关键词： Routing   Bandwidth   Approximation algorithms   Heuristic algorithms   IEEE transactions   Cloud computing   Edge computing   Service placement   routing   joint optimization   mobile edge computing  

摘要： The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network edge, in proximity to the end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be pre-stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in dense MEC networks with multidimensional constraints. We show that this problem generalizes several well-known placement and routing problems and propose an algorithm that achieves close-to-optimal performance using a randomized rounding technique. Evaluation results demonstrate that our approach can effectively utilize available storage, computation, and communication resources to maximize the number of requests served by low-latency edge cloud servers.

关键词： Blockchain   Mobile handsets   Servers   Resource management   Task analysis   Energy consumption   Optimization   Mobile edge computing   blockchain   energy consumption   delay   time to finality  

摘要： The application of blockchain to mobile edge computing (MEC) systems has attracted great interests. However, the design and optimization of blockchain and MEC in most existing works are done separately, which will result in sub-optimal performance. In this paper, we propose a joint optimization framework for blockchain-enabled MEC systems to achieve the optimal trade-off between the performance of the MEC system and the performance of the blockchain system. Specifically, both MEC and blockchain are considered as services in the framework, where energy consumption and delay/time to finality (DTF) are the performance metrics for the MEC system and the blockchain system, respectively. We formulate an optimization problem to achieve the optimal trade-off through jointly optimizing user association, data rate allocation, block producer scheduling, and computational resource allocation. To solve the problem, we decouple the optimization variables for efficient algorithm design. In addition, we develop an iterative algorithm for user association and data rate allocation and a bisection algorithm for computing resource allocation. Simulation results show the convergence of the proposed algorithms, and the proposed scheme can achieve the optimal trade-off between energy consumption and DTF.

关键词： cloud computing   genetic algorithms   mobile computing   resource allocation   power aware computing   MEC   effective assistant technology   cloud computing   mobile devices   MD   power consumption   user-oriented use case   finite computation capabilities   energy consumption   multiuser multiapplication computation offloading method   multicloudlet environment   joint optimisation   mobile edge computing  

摘要： Mobile edge computing (MEC) is an effective assistant technology that can overcome some defects of cloud computing. For the sake of alleviating the clashes between the capability constraint of cloudlets and the needs of mobile devices (MDs) for reducing executing latency as well as decreasing the power consumption of MDs, a user-oriented use case in the MEC named computation offloading is taken into consideration. Computation offloading is capable of effectively making the MEC adapt to the resources of cloudlets and MDs in different environments, and it is very beneficial to the development of the internet of things. Owing to the finite computation capabilities of the MDs and the resources of cloudlets are heterogeneous and limited;a three-objective model is established to optimise the time consumption, and the energy consumption of MDs as well as the load balancing of cloudlets jointly. Technically, the authors propose an effective multi-user multi-application computation offloading method in the multi-cloudlet environment on the basis of improved non-dominated sorting genetic algorithm III. Finally, comprehensive experiments and analysis were conducted to validate the effectiveness and efficiency of the proposed method.

关键词： Energy consumption   Servers   Object detection   Energy resolution   Image resolution   Batteries   Wireless communication   Augmented reality   energy efficiency   mobile edge computing   object detection   optimization  

摘要： This letter proposes a novel resolution and power control scheme for energy-efficient mobile augmented reality (MAR) services through computation offloading to mobile edge computing (MEC). For this purpose, analytical models for the energy consumption of a MAR device (MD), detection accuracy of MAR objects, and service latency are rigorously formulated. Considering their tradeoff in MAR with MEC, a theoretical framework for the computation offloading problem of the MD that jointly optimizes the transmission power and frame resolution is designed. The proposed scheme significantly reduces energy consumption by 73.64% with an accuracy loss of 1.6% while guaranteeing the required latency constraint.

关键词： Delays   Computational modeling   Downlink   Graphical models   Distribution functions   Analytical models   Cellular networks   Mobile edge computing   MEC   stochastic geometry   local delay   VANET  

摘要： Mobile edge computing (MEC) is a promising computing paradigm for improving the quality of service (QoS) of a vehicular ad hoc network (VANET). The local delay is one of the major components in the delay for a node to transmit a data packet to another node. This paper studies the modeling and analysis of the downlink local delay in an MEC-based highway road scenario. A theoretical model is built based on stochastic geometry for analyzing the average downlink local delay for a vehicle driving on a highway road to receive a data packet from an edge node along the highway road. In deriving the model, the spatial distributions of vehicle nodes and edge nodes are respectively modeled as an independent one-dimensional (1-D) homogeneous Poisson point process (PPP). A nearest-transmitter model is used to determine the edge node for transmitting a computing result to the corresponding service requesting vehicle node. Moreover, a carrier sense multiple access (CSMA) mechanism is considered for channel access. The derived theoretical model is validated through simulation results and the impacts of major parameters on the average downlink local delay are investigated based on the derived model. The built model provides an insight into the deployment of edge nodes in order to reduce the downlink local delay and provide timely network services.

