关键词： Task analysis   Resource management   Microcell networks   Computational modeling   Servers   Interference   Reinforcement learning   Mobile edge computing   small cell networks   multi-agent deep reinforcement learning   computation offloading   interference coordination  

摘要： Integrating mobile edge computing (MEC) with small cell networks has been conceived as a promising solution to provide pervasive computing services. However, the interactions among small cells due to inter-cell interference, the diverse application-specific requirements, as well as the highly dynamic wireless environment make it challenging to design an optimal computation offloading scheme. In this paper, we focus on the joint design of computation offloading and interference coordination for edge intelligence empowered small cell networks. To this end, we propose a distributed multi-agent deep reinforcement learning (DRL) scheme with the objective of minimizing the overall energy consumption while ensuring the latency requirements. Specifically, we exploit the collaboration among small cell base station (SBS) agents to adaptively adjust their strategies, considering computation offloading, channel allocation, power control, and computation resource allocation. Further, to decrease the computation complexity and signaling overhead of the training process, we design a federated DRL scheme which only requires SBS agents to share their model parameters instead of local training data. Numerical results demonstrate that our proposed schemes can significantly reduce the energy consumption and effectively guarantee the latency requirements compared with the benchmark schemes.

关键词： Mobile edge computing   Cooperative computation offloading   Delay minimization   Resource allocation  

摘要： Mobile edge computing (MEC) is a promising paradigm, which brings computation resources in proximity to mobile devices and allows the tasks of mobile devices to be offloaded to MEC servers with low latency. The joint problem of cooperative computation task offloading and resource allocation is a challenging issue. The joint problem of cooperative computation task offloading scheme and resource assignment in MEC is investigated in this paper, where the vertical cooperation among mobile devices, mobile edge server nodes and mobile cloud server nodes is considered, and the horizontal computation cooperation between edge nodes is considered as well. A computation offloading decision, cooperative selection, power allocation and CPU cycle frequency assignment problem is formulated. The objective is to minimize the latency while guaranteeing the constraint of transmission power, energy consumption and CPU cycle frequency. The formulated latency optimization problem is a nonconvex mixed-integer problem in general, which has binary variables and continuous variables. In order to solve the formulated problem. A joint iterative algorithm based on the Lagrangian dual decomposition, ShengJin Formula method, and monotonic optimization method is proposed. The CPU cycle frequence allocation is handled by the ShengJin Formula method due to the cubic equation of one variable about the CPU frequence allocation. The transmission power assignment is handled by the monotonic optimization method. In the algorithm convergence with different number of tasks, the proposed algorithm can quickly and effectively reach the convergence state and getting the minimum task execution delay. Numerical results demonstrate that the proposed algorithm outperforms the Full MEC, Full Local and Full Cloud three schemes in terms of execution latency.

关键词： cost-performance tradeoff   edge server   mobile edge computing   queueing model   server configuration  

摘要： Before service providers build up an mobile edge computing (MEC) platform, an important issue that needs to be considered is the configuration of computing resources on edge servers. Since the computing resources on an edge server are limited compared with a cloud server and the service provider's deployment budget is limited, it would be unrealistic to equip all edge servers with abundant computing resources. In addition, the edge servers have different computation demands due to their different geographies. Therefore, this article investigates the problem of server configuration optimization in an MEC environment based on a given computation demand statistics of the selected deployment locations. Our strategy is to treat each edge server as an M/M/m queueing model, and then establish the performance and cost models for the system. Two optimization problems, including cost constrained performance optimization, and performance constrained cost optimization are formulated based on our models and solved by a series of fast numerical algorithms. We also conduct extensive numerical simulation examples to show the effectiveness of the proposed algorithms. MEC service providers can use our strategy to get the appropriate type of processor and obtain the optimal processor number for each edge server to achieve two different goals: (1) deliver the highest-quality services with a given cost constraint;(2) minimize the investment cost with a service-quality guarantee. Our research is of great significance for service providers to control the tradeoff between investment cost and service quality.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) deployed cloud computing resources (such as storage and computing power) to the edge of wireless access networks to better meet the development of 5G communication and computing-intensive applications. As the first step of MEC architecture deployment, the placement of the edge server (ES) is the foundation and key, and its location affects the user experience and system performance. In this article, we study the placement of ESs in the heterogeneous networks and express it as an optimization problem. Weighing the response delay and energy consumption as the task overhead, and place the ESs on the optimal access point (AP). An adaptive clustering algorithm MTO based on AP suitability evaluation is proposed to solve the optimal solution, which minimizes the task overhead of computing tasks. Extensive experimental simulations evaluate the performance of the algorithm, and the results show that the MTO algorithm is superior to other representative methods.

关键词： mobile edge computing   mobile edge server placement   multiagent RL   edge security  

摘要： Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.

