关键词： Mobile edge computing   resource allocation   software defined cellular networks   task offloading   wireless networks  

摘要： The rapid growth of the internet usage and the distributed computing resources of edge devices create a necessity to have a reasonable controller to ensure efficient utilization of distributed computing resources in mobile edge computing (MEC). We envision the future MEC services, where quality of experience (QoE) of the services is further enhanced by software defined networks (SDNs) capabilities to reduce the application-level response time without service disruptions. SDN, which is not proposed specifically for edge computing, can in fact serve as an enabler to lower the complexity barriers involved and let the real potential of edge computing be achieved. In this paper, we investigate the task offloading and resource allocation problem in wireless MEC aiming to minimize the delay while saving the battery power of user device simultaneously. However, it is challenging to obtain an optimal policy in such a dynamic task offloading system. Learning from experience plays a vital role in time variant dynamic systems where reinforcement learning (RL) takes a long term goal into consideration besides immediate reward, which is very important for a dynamic environment. A novel software defined edge cloudlet (SDEC) based RL optimization framework is proposed to tackle the task offloading and resource allocation in wireless MEC. Specifically, Q-learning and cooperative Q-learning based reinforcement learning schemes are proposed for the intractable problem. Simulation results show that the proposed scheme achieves 31.39% and 62.10% reduction on the sum delay compared to other benchmark methods such as traditional Q-learning with a random algorithm and Q-learning with epsilon greedy.

关键词： mobile edge computing   cross-server computation offloading   energy consumption minimization   greedy algorithm  

摘要： As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computation-intensive applications and the resource-constrained mobile devices. However, most available studies on computation offloading in MEC assume that the edge severs host various applications and can cope with all kinds of computation tasks, ignoring limited computing resources and storage capacities of the MEC architecture. To make full use of the available resources deployed on the edge servers, in this paper, we study the cross-server computation offloading problem to realize the collaboration among multiple edge servers for multi-task mobile edge computing, and propose a greedy approximation algorithm as our solution to minimize the overall consumed energy. Numerical results validate that our proposed method can not only give near-optimal solutions with much higher computational efficiency, but also scale well with the growing number of mobile devices and tasks.

关键词： Mobile edge computing   multi-UAV   computation efficiency   trajectory scheduling  

摘要： The emergence of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) is of great significance for the prospective development of Internet of Things (IoT). The additional computation capability and extensive network coverage provide energy-limited smart mobile devices (SMDs) with more opportunities to experience diverse intelligent applications. In this paper, a computation efficiency maximization problem is formulated in a multi-UAV assisted MEC system, where both computation bits and energy consumption are considered. Based on the partial computation offloading mode, user association, allocation of central processing unit (CPU) cycle frequency, power and spectrum resources, as well as trajectory scheduling of UAVs are jointly optimized. Due to the non-convexity of the problem and the coupling among variables, we propose an iterative optimization algorithm with double-loop structure to find the optimal solution. Simulation results demonstrate that the proposed algorithm can obtain higher computation efficiency than baseline schemes while guaranteeing the quality of computation service.

关键词： Mobile edge computing   resource allocation   full offloading scheme   partial offloading scheme   CCCP  

摘要： Resource allocation algorithms are conceived for minimizing the energy consumption of multiuser mobile edge computing (MEC) systems operating in the face of interference channels, and where mobile users can offload their latency-sensitive tasks to the mobile edge server via a base station (BS). Latency-sensitive applications that benefit from MEC services can be divided into two major classes: 1) applications requiring uninterrupted execution and that cannot be fragmented and therefore require full offloading (FO);2) applications which can benefit from fractional or partial offloading (PO). For each class of applications, we first formulate a joint optimization problem where the aim is to minimize the overall energy consumption across the sub-network subject to latency, transmission quality, computational budget and transmit power constraints. The proposed optimization problems are nonconvex, tightly coupled, and consequently challenging to solve. By exploiting binary relaxation, smooth approximation and auxiliary variables, we convert these problems into more tractable forms and subsequently develop novel algorithms based on the concave-convex procedure (CCCP) to solve them. Furthermore, by incorporating a measure of user priority, a reduced-complexity solution is proposed for the FO scheme. The benefits of our energy-efficient resource allocation algorithms for latency-sensitive MEC are demonstrated through simulations.

关键词： mobile edge computing   offloading   genetic algorithm   resource allocation  

摘要： Mobile edge computing (MEC) can use a wireless access network to serve smart devices nearby so as to improve the service experience of users. In this paper, a joint optimization method based on the Genetic Algorithm (GA) for task offloading proportion, channel bandwidth, and mobile edge servers' (MES) computing resources is proposed in the scenario where some computing tasks can be partly offloaded to the MES. Under the limitation of wireless transmission resources and MESs' processing resources, GA was used to solve the optimization problem of minimizing user task completion time, and the optimal offloading task strategy and resource allocation scheme were obtained. The simulation results show that the proposed algorithm can effectively reduce the task completion time and ensure the fairness of users' completion times.

