of heterogeneous edge cloud nodes distributed in San Francisco city, serving mobile taxicab users across that region. The end-to-end system is implemented using a container hypervisor called LXD (Linux Container Hypervisor) executing a real-time application to detect license number plates in automobiles. The system is evaluated based on key metrics associated with application quality-of-service (QoS) and network efficiency such as the average system response time and the migration cost for different combinations of load, compute resources, inter-edge cloud bandwidth, network and user latency. A detailed migration cost analysis enables evaluation of migration strategies to improve ShareOn's performance in comparison to alternative migration techniques, achieving a gain of 15-22% in system response time for highly loaded edge cloud nodes.

关键词： Authentication   Servers   3GPP   Cellular networks   Prefetching   Long Term Evolution   Multi-access edge computing   Mobile edge computing   multi-access edge computing   authentication   mobility   latency   3GPP cellular networks  

摘要： Multi-Access or Mobile Edge Computing (MEC) is being deployed by 4G/5G operators to provide computational services at lower latencies. Federating MECs across operators expands capability, capacity, and coverage but gives rise to two issues for continuous service during roaming without re-authentication-third-party authentication and application mobility. In this work, we propose a Federated State transfer and 3rd-party Authentication (FS3A) mechanism that uses a transparent proxy to transfer the information of both authentication and application state across operators to resolve these issues. The FS3A proxy is kept transparent with virtual counterparts to avoid any changes to existing MEC and cellular architectures. FS3A provides users with a token which, when authenticated by an MEC, can be reused across operators for faster authentication. Prefetching of subscription and state is also proposed to further reduce authentication and application mobility latencies. We evaluated FS3A on an OpenAirInterface (OAI)-based testbed and the results show that token reuse and subscription prefetching reduce the authentication latency by 53-65%, compared to complete re-authentication, while state prefetching reduces application mobility latency by 51-91%, compared to no prefetching. Overall, FS3A reduces the service interruption time by 33%, compared to no token reuse and prefetching.

关键词： Mobile handsets   Task analysis   Cloud computing   Servers   Device-to-device communication   Energy consumption   Resource management   Computation offloading   social-aware   mobile edge computing   social networks   mobility  

摘要： With increasing computation-intensive tasks of various applications running on mobile devices, the limitation of computing resources and battery capacity on mobile devices makes it impossible to meet the users' Quality of Service (QoS). Fortunately, with the emergence of mobile edge computing (MEC), mobile devices can offload tasks to edge servers to efficiently solve the above problems. However, meeting the users' QoS requirements with the help of deployed MEC facilities is still challenging since mobile users' service demands vary depending on their dynamic location. In addition, increasing the number of edge servers to meet the requirements of applications would burden the initial investment and maintenance fee accordingly. In this case, using idle resources from nearby mobiles may become an effective solution. Most of the existing works do not consider the mobility of devices and users' willingness to share. Therefore, in this paper, we propose the mobile device selection algorithms (MDSA), in which the social relationship, location correlation, and mobile activity of mobile devices were considered in the selection of target mobile devices, providing device-to-device offloading. In addition, we propose the joint social-aware and mobility-aware computation offloading algorithm (JSMCO) based on the improved Kuhn-Munkres (KM) algorithm to obtain a resource allocation strategy that minimizes the energy consumption while satisfying the minimum latency condition. The proposed algorithms have been verified to reduce the offloading success rate and decrease the users' time and energy consumption in extended real datasets.

关键词： Streaming media   Switches   Costs   Servers   Transcoding   Three-dimensional displays   Heuristic algorithms   360-degree video streaming   tiling   mobile edge computing   switching cost   multi-armed bandit  

摘要： Virtual Reality (VR) and Augmented Reality (AR) applications are expected to be a key driver for the 5G network. The need of the MEC support for caching and transcoding of omnidirectional content has been identified as a major topic of research. In this paper, we study the optimal caching problem in an MEC-enabled VR system for tile-based viewport-adaptive 360-degree video streaming. For content caching, the replacement of the cached content inevitably incurs content switching cost, which is largely ignored in caching policy design. In this work, we aim to find the optimal caching policy to optimize the long-term transmission quality by considering both transmission latency and content switching cost. We apply the combinatorial multi-armed bandit (CMAB) theory to solve the problem with no a-priori knowledge on content popularity. Moreover, the CUCB with switching cost (CUCBSC) algorithm is adopted in this scenario. A transformation mechanism is designed to transform the generated single period optimization (SPO) problem into a multiple choice knapsack problem (MCKP). Rigorous theoretical analysis on the performance of the proposed algorithm is also provided. Finally, the effectiveness of the proposed learning based caching policy is confirmed by simulation results in terms of learning, hit-ratio, transmission and content switching delay.

