关键词： UAV   mobile edge computing   video transcoding   utility   DDPG  

摘要： The integration of unmanned aerial vehicles (UAVs) in future communication networks has received great attention, and it plays an essential role in many applications, such as military reconnaissance, fire monitoring, etc. In this paper, we consider a UAV-aided video transmission system based on mobile edge computing (MEC). Considering the short latency requirements, the UAV acts as a MEC server to transcode the videos and as a relay to forward the transcoded videos to the ground base station. Subject to constraints on discrete variables and short latency, we aim to maximize the cumulative utility by jointly optimizing the power allocation, video transcoding policy, computational resources allocation, and UAV flight trajectory. The above non-convex optimization problem is modeled as a Markov decision process (MDP) and solved by a deep deterministic policy gradient (DDPG) algorithm to realize continuous action control by policy iteration. Simulation results show that the DDPG algorithm performs better than deep Q-learning network algorithm (DQN) and actor-critic (AC) algorithm.

关键词： Mobile edge computing   Computation offloading   Deep Neural Networks   Supervised learning   Unsupervised learning   Reinforcement learning  

摘要： Mobile Edge Computing (MEC) is a modern paradigm that involves moving computing and storage resources closer to the network edge, reducing latency, and enabling innovative, delay-sensitive applications. Within MEC, computation offloading refers to the process of transferring computationally intensive tasks or processes from mobile devices to edge servers, optimizing the performance of mobile applications. Traditional numerical optimization methods for computation offloading often necessitate numerous iterations to attain optimal solutions. In this paper, we provide a tutorial on how Deep Neural Networks (DNNs) resolve the challenges of computation offloading. The article explores various applications of DNNs in computation offloading, encompassing channel estimation, caching, AR and VR applications, resource allocation, mode selection, unmanned aerial vehicles (UAVs), and vehicle management. We present a comprehensive taxonomy that categorizes these applications, and offer an overview of existing schemes, comparing their effectiveness. Additionally, we outline the open research issues that can be addressed through the application of DNNs in MEC offloading. We also highlight specific challenges related to DNN utilization in computation offloading. In conclusion, we affirm that DNNs are widely acknowledged as invaluable tools for optimizing computation offloading in MEC.

关键词： Servers   Data integrity   Image edge detection   Computational efficiency   Heuristic algorithms   Maintenance engineering   Quality of service   Edge data integrity   mobile edge computing   peredge one-round   signature aggregation  

摘要： Mobile edge computing allows for high-performance and low-latency applications by delegating computation and data processing tasks to edge servers. However, ensuring the integrity of cached data on these servers can be challenging due to their limited resources. Current designs often use a per-edge multi-round approach, which necessitates multiple communication rounds between each edge server and the application vendor (AppVend). This approach results in high communication and computational costs, as well as the stragglers effect during batch verification. To address these inefficiencies, we propose a Hierarchical Signature Aggregation for Edge Data Integrity (HSA-EDI) verification design. Our design adopts a novel per-edge one-round approach, which significantly reduce the number of communication rounds to one for each edge server, while mitigating the impact of stragglers. Furthermore, it remarkably reduces computational costs through a hierarchical aggregation mechanism. This mechanism supports intra-edge signature aggregation at the edge server level, followed by inter-edge aggregation at the AppVend, which enhances overall efficiency. We then conduct a theoretical analysis of HSA-EDI's correctness, security, and communication, computation, and storage efficiency. Experimental results validate its superior performance over state-of-the-art designs.

关键词： Digital twin   continual learning   mobile edge computing   resource allocation   inference model accuracy   model retraining   approximation algorithm   online algorithm  

摘要： Propelled by recent advances in Mobile Edge Computing (MEC) and the Internet of Things (IoT), the digital twin technique has been envisioned as a de-facto driving force to bridge the virtual and physical worlds through creating digital portrayals of physical objects. In virtue of the flourishing of edge intelligence and abundant IoT data, data-driven modelling facilitates the implementation and maintenance of digital twins, where simulations of physical objects are usually performed based on Deep Neural Networks (DNNs). A significant advantage of adopting digital twins is to enable decisive prediction on the behaviours of objects in near future without waiting for that really happen. To provide accurate predictions, it is vital to keep each digital twin synchronized with its physical object in real-time. However, it is challenging to maintain the real-time synchronization between a digital twin and its physical object due to the dynamics of physical objects and sensing data drift over time, i.e., the live data from a physical object diverge from the model training data of its digital twin. To address this critical issue, continual learning is a promising solution to retrain models of digital twins incrementally. In this article, we investigate digital twin synchronization issues via continual learning in an MEC environment, with the aim to maximize the total utility gain, i.e., the enhanced model accuracy. We study two novel optimization problems: the static digital twin synchronization problem per time slot and the dynamic digital twin synchronization problem for a finite time horizon. We first formulate an Integer Linear Program (ILP) solution for the static digital twin synchronization problem when the problem size is small;otherwise, we develop a randomized approximation algorithm at the expense of bounded resource violations for it. We also devise a deterministic approximation algorithm with guaranteed performance for a special case of the static digital twin s

关键词： Aerial-vehicular networks   Aerial autonomous vehicles   Mobile edge computing   High-altitude platforms   Millimeter waves   Data-aware non-orthogonal multiple access  

