关键词： Computation offloading   Convex optimization   Deep reinforcement learning   Mobile edge computing   Unmanned aerial vehicle  

摘要： With the increasing growth of internet-of-things (IoT) devices, effective computation performance has become a critical issue. Many services provided by IoT devices (e.g., augmented reality, location-tracking, traffic systems, and autonomous driving) require intensive real-time data processing, which demands powerful computational resources. Mobile edge computing (MEC) has been introduced to effectively handle this problem reliably over the internet. The inclusion of a MEC server allows computationally intensive tasks to be offloaded from IoT devices. However, communication overhead and delays are major drawbacks. With the advantages of high mobility and low cost, unmanned aerial vehicles (UAVs) can mitigate this issue by acting as MEC servers. The offloading decisions for such scenarios involve service latency, energy/power consumption, and execution delays. For this reason, this study reviews UAV-enabled MEC solutions in which offloading was the focus of research. We compare the algorithms qualitatively to assess features and performance. Finally, we discuss open issues and research challenges in terms of design and implementation.

关键词： Streaming media   Quality of experience   Resource management   Wireless communication   Bit rate   Transcoding   Bandwidth   Adaptive video streaming   mobile edge computing   resource allocation   wireless networks   online optimization  

摘要： The emerging mobile edge computing (MEC) technology has been recently applied to improve adaptive bitrate (ABR) streaming service quality under time-varying wireless channels. In this paper, we consider a heterogeneous multi-user MEC-enabled video streaming network with time-varying wireless channels in sequential time frames. In particular, we aim to design an online joint transcoding and transmission resource allocation algorithm to maximize the ABR streaming user's quality of experience (QoE) subject to the bandwidth and CPU constraints. The algorithm is "online" in the sense that the bitrate and resource allocation decisions made at each frame depend only on the observation of past events. We formulate the problem as a mixed integer non-linear programming (MINLP) that jointly determines bitrate adaptation, bandwidth allocation, and CPU cycle assignment. To cope with the challenge arising from the coupling decisions of adjacent frames, we propose a low-complexity online algorithm, named OCCA. Specifically, by introducing queueing model constraints, we transform the offline non-conex MINLP problem into a multi-frame problem. Then, we analytically decouple the multi-stage(frame) problem to multiple per-frame convex subproblems that can be solved with high robustness and low computational complexity. We perform simulations with realistic scenarios to evaluate the performance of the proposed algorithm. Results manifest that compared with state-of-the-art approaches, our proposed algorithm can provide 97.84% (on average) extra QoE.

关键词： blockchain   mobile edge computing   computation offloading   deep deterministic policy gradient (DDPG)  

摘要： A mobile edge computing (MEC)-enabled blockchain system is proposed in this study for secure data storage and sharing in internet of things (IoT) networks, with the MEC acting as an overlay system to provide dynamic computation offloading services. Considering latency-critical, resource-limited, and dynamic IoT scenarios, an adaptive system resource allocation and computation offloading scheme is designed to optimize the scalability performance for MEC-enabled blockchain systems, wherein the scalability is quantified as MEC computational efficiency and blockchain system throughput. Specifically, we jointly optimize the computation offloading policy and block generation strategy to maximize the scalability of MEC-enabled blockchain systems and meanwhile guarantee data security and system efficiency. In contrast to existing works that ignore frequent user movement and dynamic task requirements in IoT networks, the joint performance optimization scheme is formulated as a Markov decision process (MDP). Furthermore, we design a deep deterministic policy gradient (DDPG)-based algorithm to solve the MDP problem and define the multiple and variable number of consecutive time slots as a decision epoch to conduct model training. Specifically, DDPG can solve an MDP problem with a continuous action space and it only requires a straightforward actor-critic architecture, making it suitable for tackling the dynamics and complexity of the MEC-enabled blockchain system. As demonstrated by simulations, the proposed scheme can achieve performance improvements over the deep Q network (DQN)-based scheme and some other greedy schemes in terms of long-term transactional throughput.

