关键词： Average power consumption   average response time   computation offloading   cost-performance ratio   mobile edge computing   Nash equilibrium   non-cooperative game   queueing model  

摘要： There are two fundamental purposes in mobile edge computing, i.e., performance enhancement and cost reduction. By offloading computation tasks to a mobile edge cloud (MEC), a user equipment (UE), also called mobile user, mobile subscriber, or mobile device, can possibly reduce its average response time, which is the main performance measure, and can possibly reduce its average power consumption. Optimizing both performance and cost may be conflicting requirements. In this paper, we optimize the cost performance ratio (CPR), i.e., the power-time product, which combines performance (average response time) and cost (average power consumption) into one quantity. A unique feature in mobile edge computing is the competitiveness of mobile users, who are selfish in competing for resources in a mobile edge cloud. We take a game theoretic approach to the stabilization of a competitive mobile edge computing environment. The main contributions of the paper are summarized as follows. 1) We consider a mobile edge computing environment with multiple UEs and a single MEC. We establish an M/G/1 queueing model for the UEs and an M/G/m queueing model for the MEC. The UEs are entirely heterogeneous in terms of task characteristics, computation and communication speeds, and power consumption models for both computation and communication. 2) We analytically derive the average response time and the average power consumption of each UE and the MEC, so that cost-performance ratio optimization can be studied mathematically and rigorously. 3) We establish a non-cooperative game framework to systematically study the stabilization of a competitive mobile edge computing environment. Our framework includes a set of seven non-cooperative games among the UEs and the MEC, each attempts to minimize its payoff function, i.e., its cost-performance ratio. These games are different in terms of the number of variables to play and which variables to play. 4) We develop efficient algorithms for each player

关键词： 5G   mobile edge computing   ultra-reliable low latency communications   augmented reality   code-partitioning offloading  

摘要： Augmented reality (AR) is one of the emerging use cases relying on ultra-reliable and low-latency communications (uRLLC). The AR service is composed of multiple dependent computational-intensive components. Due to the limited capability of user equipment (UE), it is difficult to meet the stringent latency and reliability requirements of AR service merely by local processing. To solve the problem, it is viable to offload parts of the AR task to the network edge, i.e., mobile edge computing (MEC), which is expected to extend the computing capability of the UE. However, MEC also incurs extra communication latency and errors on the wireless channel;therefore, it is challenging to make an optimum offload decision. So far, a little of state-of-the-art work has considered both the latency and reliability of the MEC-enabled AR service. In this paper, we study the scenario multiple edge nodes cooperate to complete the AR task. The dependency of task components is modeled by a directed acyclic graph through code partitioning. We aim to minimize the service failure probability (SFP) of the MEC-enabled AR service considering reliability and latency. We design an integer particle swarm optimization (IPSO)-based algorithm. Although the solution of IPSO-based algorithm approaches the optimum of the problem, it is infeasible to use IPSO for real-time AR services in practice due to the relatively high computational complexity. Hence, we propose a heuristic algorithm, which achieves a performance close to that of the IPSO-based algorithm with much lower complexity. Compared with state-of-the-art work, the heuristic algorithm can significantly improve the probability to fulfill the targeted SFP in various network conditions. Due to the generic characteristics, the proposed heuristic algorithm is applicable for AR services, as well as for many other use cases in uRLLC.

关键词： Internet of Things   latency tolerable data   mobile edge computing   task offloading   data transfer  

摘要： With the emergence of in-depth research of emerging technologies or 5G mobile communication technology methods, the IoT applications have been further sublimated. In this paper, the new characteristics and new challenges appearing in the current mobile edge computing are sorted out, and the latest related models and work are summarized. The important optimization models and moving models and wireless block data in mobile edge computing are analyzed and discussed. On this basis, this paper mainly designs and verifies the following three aspects of mobile edge computing. A joint optimization model of task offloading and power allocation is established, and a centralized joint optimization algorithm for task unloading and power allocation is proposed. Based on the equalization delay and the impact of energy consumption on task unloading, the algorithm can use the idle resources that can be used to distribute and unload the computing tasks. The simulation experiments show that the algorithm can not only coordinate task offloading and power allocation effectively, but also improve the balance between system delay and energy consumption. Delay-tolerable data can be modeled as a partially observable Markov decision process in a software-defined transport and compute node selection process. Compared with the existing scheme, the proposed method can effectively reduce system overhead, shorten data calculation execution time, improve data calculation efficiency, and ensure that the delay can tolerate data transmission arrival rate under the condition of transmission delay.

