关键词： Mobile edge computing  

摘要： —To overcome devices’ limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation. However, current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users’ power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are re-associated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user-server association policy is proposed, taking into account the channel quality as well as the servers’ computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user-server association is solved in the long timescale while a dynamic task offloading and resource allocation policy is executed in the short timescale. Simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly-reliable task computation and lower delay performance, compared to several baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Data path   FPGA 5G   Mobile edge computing   Reconfigurable hardware   Network architecture and design  

摘要： The Fifth-Generation (5G) networks, as the emerging next generation mobile networks, are adopting softwarization and virtualization technologies as the cornerstones for the network operators to gain significant competitive advantages by reducing both capital and operational expenditure, enabling agile and flexible service creation and deployment, among others. Meanwhile, a virtualized and softwarized 5G network would suffer from downgraded system performance due to this unprecedented paradigm shift towards software-based networking. Addressing one of the top challenges in this context, this paper focuses on improving the performance of the data plane from the edge to the core network segment (backhaul) in a 5G multi-tenant network by leveraging and exploring the programmability introduced by software-based networking. A fully functional prototype has been designed and implemented utilizing a Field Programmable Gate Arrays (FPGA5) acceleration-based platform, and the proto-typed system has been empirically tested and evaluated to demonstrate the superior performance enhancements. The proposed solution can effectively support 5G networks in delivering mission-critical or time-sensitive applications such as ultra-high definition video use cases as experimentally validated and shown in this paper, by fulfilling the strict Quality of Service (QoS) requirements imposed to the data plane.

关键词： mobile edge computing   UAV trajectory   bit allocation   power allocation   resource partitioning   energy minimization  

摘要： In this paper,a cellular-connected unmanned aerial vehicle(UAV)mobile edge computing system is studied where several UAVs are associated to a terrestrial base station(TBS)for computation *** compute the large amount of data bits,a part of computation task is migrated to TBS and the other part is locally handled at *** goal is to minimize the total energy consumption of all UAVs by jointly adjusting the bit allocation,power allocation,resource partitioning as well as UAV trajectory under TBS’s energy *** deeply comprehending the impact of multi-UAV access strategy on the system performance,four access schemes in the uplink transmission is considered,i.e.,time division multiple access,orthogonal frequency division multiple access,one-by-one access and non-orthogonal multiple *** involved problems under different access schemes are all formulated in non-convex forms,which are difficult to be tackled *** solve this class of problem,the successive convex approximation technique is employed to obtain the suboptimal *** numerical results show that the proposed scheme save significant energy consumption compared with the benchmark schemes.

关键词： mobile edge computing   service provider selection   joint resource allocation   evolutionary game  

摘要： To satisfy mobile terminals ’( MTs) offloading requirements and reduce MTs’ cost,a joint cloud and wireless resource allocation scheme based on the evolutionary game( JRA-EG) is proposed for overlapping heterogeneous networks in mobile edge computing environments. MTs that have tasks offloading requirements in the same service area form a population. MTs in one population acquire different wireless and computation resources by selecting different service providers( SPs). An evolutionary game is formulated to model the SP selection and resource allocation of the MTs. The cost function of the game consists of energy consumption,time delay and monetary cost. The solutions of evolutionary equilibrium( EE) include the centralized algorithm based on replicator dynamics and the distributed algorithm based on *** results show that both algorithms can converge to the EE rapidly. The differences between them are the convergence speed and trajectory stability. Compared with the existing schemes,the JRA-EG scheme can save more energy and have a smaller time delay when the data size becomes larger. The proposed scheme can schedule the wireless and computation resources reasonably so that the offloading cost is reduced efficiently.

关键词： Mobile cloud Computing   mobile edge computing   cloud computing   5G systems   parallelism   mobile storage  

摘要： The realization of mobile edge computing (MEC) over emerging fifth (5G) generation of wireless systems arises as a driving-force in the future of cloud computing. In order to cope with the volume, variety, and velocity of the IoT traffic while making optimal use of the network infrastructure, a synergistic exploitation of MEC and 5G should be put forward to support advanced resource management applications. In this paper, we propose the use of joint parallelism between wireless and cloud domains to efficiently respond to mobile data deluge. We review the literature, discuss the enabling network architecture, potentials. challenges, and open issues related to the realization of such level of parallelism. We present and evaluate two design examples - parallel computation offload method (PCOM) and parallel transmission and storage method (PTSM)-which outline the benefits of parallelism for computation-hungry and storage-hungry applications, respectively. Results of our optimization formulation show that PCOM and PTSM are able to make an efficient use of the network resources and support a heavy instantaneous workload by means of the parallelism.

关键词： Mobile edge computing   Mobility management   Dynamic offloading   Task size assignment  

摘要： Mobile Edge Computing (MEC) brings computation closer to user equipment (UE) and reduces the latency as well as energy consumption for computation offloading. However, with the development of Ultra Dense Network (UDN), traditional offloading algorithms and mobility management (MM) approaches are not sufficient anymore with a frequent handover between base stations. It becomes far from optimal to execute offloading algorithms only at the beginning of a task when UE moves frequently or in the environment of UDN. Also, simple dynamic adjustment for offloading proportion of data input during UE's movement cannot solve this problem very well, because this approach executes global decision with information from only one position. Exploiting short-term mobility prediction in MM, we propose a novel dynamic mobility-aware partial offloading (DMPO) algorithm to figure out the amount of data for offloading dynamically, together with the decision of communication path in MM, minimizing the energy consumption while satisfying the delay constraint. This proposed algorithm predicts the time to next handover as well as the moving of UE in this phase and assigns data size to each time slot in this phase to achieve our goal of minimizing energy consumption while satisfying the delay constraint. In the simulation, we obverse the average delay and energy consumption with different delay constraint, moving speed of UE and base station density. The results demonstrate that our proposed algorithm saves up to 70% of energy while having a better performance in satisfying the delay constraint compared to traditional approaches. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Heterogeneous tasks   Internet of Things (IoT)   mobile edge computing   online learning   saddle-point method  