关键词： mobile edge computing   online resource allocation   task arrival prediction   long short-term memory   Lyapunov optimization  

摘要： MEC servers (MESs) support multiple queues to accommodate the delay requirements of tasks offloaded from end devices or transferred from other MESs. The service time assigned to each queue trades off the queue backlog and energy consumption. Because multiple queues share the computational resources of a MES, optimally scheduling the service time among them is important, reducing the energy consumption of a MES and ensuring the delay requirement of each queue. To achieve a balance between these metrics, we propose an online service-time allocation method that minimizes the average energy consumption and satisfies the average queue backlog constraint. We employ the Lyapunov optimization framework to transform the time-averaged optimization problem into a per-time-slot optimization problem and devise an online service-time allocation method whose time complexity is linear to the number of queues. This method determines the service time for each queue at the beginning of each time slot using the observed queue length and expected workload. We adopt a long short-term memory (LSTM) deep learning model to predict the workload that will be imposed on each queue during a time slot. Using simulation studies, we verify that the proposed method strikes a better balance between energy consumption and queuing delay than conventional methods.

关键词： multimedia applications   resource allocation   mobile edge computing  

摘要： Due to the advancements of information technologies and the Internet of Things (IoT), the number of distributed sensors and IoT devices in the social IoT (SIoT) systems is proliferating. This has led to various multimedia applications, face recognition and augmented reality (AR). These applications are computation-intensive and delay-sensitive and have become popular in our daily life. However, IoT devices are well-known for their constrained computational resources, which hinders the execution of these applications. Mobile edge computing (MEC) has appeared and been deemed a prospective paradigm to solve this issue. Migrating the applications of IoT devices to be executed in the edge cloud can not only provide computational resources to process these applications but also lower the transmission latency between the IoT devices and the edge cloud. In this paper, computation resource allocation and multimedia applications offloading in MEC-assisted SIoT systems are investigated. We aim to optimize the resource allocation and application offloading by jointly minimizing the execution latency of multimedia applications and the consumed energy of IoT devices. The studied problem is a formulation of the total computation overhead minimization problem by optimizing the computational resources in the edge servers. Besides, as the technology of dynamic voltage scaling (DVS) can offer more flexibility for the MEC system design, we incorporate it into the application offloading. Since the studied problem is a mixed-integer nonlinear programming (MINP) problem, an efficient method is proposed to address it. By comparing with the baseline schemes, the theoretic analysis and simulation results demonstrate that the proposed multimedia applications offloading method can improve the performances of MEC-assisted SIoT systems for the most part.

关键词： Mobile edge computing   Quality of service   Multi-layer optimization   Data caching   Resource allocation  

摘要： Mobile edge computing (MEC) is a key feature of next generation heterogeneous networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. In this research we investigated on connection management in multi-access edge computing systems using intelligent offloading and caching strategy. This paper presents joint QoS-aware resource allocation and mobile edge computing in multi-access heterogeneous networks in order to maximize total system's energy efficiency in addition to guaranty the users QoS requirements. Firstly, based on the multi-server MEC scenario, a new goal function is designed considering calculation and communication models, in order to decreasing the completion time of all computing tasks and achieving optimal energy efficiency under delay constraints. Then, the continues carrier allocation and user association variables in addition to the interference coordination incorporated in the goal function, modifies the primary optimization problem to a mixed integer nonlinear programming (MINLP). Also, considering user's minimum data rate and maximum transmission power constraints, a carrier-matching algorithm is introduced to obtain the optimal channel allocation strategy, which first matches the user with the channel and subsequently, the Dinkelbach-like method is applied to obtain the optimal resource allocation. Based on the simulations, the proposed approach not only achieves higher energy efficiency but also enhances the total network throughput in multiple-sources scenarios.