关键词： Vehicular heterogeneous network   mobile edge computing   multipath transmission   work-load allocation  

摘要： With the rapid development of intelligent transportation, various applications which have millisecond delay requirements appear in the vehicular heterogeneous network. Offloading these delay-sensitive applications into edge nodes is a trend and direction of development. However, with the increase in the number of vehicular applications, the conventional methods of distance-based edge node workload allocation make the workload allocation unbalanced, causing some edge nodes to be overloaded, resulting in response time of corresponding application being too long to meet low delay requirements. In this paper, we tackle the problem of edge node overload and propose a multipath transmission workload balancing optimization scheme, which uses multipath transmission in the edge computing architecture as the transport protocol support for the communications between the vehicles and the edge nodes and the real-time virtual machine (VM) migration happens between the edge nodes. First, the application is assigned to the edge node closest to each vehicle. When the workload of the edge node exceeds its capacity, the scheme iteratively selects the application with the longest response time. Second. if its response time exceeds the response time passed to the cloud computing center, it is assigned to the cloud computing center for processing;if not, it is reassigned to the standby edge to minimize its response time until all vehicle applications cannot find a better edge node. Then, computing resources are allocated to each edge node, and resources of different sizes are allocated to different types of VMs in each edge node through convex optimization. Finally, the extensive simulation results illustrate that the multipath transmission workload balancing optimization scheme can effectively reduce the average response time of the vehicular applications compared to existing schemes.

关键词： Task analysis   Trajectory   Unmanned aerial vehicles   Three-dimensional displays   Wireless communication   Energy consumption   Servers   Unmanned aerial vehicle   Internet of Thing   mobile edge computing   3D trajectory design   cache deployment  

摘要： This paper studies an unmanned aerial vehicle (UAV)-enabled mobile edge network for Cyber-Physical System (CPS), where UAV with fixed-wing or rotary-wing is dispatched to provide communication and mobile edge computing (MEC) services to ground terminals (GTs). To minimize the energy consumption so as to extend the endurance of the UAV, we intend to jointly optimize its 3D trajectory and the task-cache strategies among GTs to save the energies spent on flight propulsion and GT tasks. Such joint trajectory-task-cache problem is difficult to be optimally solved, as it is non-convex and involves multiple constraints. To tackle this problem, we reformulate the optimizing of task offloading and cache into two tractable linear program (LP) problems, and the optimizing of UAV trajectory into three convex Quadratically Constrained Quadratically Program (QCQP) problems on horizontal trajectory, vertical trajectory and flight time of the UAV respectively. Then a block coordinate descent algorithm is proposed to iteratively solve the formed sub-problems through a successive convex optimization (SCO) process. A high-quality sub-optimal solution to the joint problem then will be obtained, after the algorithm converging to a prescribed accuracy. The numerical results show the proposed solution significantly outperforms the baseline solution.

关键词： Mobile edge computing   migration strategy   quality of services   vehicle to network services  

摘要： In 5G mobile networks, the convergence of cloud computing and communication leads to mobile edge computing, benefiting vehicular networks. However, the advent of a wide variety of new services and devices has changed the vehicular network landscape, challenging vehicle-to-network services' migrations. In this paper, we focus on optimizing long-term average latency of multiple services with a different quality of services (QoS). We first introduce an offline algorithm, which can be used to find the optimal migration strategy of services. Then, we analyze the negative effect of trajectory prediction and suggest an optimizing method to reduce this effect by partial updating. Finally, based on this method, we propose a partial dynamic optimization algorithm to approximate the optimal solution, by integrating the priority queue, which utilizes QoS information. We simulate the average service latency, confirming that the proposed partial dynamic optimization algorithm keeps a stable service latency and performs better than other existing algorithms, considering the negative effect of trajectory prediction. We also verify that the proposed algorithm can meet the low latency requirement of the vehicles and the different demands of different services. Besides, the partial dynamic optimization algorithm has a lower time complexity.

