关键词： Internet of Things   mobile edge computing   mixed integer nonlinear program   deep reinforcement learning  

摘要： Internet of Things (IoT) has emerged as an enabling platform for smart cities. In this paper, the IoT devices' offloading decisions, CPU frequencies and transmit powers joint optimization problem is investigated for a multi-mobile edge computing (MEC) server and multi-IoT device cellular network. An optimization problem is formulated to minimize the weighted sum of the computing pressure on the primary MEC server (PMS), the sum of energy consumption of the network, and the task dropping cost. The formulated problem is a mixed integer nonlinear program (MINLP) problem, which is difficult to solve since it contains strongly coupled constraints and discrete integer variables. Taking the dynamic of the environment into account, a deep reinforcement learning (DRL)-based optimization algorithm is developed to solve the nonconvex problem. The simulation results demonstrate the correctness and the effectiveness of the proposed algorithm.

关键词： Task analysis   Servers   Resource management   Optimization   Radio links   Energy consumption   Computational modeling   Mobile edge computing   task offloading   resource allocation   end-to-end latency   task placement  

摘要： In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush-Kuhn-Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%.

关键词： Processor scheduling   Task analysis   Wireless communication   Dynamic scheduling   Heuristic algorithms   Resource management   Computational modeling   Mobile edge computing   computation offloading   reinforcement learning   resource allocation  

摘要： Mobile edge computing (MEC) has been envisioned as a promising paradigm that could effectively enhance the computational capacity of wireless user devices (WUDs) and quality of experience of mobile applications. One of the most crucial issues of MEC is computation offloading, which decides how to offload WUDs' tasks to edge severs for further intensive computation. Conventional mathematical programming-based offloading approaches could face troubles in dynamic MEC environments due to the time-varying channel conditions (caused primarily by WUD mobility). To address the problem, reinforcement learning (RL) based offloading approaches have been proposed, which develop offloading policies by mapping MEC states to offloading actions. However, these approaches could fail to converge in large-scale MEC due to the exponentially-growing state and action spaces. In this article, we propose a novel online computation offloading approach that could effectively reduce task latency and energy consumption in dynamic MEC with large-scale WUDs. First, a RL-based computation offloading and energy transmission algorithm is proposed to accelerate the learning process. Then, a joint optimization method is adopted to develop the allocating algorithm, which obtains near-optimal solutions for energy and computation resources allocation. Simulation results show that the proposed approach can converge efficiently and achieve significant performance improvements over baseline approaches.

关键词： satellite-terrestrial network   mobile edge computing   satellite-terrestrial cooperation   offloading decision   resource allocation  

摘要： With the development of satellite-terrestrial network (STN), mobile edge computing (MEC) servers are deployed at low orbit earth (LEO) satellites to provide computing services for user devices (UEs) in areas without terrestrial network coverage. There is symmetry between satellite networks and terrestrial networks, but there is asymmetry between their resources. Computing resources of satellites' MEC servers may not be enough. The satellite-terrestrial cooperation is promising, where a satellite migrates tasks to a base station (BS) in an adjacent area, thus utilizing computing resources of the BS's MEC server. Although there are some studies on computation offloading in STN, few studies consider a satellite as both a relay and a computing unit to assist UEs in computing tasks. This paper proposes a joint offloading decision and resource allocation scheme in MEC-enabled STN, which minimizes the completion delay of all UEs' indivisible tasks. Firstly, the optimization problem is formulated and decomposed. Then, the proposed scheme based on potential game and the Lagrange multiplier method makes UEs' task offloading decisions and allocates the satellite's and the BS's computing resources, thus obtaining the optimal solution through continuous iterations. Finally, the simulation results validate that the proposed scheme can obtain better gain than other baseline schemes.

关键词： Mobile edge computing   service migration   intelligent service migration algorithm   partially observable Markov decision process  

摘要： Mobile edge computing, enabling cloud computing capabilities at edge servers, is shown to be an effective way to enhance network performance as well as user experience. However, due to user mobility and limited coverage of a single edge server, service migration is inevitable to guarantee the quality of service (QoS). The service migration problem is to decide when, where and how to migrate the ongoing service from an edge server to another. In this paper, we formulate the problem as a partially observable markov decision process (POMDP) based on the fact that an edge server can obtain only partial user's information. A learning based intelligent service migration algorithm, named iSMA, is proposed to minimize the long-term average service delay of all users. iSMA consists of two function modules, a latent space model and a cross-entropy planning algorithm. The latent space model is used to infer the full state of the environment based on the partial information observed, and the cross-entropy planning algorithm is used to search the best service migration strategy. Numerical results show that our proposed iSMA reduces the service delay by about 28% and 24% when compared with two well-known deep learning based solutions.