关键词： Task analysis   NOMA   Delays   Energy consumption   Resource management   Simulation   Servers   Multiuser multi-server computation offloading   mobile edge computing   NOMA   OMA   weighted hypergraph matching  

摘要： Since mobile devices typically have limited computation resources, offloading computation tasks to fog access points (F-APs) is a promising approach to support delay-sensitive and computation-intensive applications. This paper considers joint computation and communication resource allocation for multiuser multi-server systems, which aims to maximize the number of users being served and minimize the total energy consumption subject to delay tolerance constraints. The joint computation and communication resource allocation problem is solved optimally for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes. The joint user pairing and fog access point assignment problem for NOMA is proved to be NP-hard. For both NOMA and OMA, heuristic and optimal algorithms based on graph matching are designed. The optimal algorithms, though of high complexity, allow NOMA and OMA to be compared at their full potential and serve as benchmarks for evaluating the heuristic algorithms. Simulation results show that NOMA significantly outperforms OMA in terms of outage probability and energy consumption, especially for tight delay tolerance constraints and large computational tasks. Simulation results also demonstrate that our proposed NOMA and OMA schemes significantly outperform the swap-enabled matching algorithm widely used in the literature.

关键词： Roads   Vehicles   Cloud computing   Computer architecture   Sensors   Wireless sensor networks   Safety   Vehicular networks   VANETs   intelligent vehicles   vehicular cloud computing   cloudlets   cloud infrastructure   mobile edge computing   fog computing   road traffic management   intelligent transportation systems   ITS   machine-to-machine communications  

摘要： In this survey, we analyze the proposals of vehicular communication systems in the context of road traffic management. Starting with the definition of communications between vehicles (V2V), vehicles-to-infrastructure (V2I) and vehicles-to-everything (V2X), we first focus on the requirements and current standards for the Intelligent Transport Systems (ITS), including the maximum communication delay, the communication range and the size of messages (in the case of V2I transmission). After that, we analyze the use cases in line with the implementation of intelligent traffic management and review the respective methods that support or directly manage traffic on roads. One of the primary objectives of this paper is to highlight the architectures of four classes of systems able to support vehicular traffic management and communication between vehicles and roadside infrastructure, namely: vehicular cloud computing (VCC), cloudlets, Mobile Edge Computing (MEC) and fog computing. In this context, we also present our classification of the methods for these four classes of architectures. In the end, we provide our opinion on problems and limitations concerning the deployment of mechanisms belonging to each considered architecture class.

关键词： Task analysis   Heuristic algorithms   Wireless communication   Resource management   Computational modeling   Servers   Fading channels   Mobile edge computing   federated learning   deep reinforcement learning   online computing offload   wireless powered communication  

摘要： Mobile edge computing (MEC) is considered a more effective technological solution for developing the Internet of Things (IoT) by providing cloud-like capabilities for mobile users. This article combines wireless powered communication (WPC) technology with an MEC network, where a base station (BS) can transfer wireless energy to edge users (EUs) and execute computation-intensive tasks through task offloading. Traditional numerical optimization methods are time-consuming approaches for solving this problem in time-varying wireless channels, and centralized deep reinforcement learning (DRL) is not stable in large-scale dynamic IoT networks. Therefore, we propose a federated DRL-based online task offloading and resource allocation (FDOR) algorithm. In this algorithm, DRL is executed in EUs, and federated learning (FL) uses the distributed architecture of MEC to aggregate and update the parameters. To further solve the problem of the non-IID data of mobile EUs, we devise an adaptive method that automatically adjusts the FDOR algorithm's learning rate. Simulation results demonstrate that the proposed FDOR algorithm is superior to the traditional numerical optimization method and the existing DRL algorithm in four aspects: convergence speed, execution delay, overall calculation rate and stability in large-scale and dynamic IoT.