关键词： DVB   digital video broadcasting   HbbTV   hybrid broadcast broadband   MEC   mobile edge computing   MNO   mobile network operator   NFV   network function virtualization   RAN   radio access network   SDN   software defined network   VLC   visible light communication  

摘要： Fifth-Generation (5G) mobile networks are expected to perform according to the stringent performance targets assigned by standardization committees. Therefore, significant changes are proposed to the network infrastructure to achieve the expected performance levels. Network Function Virtualization, cloud computing and Software Defined Networks are some of the main technologies being utilised to ensure network design, with optimum performance and efficient resource utilization. The aforementioned technologies are shifting the network architecture into service-based rather device-based architecture. In this regard, we introduce Media Casting as a service (MCaaS) and IoRL-Cache service (IoRL-C). The former is a service that proposed as an integration solution between the broadcasting industry and Mobile Network Operators, while the latter proposed as a solution for improving IoRL small-cells caching efficiency. IoRL is an emerging 5G small-cell for indoor environment, which utilises mmWave and Visible Light Communications (VLC) as access technologies, while exploiting Software Defined Networking (SDN) and Network Function Virtualisation (NFV) technologies to offer flexible and intelligent services to its clients. In this paper, we introduce MCaaS and IoRL-C services, we perform simulation work for testing IoRL-C as a proof of concept for both services. The network was simulated using OMNeT++ simulation tool, and we validated the services efficiency by comparing their performance with traditional deployments. We have also examined the cache service performance at different link lengths, and found out that IoRL-C is able to support IoRL small-cells with more than 50+Km separation distances.

关键词： Global positioning system (GPS) devices   mobile edge computing   resource-constrained   trajectory mapping   trajectory compression  

摘要： Vehicles can be easily tracked due to the proliferation of vehicle-mounted global positioning system (GPS) devices. V Tracer is a cost-effective mobile system for online trajectory compression and tracing vehicles, taking the streaming GPS data as inputs. Online trajectory compression, which seeks a concise and (near) spatial-lossless data representation before revealing the next vehicle's GPS position, is gradually becoming a promising way to alleviate burdens such as communication bandwidth, storing, and cloud computing. In general, an accurate online mapmatcher is a prerequisite. This two-phase approach is nontrivial because we need to overcome the essential contradiction caused by the resource-constrained GPS devices and the heavy computation tasks. V Tracer meets the challenge by leveraging the idea of mobile edge computing. More specifically, we offload the heavy computation tasks to the nearby smartphones of drivers (i.e., smartphones play the role of cloudlets), which are almost idle during driving. More importantly, they have relatively more powerful computing capacity. We have implemented V Tracer on the Android platform and evaluate it based on a real driving trace dataset generated in the city of Chongqing, China. Experimental results demonstrate thatV Tracer achieves the excellent performance in terms of matching accuracy, compression ratio, and it also costs the acceptable memory, energy, and app size.

关键词： unmanned aerial vehicle   mobile edge computing   offloading   energy efficiency  

摘要： The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different

关键词： Task analysis   Mobile handsets   Processor scheduling   Power demand   Wireless communication   Optimal scheduling   Mobile edge computing   Markov decision process   Lyapunov optimization   power-latency tradeoff  

摘要： Mobile edge computing (MEC) is a promising technique to improve the quality of computation experience for mobile devices by providing computation resources in their close proximity. However, the design of scheduling policies for MEC systems inevitably encounters a challenging optimization problem that should take both transmissions and computations into consideration. In particular, how to jointly schedule transmissions and computations should adapt to the cross-layer system dynamics, i.e., random task arrivals and channel state variations. We formulate this scheduling problem as a joint optimization problem for both transmissions and computations in order to minimize the power consumption of mobile devices, while meeting the latency requirement. With given distributions of the system dynamics, Markov decision process (MDP) is used to model the system operations. Based on this model, the power-optimal scheduling policy can be obtained by converting the joint optimization problem to linear programming (LP) by using variable substitutions and thus the optimal power-latency tradeoff can be achieved. When the distribution information of the system dynamics is unknown, we exploit the Lyapunov optimization to present a low complexity scheduling policy. Our theoretical analysis and extensive simulation studies show that our approach can offer a good tradeoff between power consumption and latency.

关键词： Mobile handsets   Bandwidth   Task analysis   Servers   Wireless communication   Computational modeling   Edge computing   Mobile edge computing   wireless caching   multicast   low-latency   bandwidth  

摘要： In this paper, we present a novel mobile edge computing (MEC) model where the MEC server has the input and output data of all computation tasks and communicates with multiple caching-and-computing-enabled mobile devices via a shared wireless link. Each task request can be served from local output caching, local computing with input caching, local computing without local caching or MEC downloading, each of which incurs a unique bandwidth requirement of the multicast link. Aiming to minimize the transmission bandwidth, we optimize the joint caching and computing policy at mobile devices subject to latency, caching, power and multicast transmission constraints. The joint policy optimization problem is shown to be NP-hard. To tackle the problem of intractability of priori knowledge of users' request popularity, we approximate the expectation via sampling. When all the output data size is smaller than the input data size, we reformulate the problem as minimization of a monotone submodular function over matroid constraints and obtain the optimal solution via a strongly polynomial algorithm of Schrijver. Otherwise, by leveraging concave convex procedure together with the alternating direction method of multipliers, we propose a low-complexity high-performance algorithm and prove it converges to a local minimum. Furthermore, in homogeneous case, we theoretically reveal how much bandwidth gain can be achieved from computing and caching resources at mobile devices or the multicast transmission. Our results indicate that exploiting the computing and caching resources at mobile devices as well as multicast transmission can provide significant bandwidth savings.