关键词： Optimization   Task analysis   Trajectory   Energy consumption   Processor scheduling   Vehicle dynamics   Edge computing   Mobile edge computing   multi-UAVs   two-layer optimization   conflict elimination   decoupling  

摘要： In this study, we introduce a multi-unmanned aerial vehicle (multi-UAV) enabled mobile edge computing (MEC) system, with UAVs as the computing server for the task offloading of ground users. The energy consumption for ground users is minimized by jointly optimizing the UAV task scheduling, bit allocation, and UAV trajectory in a unified framework. To accomplish such goal, we propose a two-layer optimization strategy, where the upper layer optimizes the UAV task scheduling based on a dynamic programming-based bidding optimization method, while the lower one solves the bit allocation and UAV trajectory. In particular, the lower layer is decoupled into several subproblems to reduce the computational complexity, which can be easily solved using an alternating direction method of multipliers. However, the UAV trajectories optimized by solving the decoupled subproblems may lead to path conflicts. As such, we further propose a re-optimization strategy to eliminate such conflicts. Experimental results demonstrate that the proposed strategy achieves a favorable performance than those of greedy and random strategies in terms of total user energy consumption, the trajectory conflicts can be eliminated effectively, and the UAV trajectory can satisfy the safety constraints.

关键词： Task analysis   Servers   Delays   Cloud computing   Heuristic algorithms   Edge computing   Collaboration   Collaborative Data Caching   Multi-user Computation Offloading   Convex Optimization   Mobile Edge Computing  

摘要： Mobile edge computing (MEC) can use wireless access network (RAN) to provide the services required by user's information technology (IT) and cloud computing functions nearby, which can create a high-performance and low latency service environment. Performing task offloading and data caching at access points (APs) in a cooperative manner can reduce the heavy backhaul load and the retransmission of content downloading. However, in edge networks (ENs), how to maximize storage utilization while reducing service latency and energy consumption is still a key issue, because the heterogeneity of ENs and the uneven distribution of users make it difficult to determine which MEC server and what data should be cached. In this paper, we study a two-tier MEC system, which enables data caching and computing offloading policy to minimize the network cost at the user equipment (UE) side, while satisfying the constraints of task offloading deadline, the cache capacity at APs and the computing capability of MEC servers. The optimization problem is formulated as a mixed integer nonlinear program (MINLP) problem. In order to solve the problem, we transform it into an equivalent task offloading convex optimization problem by fixing an optimization variable. Furthermore, we solve a cache placement problem by dynamic programming (DP) algorithm. Then we propose a distributed collaborative data caching and computing offloading (CDCCO) iterative algorithm. Simulation results demonstrate that our proposed CDCCO algorithm can significantly reduce the network cost and achieve better performance than other existing schemes.

关键词： Mobile edge computing  

摘要： It has been tough for the sports industry and the athletes during the COVID-19 pandemic, but in Taiwan, we have been lucky to contain the pandemic to a level that we could hold the 2021 National Intercollegiate Athletic Games (NIAG) in early May. We formed a Sport Technology Team of more than 30 scholars, students, and engineers to provide novel systems and solutions that make the athletic games rich of sport technologies. Some of the features could be the first time shown to the internet audience for large-scale athletic games. The technologies involved include table tennis ball trajectory and bounce distribution, badminton shuttlecock tracking and trajectory, augmented-reality enriched content for real-time video streaming on social networks, real-time 3D broadcasting with wide-field free view-angle, in-stadium video stream pushing by private 5G network with Mobile Edge Computing (MEC), AI-based sport data analytics during live streaming, a Technology-Enhanced Broadcasting System for sport events, etc. This paper introduces the respective technologies that we have developed, deployed, and demonstrated in the 2021 NIAG, Taiwan. We stress the architecture design and integration of TBS, which is a System of Systems, as well as experimental results on real athletic games in the smart stadiums that we have established. © 2021, CC BY-NC-SA.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) emerges as a promising solution for servicing delay-sensitive tasks at the edge network. A body of recent literature started to focus on cost-efficient service placement and request scheduling. This work investigates the joint optimization of service placement and request scheduling in a dense MEC network, and develops an efficient online algorithm that achieves close-to-optimal performance. Our online algorithm consists of two basic modules: (1) a regularization with look-ahead approach from competitive online convex optimization, for decomposing the offline relaxed minimization problem into multiple sub-problems, each of which can be efficiently solved in each time slot;(2) a randomized rounding method to transform the fractional solution of offline relaxed problem into integer solution of the original minimization problem, guaranteeing a low competitive ratio. Both theoretical analysis and simulation studies corroborate the efficacy of our proposed online MEC optimization algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Delays   Unmanned aerial vehicles   Processor scheduling   Optimization   Games   Computational modeling   Minimization   UAV swarms   mobile edge computing   delay   scheduling   potential game  

摘要： Coalition-based unmanned aerial vehicle (UAV) swarms havebeen widelyused in urgent missions. To fasten the completion, mobile edge computing (MEC) has been introduced into UAV networks where coalition leaders act as servers to help members with data computing. This paper investigates a relative delay optimization in MEC-assisted UAV swarms. Considering that the scheduling methods have great impact on the delay, some theoretical analyses are made and a scheduling method based on the shortest effective job first (SEJF) is proposed. Based on the coupled relationship between scheduling and resource allocation, the computation offloading and channel access problems are then jointly optimized. To solve the problem in distributed UAV networks, the optimization problem is formulated as an offloading game. It is proved that the game is an exact potential game (EPG) and it has at least one pure strategy Nash Equilibrium (PNE). To reach the PNE, a distributed offloading algorithm based on concurrent best-better response (CBBR) is designed. Finally, the simulations show that the performance of the proposed CBBR algorithm is better than traditional algorithms. Compared with other scheduling methods, the proposed scheduling method based on SEJF reduces the delay by up to 30%.