关键词： Mobile edge computing   Offloading availability   Offloading decisions   Task scheduling   Multi-objective optimization  

摘要： Deploying cloud service at base stations (Cloudlets, considered as Mobile Edge Computing) or sharing resources between mobile devices (Mobile Cloudlets) are expected to be enablers of Edge Computing or Fog computing. Different Mobile Edge Computing systems have been proposed based on hierarchical cloud architecture. However, the dynamics and diversity of mobile cloudlets makes it challenging to offload tasks to optimal nodes (process locally, or offload to a device in Mobile Cloudlets or Cloudlets). In this paper, offloading availability is formulated by considering user mobility similarity and the residual energy of mobile devices. To guarantee offloading availability and minimize energy consumption as well as data size transmitted through the cellular access links, offloading decisions for multi-tasks (task scheduling problem) are formulated to be a multi-objective optimization problem and we develop a centralized algorithm TS-SMOSA such that efficient-energy offloading decisions are made and tasks can be allocated to optimal nodes. Finally, experimental results are promising and show near optimal solutions for all of our studied scenario.

关键词： Mobile edge computing   cognitive radio   imperfect spectrum sensing   wireless power transfer   semi-closed form  

摘要： Most mobile edge computing (MEC) works assume that the mobile devices (MDs) can offload their tasks to the MEC-severs at anytime, which may not be a practical assumption due to the tension between a large number of MDs and the scarce spectrum resources. In this paper, a framework for wireless powered cognitive radio (CR)-based MEC-enabled networks is proposed, which integrates three technologies: MEC, CR, and wireless power transfer (WPT). CR technology with imperfect spectrum sensing is adopted by the MD to find the spectrum access opportunities. An optimization problem is formulated to maximize the average calculated number of bits (CNoB) of the MD, which is non-convex and hard to solve. A two-loop procedure using a one-dimensional line search method is proposed. The time for spectrum sensing, WPT, energy harvesting (EH) and offloading, the central processing unit (CPU) frequency, and the transmit power of the MD are jointly optimized. Some semi-closed form solutions are obtained through Lagrangian dual decomposition and successive pseudo-convex approximation (SPCA) methods. Simulation results are presented to show the effectiveness of the proposed CR-based MEC scheme with different parameters.

关键词： Content delivery network   mobile edge computing   optimal caching strategy   zone-based cooperative content caching  

摘要： Mobile edge computing (MEC) is a promising solution to meet the latency requirement for delay-sensitive services in a 5G radio access network (RAN). Its key idea is to deploy computing and storage capacities at the edge of the RAN to quickly provision content and processing capacities as required by the users. Efficient content caching and delivery are key issues to ensure the success of this technique. This paper proposes a zone-based cooperative content caching and delivery scheme for a RAN supporting MEC (MEC-RAN), where the RAN is modelled as a zone and is further sub-divided into multiple sub-zones. Content items are cooperatively cached and delivered among multiple sub-zones. The caching problem is formulated as a mixed integer linear programming model. We also develop a heuristic cooperative content caching strategy to decide the content items to be cached in each MEC server. This novel strategy divides the storage space in each MEC server into two parts. The firrst part caches locally popular contents and the second part is used to cooperatively cache zone-wide popular items. We study the proposed scheme both through simulations and implementation on a testbed that consists of a subnetwork on our campus and commercial cloud service from Ali-Cloud. Both of these show that the proposed zone-based scheme performs better than other typical caching strategies in terms of average content delivery latency and balanced loading of the MEC servers.

关键词： Mobile edge computing   intelligent and connected vehicle   upper confidence bound algorithm   Markov model   Q-learning  

摘要： With the rise of intelligent and connected vehicles (ICVs), new vehicle applications continue to emerge, while the computing capability of vehicles remains limited. Mobile edge computing (MEC) is considered to be the most effective technique for mitigating vehicle computing pressure, with computation offloading being a key technology for MEC. To solve the problem of excessive task processing delay and energy consumption due to the vehicle-limited computing power in the vehicular network, we consider the tasks and the characteristics of MEC, and divide the tasks into indivisible tasks and divisible tasks according to the size of data (that is, whether it affects functionality after segmentation). Then, two computation offloading algorithms are proposed named binary offloading and partial offloading separately. The binary offloading unloads the task to the mobile edge computing server as a whole and selects only an optimal offloading site;thus, an improved upper confidence bound algorithm is adopted. The partial offloading divides the complex tasks with large data volumes through time slots processed by different MEC servers, and uses the Q-learning algorithm to find the most effective offloading strategy. The simulation results show that the total cost of delay and energy consumption of the binary offloading algorithm is lower when processing computationally intensive tasks. When addressing divisible and complex tasks, the partial offloading algorithm improves the real-time performance of the tasks significantly and conserves the energy of the vehicle terminal.