摘要： Internet of Things (IoT) is featured with its seamless connectivity of billions of smart devices, which offer different functionalities and serve various personalized tasks. To meet the task-specific requirements such as latency and privacy, the fog computing emerges to extend cloud computing services to the edge of the Internet backbone. This paper deals with online fog computing emerging in IoT, where the goal is to balance computation and communication at fog networks on-the-fly to minimize service latency. Due to heterogeneous devices and human participation in IoT, the online decisions here need to flexibly adapt to the temporally unpredictable user demands and availability of fog resources. By generalizing the classic online convex optimization (OCO) framework, the low-latency fog computing task is first formulated as an OCO problem involving both time-varying loss functions and time-varying constraints. These constraints are revealed after making decisions, and allow instantaneous violations yet they must be satisfied in the long term. Tailored for heterogeneous tasks in IoT, a "thing-adaptive" online saddle-point (TAOSP) scheme is developed, which automatically adjusts the stepsize to offer desirable task-specific learning rates. It is established that without prior knowledge of the time-varying parameters, TAOSP simultaneously yields near-optimality and feasibility, provided that the best dynamic solutions vary slowly over time. Numerical tests corroborate that our novel approach outperforms the state-of-the-art in minimizing network latency.

关键词： Mobile edge computing   resilient facility location   software controller   virtual function placement   IoT  

摘要： Replacing hardware controllers with software-based virtual process control functions (VPFs) is a promising approach for improving the operational efficiency and flexibility of industrial control systems. VPFs can be executed in edge clouds in 5G mobile networks or in the wireless backhaul, which can further improve efficiency. Nonetheless, for the acceptance of virtualization in industrial control systems, a fundamental challenge is to ensure that the placement of VPFs be resilient to component failures and cyber-attacks, besides being efficient. In this paper we address this challenge by considering that VPF placement costs are incurred by reserving mobile edge computing (MEC) resources, executing VPF instances, and by data communication. We formulate the VPF placement problem as an integer programming problem, considering resilience as a constraint. We propose a solution based on generalized Benders decomposition and based on linear relaxation of the resulting sub-problems, which effectively reduces the number of integer variables to the number of MEC nodes. We evaluate the proposed solution with respect to operational cost, efficiency, and scalability in a simulated metropolitan area. Our results show that the proposed solution reduces the total cost significantly compared to a greedy baseline algorithm and a local search heuristic, and can scale to moderate problem instances.

关键词： Computation offloading   mobile edge computing   multi-access non-orthogonal multiple access   resource allocations  

摘要： Multi-access mobile edge computing (MEC), which enables mobile users (MUs) to offload their computationworkloads to the computation-servers located at the edge of cellular networks via multi-access radio access, has been considered as a promising technique to address the explosively growing computation-intensive applications in mobile Internet services. In this paper, by exploiting non-orthogonal multiple access (NOMA) for improving the efficiency of multi-access radio transmission, we study the NOMA-enabled multi-access MEC. We aim at minimizing the overall delay of the MUs for finishing their computation requirements, by jointly optimizing theMUs' offloaded workloads and the NOMA transmission-time. Despite the non-convexity of the formulated joint optimization problem, we propose efficient algorithms to find the optimal offloading solution. For the single-MU case, we exploit the layered structure of the problem and propose an efficient layered algorithm to find the MU's optimal offloading solution that minimizes its overall delay. For the multi-MU case, we propose a distributed algorithm (in which the MUs individually optimize their respective offloaded workloads) to determine the optimal offloading solution for minimizing the sum of allMUs' overall delay. Extensive numerical results have been provided to validate the effectiveness of our proposed algorithms and the performance advantage of our NOMA-enabled multi-access MEC in comparison with conventional orthogonal multiple access enabled multi-access MEC.

关键词： Mobile edge computing   small cell networks   computation offloading   Nash equilibrium   potential game  

摘要： Mobile edge computing is conceived as an appealing technology to enhance cloud computing capability of mobile devices (MDs) at the edge of the networks. Although some researchers use the technology to address the intensive tasks' high computation needs of MDs in small-cell networks (SCNs), most of them ignore considering the interests interaction between small cells and MDs. In this paper, we study a distributed computation offloading strategy for a multi-device and multi-server system based on orthogonal frequency-division multiple access in SCNs. First, to satisfy the interest requirements of different MDs and analyze the interactions among multiple small cells, we formulate a distributed overhead minimization problem, aiming at jointly optimizing energy consumption and latency of each MD. Second, to ensure the individuals of different MDs, we formulate the proposed overhead minimization problem as a strategy game. Then, we prove the strategy game is a potential game by the feat of potential game theory. Moreover, the potential game-based offloading algorithm is proposed to reach a Nash equilibrium. In addition, to guarantee the performance of the designed algorithm, we consider the lower bound of iteration times to derive the worst case performance guarantee. Finally, the simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each MD compared with different other existing algorithms.