关键词： Computational modeling   Relays   Energy consumption   Servers   Mobile handsets   Wireless communication   Acceleration   Computation offloading   computing and relaying   mobile edge computing   P2P communication  

摘要： Besides increasing the computing capacity of edge devices, mobile edge computing (MEC) can also be utilized to help communication. This paper proposes an MEC-assisted computing and relaying scheme to enhance the throughput of uncompressed data for mobile peer-to-peer (P2P) communications. We assume that the target data has a dynamic compression rate during the transmission from one mobile device to another through a relay node with MEC. In order to obtain the optimal transmission and compression strategy for the mobile devices and the relay node, a cost function that defines the tradeoff between energy consumption and latency time is investigated first. Then a closed-form solution is derived by minimizing the cost function with respect to practical constraints. Compared with conventional P2P communications without MEC, the proposed model breaks the bottleneck of P2P communications by decoupling the data compression rates at the two sides of MEC server. Numerical results verify the effectiveness of the proposed scheme.

关键词： Servers   Software as a service   Task analysis   Analytical models   Performance evaluation   Queueing analysis   Edge computing   Mobile edge computing   queueing theory   performance evaluation  

摘要： Nowadays, the ever increasing demand of mobile computing applications has triggered the shift from the centralized mobile Cloud Computing toward mobile Edge Computing (MEC). The main feature of MEC is to move computing and storage to the network edges (e.g., radio access point/base stations), enabling resource-limited mobile devices to support computation-intensive and latency-critical applications. This paper deals with the performance evaluation of a MEC system providing computational capabilities to users within a limited area, according to the software as a service (SaaS) paradigm. In particular, a Markov multi-server queuing system model with requests reneging is proposed in order to accomplish the performance evaluation of the MEC system and derive the minimum number of processors to be allocated in order to fulfill specific service requirements in terms of resulting requests dropping probability. Finally, the pertinence of the proposed Markov queueing model is validated by comparing the obtained analytical predictions with numerical results derived by resorting to extensive computer simulations carried out under the assumption of realistic operating conditions.

关键词： VANETs   SDN-based handover   Mobile edge computing   Cellular   IEEE 802   11p hybrid vehicular networks  

摘要： With the arrival of 5G, the wireless network will be provided with abundant spectrum resources, massive data transmissions and low latency communications, which makes Vehicle-to-Everything applications possible. However, VANETs always accompany with frequent network topology changes due to the highly mobile feature of vehicles. As a result, the network performance will be affected by the frequent handover. In this paper, a seamless handover scheme is proposed where the Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are employed to adapt to the dynamic topology change in VANETs. The introduction of SDN provides a global view of network topology and centralized control, which enables a stable transmission layer connection when a handover takes place, so that the upper layer performance is not influenced by the network changes. By employing MEC server, the data are cached in advance before a handover happens, so that the vehicle can restore normal communication faster. In order to confirm the superiority of our proposal, computer simulations are conducted from different aspects. The results show that our proposal can significantly improve the network performance when a handover happens.

关键词： smart city   Internet of Things   mobile edge computing   potential game   Q-learning  

摘要： With the development of global urbanization, the Internet of Things (IoT) and smart cities are becoming hot research topics. As an emerging model, edge computing can play an important role in smart cities because of its low latency and good performance. IoT devices can reduce time consumption with the help of a mobile edge computing (MEC) server. However, if too many IoT devices simultaneously choose to offload the computation tasks to the MEC server via the limited wireless channel, it may lead to the channel congestion, thus increasing time overhead. Facing a large number of IoT devices in smart cities, the centralized resource allocation algorithm needs a lot of signaling exchange, resulting in low efficiency. To solve the problem, this paper studies the joint policy of communication and computing of IoT devices in edge computing through game theory, and proposes distributed Q-learning algorithms with two learning policies. Simulation results show that the algorithm can converge quickly with a balanced solution.

关键词： Task analysis   Resource management   Delays   Servers   Internet of Things   Energy consumption   Optimization   Internet of Things (IoT) networks   millimeter-wave (mmWave)   IoT   mobile edge computing   nonorthogonal multiple access (NOMA) technique   successive convex approximation (SCA)   ultradense (UD)  

摘要： This article considers a two-tier heterogeneous network consisting of conventional sub-6-GHz macrocells along with millimeter-wave (mmWave) small cells, where mobile devices (MDs) can connect to either macrocell or small cells opportunistically via the nonorthogonal multiple access (NOMA) protocol. We employ the queuing theory in our network model to conduct an assessment on the execution delay, energy consumption and the total cost of offloading tasks in a mobile-edge computation offloading (MECO) system. The main goal is to design an energy-efficient MECO decision algorithm in an ultradense Internet of Thing (UD-IoT) network to analyze the tradeoff between execution delay and energy consumption. The proposed scheme jointly optimizes the communication and computation resource management, subject to the energy and delay constraints. Due to the mixed-integer nonlinear problem (MINLP) for resource allocation and computation offloading, an iterative algorithm along with the successive convex approximation (SCA) is proposed to achieve the optimum local frequency scheduling, power allocation, and computation offloading. The superior performance of the proposed MECO algorithm in our UD-IoT network is verified by the extensive numerical results.