摘要： In recent years, there has been a significant interest in ubiquitous coverage, high data rate connectivity, and mobile edge computing (MEC) as crucial services within the future sixth -generation (6G) wireless networks. These services are regarded as essential components, exemplifying the advancements anticipated in 6G technology. Nevertheless, the successful implementation of these services in MEC-enabled vehicular networks significantly relies on the availability of robust network coverage and telecommunication framework. Unfortunately, in far-flung and isolated regions, such framework is often lacking, posing significant challenges in achieving uninterrupted connectivity, comprehensive coverage, and efficient computation offloading. Targeting the aforementioned horizon, in this paper, an uplink non -orthogonal multiple access (NOMA)-MEC-enabled aerial -vehicular network operating at millimeter wave (mmWave) is proposed in which the ground vehicles are provided edge computing services by an aerial autonomous vehicle, i.e., a high -altitude platform (HAP). We present a system model in which a HAP equipped with MEC servers offers computation offloading capabilities to a vehicular network. Our main objective is to minimize the transaction time of a NOMA cluster offloading its data to MEC servers located at the HAP. We devise a dual -layer optimization scheme for optimizing the transmission power and computational resource allocation by using the Lagrange multipliers method and then attain convergence by implementing a sub -gradient approach. We further extend our work by proposing a dataaware NOMA clustering scheme. The simulation results demonstrate the efficacy of our proposed approach, showing a notable reduction in the transaction time in comparison to the baseline scheme. The optimal power allocation enhances the data rates which subsequently reduces the transmission time, and the optimal cores assignment effectively minimizes the computation time. Additionally,

关键词： Mobile edge computing  

摘要： Due to the limited resource capacity of edge servers and the high purchase costs of edge resources, service providers are facing the new challenge of how to take full advantage of the constrained edge resources for Internet of Things (IoT) service hosting and task scheduling to maximize system performance. In this paper, we study the joint optimization problem on service placement, resource provisioning, and workloads scheduling under resource and budget constraints, which is formulated as a mixed integer non-linear programming problem. Given that the frequent service placement and resource provisioning will significantly increase system configuration costs and instability, we propose a two-timescale framework for resource management and workloads scheduling, named RMWS. RMWS consists of a Gibbs sampling algorithm and an alternating minimization algorithm to determine the service placement and resource provisioning on large timescales. And a sub-gradient descent method has been designed to solve the workload scheduling challenge on small timescales. We conduct comprehensive experiments under different parameter settings. The RMWS consistently ensures a minimum 10% performance enhancement compared to other algorithms, showcasing its superiority. Theoretical proofs are also provided accordingly. Copyright © 2024, The Authors. All rights reserved.

关键词： Servers   Streaming media   Data models   Training   Federated learning   Backhaul networks   Adaptation models   Mobile edge computing   federated learning   reinforcement learning   adaptive bitrate streaming   content caching  

摘要： The explosive growth of mobile data traffic generated primarily from resource-hungry video streaming sessions challenges the service providers to deliver a better Quality of Service to the heterogeneous end-users. Content caching in Edge Computing is a promising solution to cope with this exponential rise in video traffic. The most popular videos are typically stored in the local caches of edge servers to provide fast and continuous access to videos. However, various edge caching strategies fail to cope with the dynamic request patterns of the users. Most learning-based caching models are generally trained in a centralized way, which overconsumes the network resources during training and transmission of video requests. Therefore, we propose a Federated Learning-based Reinforcement Learning caching framework called FedCache in this work. In FedCache, the training is decentralised on the end-user devices with its local data. The trained parameters from the end users are aggregated at the central server. A comprehensive set of experiments were performed, and FedCache outperformed existing state-of-the-art caching strategies concerning cache hit rate, access delay and backhaul traffic significantly.

关键词： Mobile edge computing  

摘要： Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Small cells with edge computing are densely deployed in 5G mobile networks to provide high throughput communication and low-latency computation. The flexibility of edge computation is empowered by the deployment of lightweight container-based microservices. In this paper, we take the first step toward optimizing the microservice management in small-cell networks. The prominent feature is that each microservice consists of multiple image layers and different microservices may share some basic layers, thus bringing deep coupling in their placement and service provision. Our objective is to minimize the expected total latency of microservice requests under the storage, communication and computing constraints of the sparsely interconnected small cell nodes. We formulate a binary quadratic program (BQP) with the multidimensional strategy of the image layer placement, the access selection and the task assignment. The BQP problem is then transformed into an ILP problem, and is solved by use of a novel sphere-box alternating direction multipliers method (ADMM) with reasonable complexity O(q4), where q is the number of variables in the transformed problem. Trace-driven experiments show that the gap between our proposed algorithm and the optimal is reduced by 35% compared with benchmark algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： In this paper, we study joint optimization of service placement, request routing, and CPU sizing in a cooperative mobile edge computing (MEC) system. The problem is considered from the perspective of the service provider (SP), which delivers heterogeneous MEC-enabled delay-sensitive services, and needs to pay for the used resources to the mobile network operators and the cloud provider, while earning revenue from the served requests. We formulate the problem of maximizing the SP’s total profit subject to the computation, storage, and communication constraints of each edge node and end-to-end delay requirements of the services as a mixed-integer non-convex optimization problem, and prove it to be NP-hard. To tackle the challenges in solving the problem, we first introduce a design trade-off parameter for different delay requirements of each service, which maintains flexibility in prioritizing them, and transform the original optimization problem by the new delay constraints. Then, by exploiting a hidden convexity, we reformulate these delay constraints into an equivalent form. Next, to handle the challenge of the complicating (integer) variables, using primal decomposition, we decompose the problem into an equivalent form of master and inner sub-problems over the mixed and real variables, respectively. We then employ a cutting-plane approach for building up adequate representations of the extremal value of the inner problem as a function of the complicating variables and the set of values of the complicating variables for which the inner problem is feasible. Finally, we propose a solution strategy based on generalized Benders decomposition and prove its convergence to the optimal solution within a limited number of iterations. Extensive simulation results demonstrate that the proposed scheme significantly outperforms the existing mechanisms in terms of various metrics, including the SP’s profit, cache hit ratio, running time, and end-to-end delay. Copyright © 2024, T