关键词： Mobile edge computing  

摘要： Recently, in order to extend the computation capability of smart mobile devices (SMDs) and reduce the task execution delay, mobile edge computing (MEC) has attracted considerable attention. In this paper, a stochastic optimization problem is formulated to maximize the system utility and ensure the queue stability, which subjects to the power, subcarrier, SMDs, and MEC server computation resource constraints by jointly optimizing congestion control and resource allocation. With the help of the Lyapunov optimization method, the primal problem is transformed into five subproblems including the system utility maximization subproblem, SMD congestion control subproblem, SMD computation resource allocation subproblem, joint power and subcarrier allocation subproblem, and MEC server scheduling subproblem. Since the first three subproblems are all single variable problems, the solutions can be obtained directly. The joint power and subcarrier allocation subproblem can be efficiently solved by utilizing alternating and time-sharing methods. For the MEC server scheduling subproblem, an efficient algorithm is proposed to solve it. By solving the five subproblems at each slot, we propose a delay-aware task congestion control and resource allocation (DTCCRA) algorithm to solve the primal problem. Theoretical analysis shows that the proposed DTCCRA algorithm can achieve the system utility and execution delay trade-off. Compared with the intelligent heuristic (IH) algorithm, when the control parameter V increases from 106 to 107, the total backlogs are decreased by 5.03% and the system utility is increased by 3.9% on average for the extensive performance by using the proposed DTCCRA algorithm.

关键词： Mobile edge computing   edge server selection   time latency   energy consumption   service migration  

摘要： Mobile edge computing is a new paradigm which provides cloud computing capabilities at the edge of pervasive radio access networks in close proximity to users. The problem of edge server selection in mobile edge environment in terms of user's overhead is investigated in this paper. Due to the limited resources of edge server, we firstly study the task completion probability of edge servers. Secondly, we formally model the problem of edge server selection in terms of time latency and energy consumption. More especially, the computation overhead method for completing the task in cases of both service migration and non-migration is investigated. Then, a new optimized edge server selection algorithm, called combined Genetic algorithm and simulated Annealing algorithm for edge Server Selection (GASS) is designed. Finally, a series of experiments on a real-word data-trace are conducted to evaluate the performance of GASS. The results show that GASS can effectively minimize the overhead of the user and outperform traditional heuristic algorithms.

关键词： Resource allocation   Mobile Edge Computing   Evolutionary game   Noncooperative game  

摘要： Mobile Edge Computing (MEC) is critical to the development of the Internet of things (IoTs) and 5G networks. However, the computation and communication resources of edge servers are limited, so it is challenging to perform resource allocation especially when the competition among edge servers is also taken into consideration. In this paper, we propose a trading model to investigate both the computation and communication resources allocation in MEC systems with multi-server and multi-user. We model the dynamic behavior of mobile users (MUs) using an evolutionary game, and then we build the deterministic and stochastic models to study the evolution of MUs where the evolutionary equilibrium is considered as the solution. We propose an evolution algorithm to obtain the evolutionary equilibrium. Furthermore, we analyze the competition among edge cloud servers (ECSs) by a noncooperative game, and propose an iteration algorithm to obtain Nash equilibrium where the ECSs can adjust the amount of resources provided to MUs and the corresponding price charged in order to attract more MUs. The existences of evolutionary equilibrium and Nash equilibrium are validated in performance evaluation.

关键词： Energy minimization   offloading number maximization   mobile edge computing   load balancing   ant colony optimization algorithm  

摘要： In this paper, we investigate the task scheduling in resource-limited mobile edge computing (MEC) network, where multiple base stations (BSs), each equipped with a MEC server, assist multiple latency-sensitive user equipments (UEs) in computing. We aim to jointly minimize the system energy consumption and maximize the number of offloaded tasks by optimizing the task scheduling between UEs and BSs. A multiple-objective and mix-integer problem is formulated, which is difficult to solve. To tackle the problem, we combine the ant colony optimization (ACO) algorithm with load balancing, and propose an efficient algorithm. The simulation demonstrates the effectiveness of the proposed algorithm.

关键词： Task analysis   Servers   Energy consumption   Delays   Voltage control   Mobile handsets   Optimization   Computation offloading   dynamic voltage scaling   energy efficiency   mobile edge computing  

摘要： By offloading computation-intensive and latency-sensitive tasks from mobile devices to the mobile edge server, mobile edge computing (MEC) has been considered as a promising technology in 5 G and beyond to reduce task delay and energy consumption. This paper considers an optimization framework of computation offloading in a MEC system with multiple devices. We aim to minimize the energy consumption of all devices with their task delay constraints by jointly optimize the communication and computation resource allocation at both the devices and the mobile edge server. Specifically, dynamic voltage scaling (DVS) technology is considered in each device to adjust the operating frequency according to its delay constraint. The formulated problem is a non-convex problem due to a couple among multiple variables. To tackle the non-convex problem, we first decompose the original problem by optimizing the offloading ratio and transmission power iteratively. Then, we proposed a joint communication and computation optimization algorithm based on the difference of convex function algorithms (DCA) to solve the optimization problem. Finally, simulation results show that the proposed joint communication and computation scheme significantly improves the energy efficiency of the devices comparing with the local computing scheme and server computing scheme.

