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： Task offloading   Device to device communication   Mobile edge computing   Energy efficiency  

摘要： Device to device (D2D) communication and mobile edge computing (MEC) are two promising technologies in fifth generation (5G) cellular mobile communication. Besides MEC, a new task offloading technique attracts the attention as D2D collaboration. However, there is lack of integrated D2D and MEC framework to address the energy and delay costs in a joint approach. This work, proposes an energy efficient and delay-aware offloading scheme (EEDOS) based on D2D collaboration in MEC. In EEDOS, mobile devices can offload their task to the MEC or an idle mobile device in their proximity. The task execution and offloading to the MEC or an idle nearby device is formulated, and the optimization problem is defined. The whole process of allocating proper offloading destination is designed in the edge server. EEDOS, classifies offloading requests according to the deadline and energy constraint of requesting device. Then, it finds the proper offloading destination by utilising the maximum matching with minimum cost graph algorithm. Through simulation, we show that EEDOS achieves 95 percent of energy efficiency in comparison of no-offloading task execution and outperforms existing studies in term of energy efficiency with an improved delay in task execution. Moreover, EEDOS is capable of performing more successful task offloading and requires less edge server resources.

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： 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： sensor networks   internet of things   mobility sensor   mobile edge computing   service cache   mobility-aware   service response time  

摘要： The mobile edge computing architecture successfully solves the problem of high latency in cloud computing. However, current research focuses on computation offloading and lacks research on service caching issues. To solve the service caching problem, especially for scenarios with high mobility in the Sensor Networks environment, we study the mobility-aware service caching mechanism. Our goal is to maximize the number of users who are served by the local edge-cloud, and we need to make predictions about the user's target location to avoid invalid service requests. First, we propose an idealized geometric model to predict the target area of a user's movement. Since it is difficult to obtain all the data needed by the model in practical applications, we use frequent patterns to mine local moving track information. Then, by using the results of the trajectory data mining and the proposed geometric model, we make predictions about the user's target location. Based on the prediction result and existing service cache, the service request is forwarded to the appropriate base station through the service allocation algorithm. Finally, to be able to train and predict the most popular services online, we propose a service cache selection algorithm based on back-propagation (BP) neural network. The simulation experiments show that our service cache algorithm reduces the service response time by about 13.21% on average compared to other algorithms, and increases the local service proportion by about 15.19% on average compared to the algorithm without mobility prediction.

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.

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： Congestion control   initial congestion window   deep reinforcement learning   mobile edge computing   software-defined networking  

摘要： Mobile edge computing provides users with low response time and avoids unnecessary data transmission. Due to the deployment of 5G, the emerging edge systems can provide gigabit bandwidth. However, network protocols have not evolved together. In TCP, the initial congestion window (IW) is such a low value that most short flows still stay in slow start phase when finishing, and do not fully utilize available bandwidth. Naively increasing IW may result in congestion, which causes long latency. Moreover, since the network environment is dynamic, we have a challenging problem-how to adaptively adjust IW such that flow completion time is optimized, while congestion is minimized. In this paper, we propose an adaptive online decision method to solve the problem, which learns the best policy using deep reinforcement learning stably and fast. In addition, we propose an approach to further improve the performance by supervised learning, using data collected during online learning. We also propose to adopt SDN to address the challenges in implementing our method in MEC systems. To evaluate our method, we build an MEC simulator based on ns3. Our simulations demonstrate that our method performs better than existing methods. It can effectively reduce FCT with little congestion caused.

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.