关键词： Mobile edge computing   UAV trajectory   bit allocation   power allocation   resource partitioning   TD-UAV scheduling  

摘要： In this paper, we employ the unmanned aerial vehicle (UAV) as a flying base station (BS) to process the application tasks migrated from the terminal devices (TDs) for saving TDs' energy consumption. To tackle the huge volume of data at TDs, we propose a resource partitioning strategy scheme where one portion of bits at TD is computed locally and the remaining portion is transmitted to UAV for computing. Our goal is to minimize the total energy consumption of TDs by jointly optimizing bit allocation, resource partitioning, power allocation at TDs/UAV, TD-UAV scheduling, and UAV trajectory with One-by-One access scheme. Due to the non-convexity of the original problem with mixed integer variables, we decompose the problem into two sub-problems. Specifically, in the first sub-problem, the TD-UAV scheduling is obtained by solving dual problem with given UAV trajectory. In the second sub-problem, the UAV trajectory is obtained by using successive convex optimization techniques with given TD-UAV scheduling. Then, an iterative algorithm is proposed to optimize the TD-UAV scheduling and UAV trajectory alternately. Numerical results are provided to demonstrate the superiority of our proposed scheme over the benchmarks.

关键词： Unmanned aerial vehicle-enabled communication   mobile edge computing   resource allocation   user association   location optimization  

摘要： In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation, and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing-based algorithm is accordingly proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering-based algorithm is proposed. The numerical results show that the proposed algorithm achieves better performance than the conventional approaches.

关键词： Servers   Heuristic algorithms   Approximation algorithms   Edge computing   Computer architecture   Computational modeling   Optimization   Mobile edge computing   optimization modeling   placement   approximation algorithm  

摘要： The Mobile Edge Network (MEN) has emerged as the basic infrastructure to support fifth-generation networks, mobile edge computing and fog computing. The characteristics of mobility must be addressed to guarantee the quality of service in MENs. As one of the critical problems in MENs, flexible deployment plays a part in exploiting edge networks. Despite the abundance of recently proposed strategies, most concentrate on the change in user demands and inevitably ignore the influence for the user mobility, which is common in future networks. We propose Provision for Mobile Edge Computing (PMEC), a prototype that takes advantage of storage devices with flexible placement. In PMEC, we accommodate various considerations and select different storage devices to cache, deploying the cache with the relationship of a two-tiered structure in the MEN. Thus, we construct a flexible overlay network with the objective of minimizing the cost in a two-tiered edge network. Based on the analysis of the problem, we solve the two-tiered placement from bottom to top using the dynamic minimal spanning tree (MST) algorithm and design two algorithms for each tier including the basic algorithm and the improved algorithms. The simulation is conducted on realistic data to demonstrate the performance of our algorithms.

关键词： Cloud computing   Task analysis   Wireless communication   Servers   Mobile handsets   Energy harvesting   Wireless sensor networks   Mobile edge computing   computation offloading   energy harvesting   multi-user mult-task scheduling  

摘要： Mobile Edge Cloud Computing (MECC) has becoming an attractive solution for augmenting the computing and storage capacity of Mobile Devices (MDs) by exploiting the available resources at the network edge. In this work, we consider computation offloading at the mobile edge cloud that is composed of a set of Wireless Devices (WDs), and each WD has an energy harvesting equipment to collect renewable energy from the environment. Moreover, multiple MDs intend to offload their tasks to the mobile edge cloud simultaneously. We first formulate the multi-user multi-task computation offloading problem for green MECC, and use Lyaponuv Optimization Approach to determine the energy harvesting policy: how much energy to be harvested at each WD;and the task offloading schedule: the set of computation offloading requests to be admitted into the mobile edge cloud, the set of WDs assigned to each admitted offloading request, and how much workload to be processed at the assigned WDs. We then prove that the task offloading scheduling problem is NP-hard, and introduce centralized and distributed Greedy Maximal Scheduling algorithms to resolve the problem efficiently. Performance bounds of the proposed schemes are also discussed. Extensive evaluations are conducted to test the performance of the proposed algorithms.

关键词： mobile edge computing   multi-user edge server selection   resource contention   total waiting time  

摘要： In mobile edge computing, a set of edge servers is geographically deployed near the mobile users such that accessible computing capacities and services can be provided to users with low latency. Due to user's mobility, one fundamental and critical problem in mobile edge computing is how to select edge servers for many mobile users so that the total waiting time is minimized. In this paper, we propose a multi-user waiting time computation model about composite services and show the resource contention of the edge server among mobile users. Then, we introduce a novel and optimal Multi-user Edge server Selection method based on Particle swarm optimization (MESP) in mobile edge computing, which selects edge servers for mobile uses in advance within polynomial time. Extensive simulations on a real-world data-trace show that the MESP algorithm can effectively reduce the total waiting time compared with traditional approaches.