关键词： Resource management   Ultra reliable low latency communication   Task analysis   Downlink   Delays   Uplink   Optimization   Branch-and-bound   finite blocklength transmission   mobile edge computing   successive convex approximation   ultra-reliable low latency communication  

摘要： In this paper, we study resource allocation algorithm design for multi-user orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) in mobile edge computing (MEC) systems. To meet the stringent end-to-end delay and reliability requirements of URLLC MEC systems, we employ joint uplink-downlink resource allocation and finite blocklength transmission. Furthermore, we propose a partial time overlap between the uplink and downlink frames to minimize the end-to-end delay, which introduces a new time causality constraint. The proposed resource allocation algorithm is formulated as an optimization problem for minimization of the total weighted power consumption of the network under a constraint on the number of URLLC user bits computed within the maximum allowable computation time, i.e., the end-to-end delay of a computation task of each user. Despite the non-convexity and the complicated structure of the formulated optimization problem, we develop a globally optimal solution using a branch-and-bound approach based on discrete monotonic optimization theory. The branch-and-bound algorithm minimizes an upper bound on the total power consumption until convergence to the globally optimal value. Furthermore, to strike a balance between computational complexity and performance, we propose two efficient suboptimal algorithms. For the first suboptimal scheme, the optimization problem is reformulated in the canonical form of difference of convex programming. Then, successive convex approximation (SCA) is used to determine a locally optimal solution. For the second suboptimal scheme, we use a high signal-to-noise ratio approximation for the channel dispersion. Then, via novel transformations, we convert the non-convex quality-of-service constraints of the original problem into equivalent second-order-cone constraints. Our simulation results reveal that the proposed resource allocation algorithm design facilitates URLLC in MEC systems, an

关键词： Federated learning   differential privacy   communication round   mobile edge computing  

摘要： Federated learning (FL), as a type of collaborative machine learning framework, is capable of preserving private data from mobile terminals (MTs) while training the data into useful models. Nevertheless, from a viewpoint of information theory, it is still possible for a curious server to infer private information from the shared models uploaded by MTs. To address this problem, we first make use of the concept of local differential privacy (LDP), and propose a user-level differential privacy (UDP) algorithm by adding artificial noise to the shared models before uploading them to servers. According to our analysis, the UDP framework can realize (is an element of(i), delta(i))-LDP for the ith MT with adjustable privacy protection levels by varying the variances of the artificial noise processes. We then derive a theoretical convergence upper-bound for the UDP algorithm. It reveals that there exists an optimal number of communication rounds to achieve the best learning performance. More importantly, we propose a communication rounds discounting (CRD) method. Compared with the heuristic search method, the proposed CRD method can achieve a much better trade-off between the computational complexity of searching and the convergence performance. Extensive experiments indicate that our UDP algorithm using the proposed CRD method can effectively improve both the training efficiency and model quality for the given privacy protection levels.

关键词： mobile edge computing   MEC   slack time   deadline and energy-efficient task scheduling   DEETS   task scheduling   mob-cloud architecture  

摘要： These days, more and more web are converting into mobile devices such as the internet of things (IoT). These applications are distributed in nature and installed on mobile and servers to achieve many business goals. With this motivation, mobile edge computing (MEC) is an evolving paradigm that reduces the energy consumption of mobile devices through the offload process. However, currently all studies only concentrated on mobile energy and widely overlooked the energy usage of the computing node resources and application deadlines during offloading and scheduling in the MEC network. This research suggests a new mobile edge cloud (mob-cloud) architecture that minimises the nodes energy and meets the deadline requirements of applications. This research formulates this problem as the convex optimisation problem under linear integer programming. The mob-cloud consists of different types of virtual machines which are implementing at the edge computing. This study introduces a novel deadline and energy-efficient task scheduling (DEETS) algorithm framework consisting of task sequencing, resource searching, and mobility-aware scheduling. Furthermore, DEETS keeps tradeoff performances between resource energy usage and task deadline. Simulation results show that the proposed DEETS outperformed all contemporary methods regarding energy usage and deadlines of applications by 50%.