关键词： Mobile edge computing   network function virtualization   service function chaining   placement   linear programming  

摘要： Mobile Edge Computing (MEC) is a promising network architecture that pushes network control and mobile computing to the network edge. Recent studies propose to deploy MEC applications in the Network Function Virtualization (NFV) environment. The mobile network service in NFV is deployed as a Service Function Chaining (SFC). In the dynamic and resource-limited mobile network, SFC placement aiming at optimizing resource utilization is a challenging problem. In this article, we solve the SFC placement problem in the MEC-NFV environment. We formulate the SFC placement problem as a weighted graph matching problem, including two sub-problems: a graph matching problem and an SFC mapping problem. To efficiently solve the graph matching problem, we propose a Linear Programming-(LP) based approach to calculate the similarity between VNFs and physical nodes. Based on the similarity, we design a Hungarian-based algorithm to solve the SFC mapping problem. Evaluation results show that our proposed LP-based solutions outperform the heuristic algorithms in terms of execution time and resource utilization.

关键词： 5G   telemedicine   mobile edge computing   artificial intelligence   internet of things  

摘要： Telemedicine over Internet of Things (IoT) generates an unprecedented amount of data, which further requires transmission, analysis, and storage. Deploying cloud computing to handle data of this magnitude will introduce unacceptable data analysis latency and high storage costs. Thus, mobile edge computing (MEC) deployed between the cloud and users, which is close to the nodes of data generation, can tackle these problems in 5G scenarios with the help of artificial intelligence. This paper proposes a telemedicine system based on MEC and artificial intelligence for remote health monitoring and automatic disease diagnosis. The integration of different technologies such as computers, medicine, and telecommunications will significantly improve the efficiency of patient treatment and reduce the cost of health care.

关键词： Virtual networks   Name-object networking   Mobile edge computing   Network slicing  

摘要： Achieving advanced Mobile Edge Computing (MEC) services such as dynamic resource assignment and slicing, maintaining Quality of Service (QoS), and enabling heterogeneous virtual functions are some of the technical challenges associated with edge-cloud enhanced 5G architectures now under consideration. This paper proposes a named-object based virtual network (NOVN) architecture to support low-latency applications in the MEC. Software router implementation running on the ORBIT testbed validates the named-object approach, showing low VN processing and control overhead, and making it possible to achieve low latency. A latency performance improvement of 30% is achieved as compared to the baseline implementation without NOVN. The results also validate feasibility of using the advanced MEC services for an example latency constrained edge cloud scenario. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing  

摘要： In Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing (MEC) systems, UAVs can carry edge servers to help ground user equipment (UEs) offloading their computing tasks to the UAVs for execution. This paper aims to minimize the total time required for the UAVs to complete the offloaded tasks, while optimizing the three-dimensional (3D) deployment of UAVs, including their flying height and horizontal positions. Although the formulated optimization is a mixed integer nonlinear programmming, we convert it to a convex problem and develop a successive convex approximation (SCA) based algorithm to effectively solve it. The simulation results show that the joint optimization of the horizontal and the vertical position of a group of UAVs can achieve better performance than the traditional algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing   IoT   Blockchain   Bernoulli distribution convergence   Data management   Data processing  

摘要： With the creation of a mobile edge computing environment in which IoT technologies are converged on cloud services, the importance of high-capacity data processing technologies is increasing. In this paper, we propose a block chain-based convergence data management technique to efficiently handle different kinds of data processed in mobile edge computing environment. The proposed technique minimized data loss by connecting information from specific IoT devices to multiple hash boxes and adding electronic signatures to the first and last information. In addition, the similarity of IoT data is applied stochastically to respond flexibly to changes in the system. Since the proposed technique links IoT data with different probabilities, it has distributed and deployed IoT data ofnbits according to certain rules to reduce the load that can occur on the server. As a result of performance evaluation, the proposed technique has improved the conversion performance assessment of average transcoding propit because block password hash increases depending on AP density and scope when managing blockchain-based IoT data. In addition, the time to generate IoT data was improved because the cumulative use of transactions processed through similarity of blockchain-based IoT data was managed in a group of certain sizes to link the blocks consistently.

关键词： Mobile edge computing   Energy efficiency   User selection   Resource allocation   Convex optimization  

摘要： Mobile edge computing (MEC) as a new type of computing model can expand the computing power of cloud computing to the edge of radio access network (RAN), which brings a large number of applications close for end user. Compared to traditional cloud computing, computation tasks being offloaded to edge clouds nearby to execute can reduce transmission delay and energy consumption. However, how to select the best edge cloud in a dense cell to execute tasks remains challenging. To address this challenge, in this paper we propose joint user selection and resource allocation algorithm in MEC to maximize the user's energy efficiency, defined as the ratio of user throughput to its energy consumption. We formulate the energy efficiency maximization problem as a mixed integer fractional nonlinear optimization problem, which involves both users' offloading selection and uplink transmission power. To solve this non-convex optimization problem, we transform it into an equivalent subtractive convex optimization problem by relaxation transformation method, and furthermore provide the corresponding optimal solution of user selection and power allocation. Numerical results show that compared with other selection schemes, the proposed optimal scheme has a significant improvement in energy efficiency. (c) 2020 Elsevier B.V. All rights reserved.