关键词： Mobile edge computing   tandem queue   effective capacity   resource allocation   statistical QoS provisioning  

摘要： In the mobile edge computing (MEC) network, the applications of devices can be offloaded to the MEC server via the wireless link and then processed through the computation resource, to satisfy the computation and latency demand. Thus, a two-stage tandem queue is formed in the MEC network, consisting of the transmission queue and computation processing queue. However, the fluctuating wireless channel environment not only leads to the stochasticity of service in the first transmission queue, but also brings random computation task arrival in the second computation processing queue, which makes it difficult to guarantee the end-to-end quality of service (QoS) requirement. In this paper, we firstly derive the effective capacity of MEC with the two-stage tandem queue. Further, we formulate the joint bandwidth and computation resource allocation problem under the statistical QoS guarantee, to maximize the total revenue of network. This problem is proven to be NP-hard by the reduction to the two-dimensional knapsack problem. Then we propose an efficient algorithm based on alternating direction method of multipliers (ADMM) to reduce the computation complexity, where the complicated problem can be decomposed and transformed into some convex subproblems. Simulation results reveal the inherent relationship between the required bandwidth and computation resource in terms of the supported arrival rate and end-to-end delay, and also demonstrate the proposed scheme can achieve better performance than other schemes.

关键词： pricing   transcoding   resource allocation   cache storage   scheduling   cellular radio   game theory   MEC server   BSs   caching   computing space   storage   computational capacities   mobile edge networks   computation resource allocation   mobile edge computing   fifth generation networks   computing resources   mobile networks   joint caching   computing resource allocation mechanism  

摘要： To cope with the explosion of video traffic, mobile edge computing (MEC) has been introduced as a promising technology in fifth generation networks to enable caching and computing resources to transcode video contents and cache them within the edge of mobile networks. In this study, a joint caching and computing resource allocation mechanism is proposed to effectively schedule the resources of the mobile edge networks. The authors formulate a Stackelberg game to analyse the allocation problem between the MEC server and multiple base stations (BSs). The MEC server aims to impose the prices to the BSs to maximise its revenue, while the BSs compete to determine the caching/computing space that they can occupy at the MEC server to improve the quality of experience of their service users. The competition between the BSs is formulated as a non-cooperative subgame where each BS is designed to maximise the request rate that can be served by the MEC server. Then, the proposed games are proved to converge to a unique Stackelberg equilibria. Numerical simulations are presented to verify the proposed scheme. It is shown that the proposed schemes are effective in resource allocation for both storage and computational capacities in mobile edge networks.

关键词： Multimedia applications   Mobile edge computing   IoT   Context-aware computing   Cloud computing   Mobile sensors  

摘要： The enormous growth of the Internet of Things (IoT) devices gave governments, businesses, and individual users new means to accomplish their missions. Several IoT applications require deployment at a large scale such as smart cities. Large-scale IoT applications help achieve better monitoring services and more efficient cities. IoT multimedia (IoTMM) applications provide a unique level of intelligence that cannot be obtained with traditional IoT applications. IoTMM applications intensely consume bandwidth, processing, and storage resources compared to traditional IoT applications. IoT nodes vary in their capabilities, where some nodes have high processing capabilities while others do not. Cloud services models (IaaS, PaaS, and SaaS) provide a perfect solution for applications of high demand for resources. However, such models are inefficient when dealing with multimedia applications due to the high bandwidth requirements before reaching the cloud. Mobile edge computing (MEC) model creates a new level of providers according to the proximity of the end users to the network resources. Edge providers can offload some processing that is usually done at the cloud, which improves the overall performance of cloud-based applications. In this paper, we propose a new distributed structure for processing multimedia applications in the cloud, where different layers of processing and providers are involved. Every layer is responsible for a specific role depending on the type of the multimedia application and multimedia device. The major contribution of the proposed architecture includes two main elements: the support of scalable IoTMM applications in large deployment scenarios and the support of effective multimedia information sharing. The proposed architecture supports a scalable architecture for IoTMM applications with minimum additional resources compared to the traditional models. The proposed model allows for effective and practical sharing of multimedia information (raw mu

关键词： Fog computing   Edge computing   Cloud computing   Internet of things (IoT)   Cloudlet   Mobile edge computing   Multi-access edge computing   Mist computing  

摘要： With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

关键词： Mobile edge computing   Wireless energy transfer   Multi-user offloading   ADMM  

摘要： In this paper, a wireless powered mobile edge computing (WP-MEC) system is considered, in which a hybrid access point integrated with MEC servers can charge N wireless devices (WDs) by broadcasting radio-frequency signals, and the time division multiple access (TDMA) protocol is used for task offloading of WDs. The goal of this paper is to maximize the weighted sum computation rate by joint optimization of system resources management and task computing time allocation. To solve this optimization problem, an alternating direction multiplier method (ADMM) based distributed optimization method is proposed. The proposed method can decompose the optimization problem into N sub-problems, which are solved by N WDs. Experimental results show that the proposed method outperforms the benchmarks and greatly increases the weighted sum computation rate while keeping the energy consumption at a low level under the premise of time complexity O(N).