关键词： Device to Device   Computation Offloading   IoT   Mobile Edge Computing   Wireless Sensor Networks  

摘要： In this paper, allocation memory algorithms (i.e. First Fit, Next Fit and Best Fit) are redesigned to offload the tasks of end devices in multi-device multi-task D2D communication. The proposed algorithms offload each task to single device, which enhance the performance by 4x at their maximum performance. To enhance the performance, the next fit algorithm is redesigned to offload the task to multiple devices, which enhances the performance to 88x at its maximum performance. The proposed algorithms are evaluated for different cell scenarios (i.e. femto, pico, micro and macro cells). Simulation results demonstrates that utilizing computation offloading minimizes the latency.

关键词： Mobile edge computing  

摘要： The development and popularization of mobile Internet and wireless communication technology have spawned a large number of computation-intensive and delay-intensive applications. Limited computing resources and existing technologies cannot meet the performance requirements of new applications. Mobile edge computing technology can use wireless communication technology to offload data to be stored and computing tasks to the nearby assistant or edge server with idle resources. Based on the data offloading of distributed wireless sensor device to device communication, the architecture is designed and the basic framework of distributed mobile edge computing is constructed. To solve the problem of high mobile cloud computing technology, the offloading model of optimized mobile edge computing was proposed, and the stability and convergence of the proposed algorithm were proved. Finally, the system performance of the proposed algorithm is verified by simulation. The results show that the proposed algorithm can converge within a finite number of steps. Compared with other benchmark schemes, the proposed algorithm has better performance in reducing system energy consumption, reducing moving edge response delay and system total delay.

关键词： Mobile edge computing   Computation offloading   Energy harvesting  

摘要： Mobile edge computing (MEC) has emerged for meeting the ever-increasing computation demands from mobile applications. Mobile users endowing with computation and energy harvesting (EH) capabilities, called EH devices, are desired in MEC systems. With computation capability, EH devices can switch between local computing and mobile-edge computing for better QoEs. With EH technologies, mobile users could survive perpetually for supporting long-term task processing. However, the heterogeneous computation resource among EH devices and edge servers, limited wireless resource as well as time-constrained harvestable energy challenge the computation offloading. This paper investigates a green MEC with EH devices and develops an effective EH computation offloading scheme towards minimizing delay for green MEC. We formulate and analyze the EH computation offloading problem with game theory. Then, an EH computation offloading game scheme, including the Lyapunov drift-based energy harvesting and computation offloading best response algorithms, is designed to find out optimal energy harvesting and computation offloading solutions to address the problem. The simulation results have been conducted to demonstrate the efficiency of the proposal.

关键词： Mobile edge computing  

摘要： This paper considers a practical mobile edge computing (MEC) system, where edge server does not preinstall the program required to perform user offloaded computing tasks. A partial program offloading (PPO) scheme is proposed, which can divide a user program into two parts, where the first part is executed by the user itself and the second part is transferred to an edge server for remote execution. However, the execution of the latter part requires the results of the previous part (called intermediate result) as the input. We aim to minimize the overall time consumption of a multi-server MEC system to complete all user offloaded tasks. It is modeled as a mixed integer nonlinear programming (MINLP) problem which considers user-and-server association, program partitioning, and communication resource allocation in a joint manner. An effective algorithm is developed to solve the problem by exploiting its structural features. First, the task completion time of a single server is minimized given the computing workload and available resource. Then, the working time of the edge servers are balanced by updating user-and-server association and communication resource allocation. Numerical results show that significant performance improvement can be achieved by the proposed scheme. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G   6G   Smart Internet of Vehicle   Mobile edge computing   Simultaneous wireless information and power transfer   Full-duplex   Multi-input and multi-output  

摘要： Smart Internet of Vehicles (IoV) as a promising application in Internet of Things emerges with the development of the fifth generation mobile communication (5G). Nevertheless, the heterogeneous requirements of sufficient battery capacity, powerful computing ability and energy efficiency for electric vehicles face great challenges due to the explosive data growth in 5G and the sixth generation of mobile communication (6G) networks. In order to alleviate the deficiencies mentioned above, this paper proposes a mobile edge computing (MEC) enabled IoV system, in which electric vehicle nodes upload and download data through an anchor node which is integrated with a MEC server. Meanwhile, the anchor node transmitters radio signal to electric vehicles with simultaneous wireless information and power transfer technology so as to compensate the battery limitation of eletric vehicles. Moreover, the spectrum efficiency is further improved by multi-input and multi-output and full-duplex technologies which is equipped at the anchor node. In consideration of the issues above, we maximize the average energy efficiency of electric vehicles by jointly optimize the CPU frequency, vehicle transmitting power, computing tasks and uplink rate. Since the problem is nonconvex, we propose a novel alternate interior-point iterative scheme (AIIS) under the constraints of computing tasks, energy consumption and time latency. Results and discussion section verifies the effectiveness of the proposed AIIS scheme comparing with the benchmark schemes.