关键词： mobile edge computing   physical-layer security   Lyapunov optimization   ergodic secrecy queue  

摘要： Mobile edge computing can provide powerful computation services around the ***,given the broadcast nature of wireless transmissions,offloading the computation tasks via the uplink channels would raise serious security *** paper proposes an online approach to jointly optimize local processing,transmit power,and task offloading decisions without the a-priori knowledge of the dynamic *** proposed approach can guarantee the secure offloading and asymptotically minimize the time-average energy consumption of devices while maintaining the stability of the ergodic secrecy queues and task *** exploiting the Lyapunov optimization,the local processing,transmit power,and task offloading variables can be decoupled between time *** subproblems on local processing and computation offloading can be solved *** optimization and graph matching can be used to solve the computation offloading *** show that the performances of the proposed approach are superior to other popular approaches.

关键词： Mobile edge computing   task offloading   payment determination   stable matching  

摘要： In mobile edge computing (MEC), it is challenging to offload tasks to appropriate edge nodes due to the heterogeneity in both tasks and edge nodes. Most existing task offloading mechanisms mainly aim at optimizing the global system performance, e.g., social welfare, while ignoring the personal preferences of the individual tasks and edge nodes. However, in an open MEC system, a task offloading decision is prone to be unstable if edge nodes or task owners have incentives to deviate from the decided allocation, and seek for alternative choices to improve their own utilities. In addition, to win the competition, task owners may gradually adjust their payments, which brings new challenge in achieving the stability of the system. To address the above issues, this paper constructs a distributed many-to-many matching model to capture the interaction between mobile tasks and edge nodes, with the consideration of their diverse resource requirements and availabilities. Based on this, we design both distributed and centralized stable matching based algorithms to jointly offload the tasks to edge nodes, and determine their payments. We prove that the proposed mechanisms achieve several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed schemes can get optimal social welfare, when the considered tasks are homogeneous in terms of their resource requirements. Finally, we conduct simulations to validate the effectiveness of the proposed work.

关键词： Prediction algorithms   Delays   Optimization   Cloud computing   Edge computing   5G mobile communication   Heuristic algorithms   Mobile edge computing   predictive service placement   two-timescale Lyapunov optimization  

摘要： Mobile edge computing (MEC) is emerging to support delay-sensitive 5G applications at the edge of mobile networks. When a user moves erratically among multiple MEC nodes, the challenge of how to dynamically migrate its service to maintain service performance (i.e., user-perceived latency) arises. However, frequent service migration can significantly increase operational cost, incurring the conflict between improving performance and reducing cost. To address these mis-aligned objectives, this paper studies the performance optimization of mobile edge service placement under the constraint of long-term cost budget. It is challenging because the budget involves the future uncertain information (e.g., user mobility). To overcome this difficulty, we devote to leveraging the power of prediction and advocate predictive service placement with predicted near-future information. By using two-timescale Lyapunov optimization method, we propose a T-slot predictive service placement (PSP) algorithm to incorporate the prediction of user mobility based on a frame-based design. We characterize the performance bounds of PSP in terms of cost-delay trade-off theoretically. Furthermore, we propose a new weight adjustment scheme for the queue in each frame named PSP-WU to exploit the historical queue information, which greatly reduces the length of queue while improving the quality of user-perceived latency. Rigorous theoretical analysis and extensive evaluations using realistic data traces demonstrate the superior performance of the proposed predictive schemes.

关键词： Trajectory   Data compression   Global Positioning System   Data centers   Encoding   Roads   Vehicles   Huffman forest   huffman tree   mobile edge computing   time-varying velocity   trajectory data compression  

摘要： An increasing number of vehicles are now equipped with GPS devices to facilitate fleet management and send their GPS locations continuously, generating a huge volume of trajectory data. Sending and storing such vehicle trajectory data cause sustainable communication and storage overheads. Trajectory data compression becomes a promising way to alleviate overhead issues. However, previous solutions are commonly carried out at the side of the data center after data having been received, thus saving the storage cost only. Here, we bring the idea of mobile edge computing and transfer the computation-intensive data compression task to the mobile devices of drivers. As a result, the trajectory data is reduced at the side of data generators before being sent out;thus, it can lower data communication and storage costs simultaneously. We propose DAVT, an error-bounded trajectory data representation, and a compression framework. Specifically, the trajectory data is reformatted into three parts (i.e., Distance, Acceleration & Velocity, and Time), and three compressors are wisely devised to compress each part. For D and AV parts, a similar Huffman tree-forest structure is exploited to encode data elements effectively, but with quite different rationales. For the T part, the large absolute timestamps are transformed to small time intervals firstly, and different encoding techniques are adopted based on the data quality. We evaluate our proposed system using a large-scale taxi trajectory dataset collected from the city of Beijing, China. Our results show that our compressors outperform other baselines.