关键词： Meta-heuristic methods   Tabu Search (TS)   Network optimization   Mobile Edge computing   Edge Servers placement   MATLAB  

摘要： With the rapid increased growth of Internet of Things (IoT) and 5G networks in smart cities, big data can be generated, resulting in higher latency for the conventional cloud computing model. To enhance users experience, commodity servers were built at the edges of decentralized networks of mini data centers serving regions. Our paper deals with this problem, in purposes of making a total cost optimization assignment cell to servers in Edge computing network where a so-called meta-heuristic (MH) optimization method was chosen. The first task of the present work is devoted to different methods used in server’s Edge placement network optimization including MH methods. Second, the cell assignment to Edge server problem is formulated. The third task is taken up by developing a Tabu Search (TS) application (of fourteen cells network and three Edge servers as a quasi-optimal procedure) for total costs of networks, simulated under MATLAB software. As perspectives, the results compared with other MH methods should be validated, and the server capacity and traffic constraints for each cell have to be added.

关键词： Mobile edge computing   latency   energy consumption   opportunistic selection  

摘要： In this paper, we investigate a mobile edge computing (MEC) network with two computational access points (CAPs), where the source is equipped with multiple antennas and it has some computational tasks to be accomplished by the CAPs through Nakagami-m distributed wireless links. Since the MEC network involves both communication and computation, we first define the outage probability by taking into account the joint impact of latency and energy consumption. From this new definition, we then employ receiver antenna selection (RAS) or maximal ratio combining (MRC) at the receiver, and apply selection combining (SC) or switch-and-stay combining (SSC) protocol to choose a CAP to accomplish the computational task from the source. For both protocols along with the RAS and MRC, we further analyze the network performance by deriving new and easy-to-use analytical expressions for the outage probability over Nakagami-m fading channels, and study the impact of the network parameters on the outage performance. Furthermore, we provide the asymptotic outage probability in the low regime of noise power, from which we obtain some important insights on the system design. Finally, simulations and numerical results are demonstrated to verify the effectiveness of the proposed approach. It is shown that the number of transmit antenna and Nakagami parameter can help reduce the latency and energy consumption effectively, and the SSC protocol can achieve the same performance as the SC protocol with proper switching thresholds of latency and energy consumption.

关键词： Location privacy protection   MinHash algorithm   Jaccard similarity   Online taxi-hailing   Mobile edge computing  

摘要： Online taxi-hailing has become people's most popular trip mode due to its convenience and low cost. However, it also poses a privacy threat to passengers and drivers, since the online taxi-hailing service providers are able to track their precise mobility trajectories. In addition, there is a certain time delay between the time of a passenger makes a request and the time of the driver arrives the passenger's boarding position in current online taxi-hailing system. To solve these two problems, we present a new and efficient location privacy protection scheme based on the MinHash algorithm (LPPM). With the LPPM, the exact positions of passengers and drivers are generalized into a set of points of interest around them, and the distance between them is transformed into the similarity between the two sets. Thus a service provider can efficiently match passengers and drivers by using MinHash algorithm without revealing their specific location information. In this paper, we use mobile edge computing technology in the online taxi-hailing system to address the second challenge. It can speed up data processing, drivers can make decisions in advance and reduce the possibility of road congestion. Security analysis shows that LPPM has high security, and the final experimental results confirmed that LPPM is effective.

关键词： Mobile edge computing  

摘要： Today a lot of data sensed from the environment by the Internet of things applications. These data need to process with the lowest delay. Mobile devices (MDs) as ubiquitous tools are end devices in the network. These devices with limited resources cannot process all computations locally. Mobile edge computing (MEC) is a good architecture for processing computations in the network's edge. It solves the cloud challenges such as delay, energy, and cost. If MDs could not process the computations, they will offload tasks to the edge or cloud. Research shows that ignoring context information of application, requests, sensors, resources, and network tools cause to not complete the offloading method. In this article, we consider Bayesian learning automata (BLA) with considering context-aware offloading in MEC with multiuser. BLA learns all states and actions in the network and helps us to improve the offloading algorithm. The contexts are collected using autonomous management as the monitor-analysis-plan-execution loop in all offloading processes. The simulation results indicate that our method is superior to local computing and offload without considering context-aware algorithms in some metrics such as energy consumption, execution cost, network usage, delay, and fairness. © 2020 John Wiley & Sons Ltd

关键词： Mobile edge computing   device-to-device cooperation   human mobility   task scheduling   latency minimization  

摘要： Mobile edge computing (MEC) has emerged as a new paradigm to assist low latency services by enabling computation offloading at the network edge. Nevertheless, human mobility can significantly impact the offloading decision and performance in MEC networks. In this context, we propose device-to-device (D2D) cooperation based MEC to expedite the task execution of mobile user by leveraging proximity-aware task offloading. However, user mobility in such distributed architecture results in dynamic offloading decision that instigates mobility-aware task scheduling in our proposed framework. We jointly formulate task assignment and power allocation to minimize the total task execution latency by taking account of user mobility, distributed resources, tasks properties, and energy constraint of the user device. We first propose Genetic Algorithm (GA)-based evolutionary scheme to solve our formulated mixed-integer non-linear programming (MINLP) problem. Then we propose a heuristic named mobility-aware task scheduling (MATS) to obtain effective task assignment with low complexity. The extensive evaluation under realistic human mobility trajectories provides useful insights into the performance of our schemes and demonstrates that, both GA and MATS achieve better latency than other baseline schemes while satisfying the energy constraint of mobile device.