关键词： Servers   Task analysis   Pricing   Games   Energy consumption   Resource management   Dynamic scheduling   Mobile edge computing   computation offloading   resources pricing   Stackelberg game  

摘要： Owing to the limited computing resources at the MEC server, reasonable strategies of resources pricing and task offloading are necessary to be designed. In this letter, a scenario of multi-users and single MEC server with limited computing resources is studied, where each end-user has a divisible task which could be offloaded portion to the MEC server for execution, both local and offloaded portions of a task cannot be instantiated for execution at the same time, and a stackelberg game based pricing and offloading is investigated. The game is established based on the relationship between the MEC server resources pricing and task offloading quantities, and the existence of the stackelberg equilibrium is proved. Differential evolution algorithm is then used to find the best strategies of the resources pricing for the MEC server and task offloading quantities for end-users. Simulation results show that the investigations can improve the profit of the MEC server and the utility of end-users, which result in the win-win situation.

关键词： Mobile edge computing  

摘要： Recently, Collaborative Edge Computing (CEC) has been proposed as an effective method to improve the performance of Mobile Edge Computing (MEC) systems by offloading workload from busy MEC servers to idle ones. Although extensive researches have been conducted to study the system design and scheduling algorithms in CEC, the incentive mechanism between end-users and collaborative servers yet receives much less attention. To fill the gap, in this paper, we propose an online auction-based incentive mechanism where the task requests arrive stochastically and MEC servers need to decide whether to accept them without future information. To guarantee the applicability of our mechanism, we consider a comprehensive model where both the valuation of tasks and operation costs of edge servers are represented by general functions. Through theoretical analysis, we formally proved our mechanism achieves desirable properties, such as truthfulness and polynomial time complexity. Based on the primal-dual optimization framework, we further demonstrate the competitive ratio of our mechanism with respect to the optimal offline social welfare. Extensive simulations are conducted to verify our theoretical analysis and validate the effectiveness of our mechanism. © 2022, The Authors. All rights reserved.

关键词： Mobile edge computing   Multicarrier non-orthogonal multiple access   Resource block assignment   Deep reinforcement learning  

摘要： Future network services are emerging with an inevitable need for high wireless capacity along with strong computational capabilities, stringent latency and reduced energy consumption Two technologies are promising, showing potential to support these requirements: multi-access (or mobile) edge computing (MEC) and non orthogonal multiple access (NOMA). While MEC allows users to access the abundant computing resources at the edge of the network, NOMA technology enables an increase in the density of a cellular network. However, integrating NOMA technology into MEC systems faces challenges in terms of joint offloading decisions (remote or local computation) and inter-user interference management. In this paper, with the objective of maximizing the system-wide sum computation rate under latency and energy consumption constraints, we propose a two stage deep reinforcement learning algorithm to solve the joint problem in a multicarrier NOMA-based MEC system, in which the first-stage agent handles offloading decisions while the second-stage agent considers the offloading decisions to determine the resource block assignments for users. Simulation results show that compared with other benchmark algorithms, the proposed algorithm improves the sum computation rate while meeting the latency and energy consumption requirements, and it outperforms the approach in which a single agent handles both offloading decisions and resource block assignments due to faster convergence performance.

关键词： Mobile edge computing   Calculation task allocation   Unmanned aerial vehicle communications  

摘要： Owing to the high economic benefits, flexible deployment, and controllable maneuverability, unmanned aerial vehicles (UAVs) have been envisioned as promising and potential technologies for dispensing wireless communication services. This paper investigates a mobile edge computing (MEC) system assisted by multiple access points (APs) and an UAV, in which APs may not be able to straightly establish wireless communications with terrestrial Internet of Thing devices (IoT) due to ground signal blockage. Consequently, an UAV is dispatched as a mobile AP to serve a group of users and render the air-to-ground channel. In this scenario, we contemplate dividing the computing tasks of IoTDs into three parts: either be calculated locally, or offloaded to the UAV for processing, or accomplished on AP through relaying. This work attempts to minimize the weighted sum of communication consumption, calculation consumption, and the UAV's flight consumption over a finite UAV mission duration by jointly optimizing calculation task allocation ratio, power distribution as well as the UAV's trajectory. However, the resulting problem we put forward is demonstrated to be highly non-convex and challenging to solve. To tackle this issue, we decompose the original problem into two sub-problems hinging on the block coordinate descent (BCD) method. We settle the two sub-problems iteratively through the Lagrangian duality method and succession convex approximation (SCA) technique. The simulation results further reveal that the proposed approach is superior to other comparison baselines.