关键词： Quality of service   Streaming media   Delays   Multimedia systems   Scheduling algorithms   Heuristic algorithms   Optimization   Deep-Q-network   deep reinforcement learning   mobile edge computing   multimedia   quality of service  

摘要： By offloading storage and computing resources to the edge of networks, mobile edge computing (MEC) is emerged as a promising architecture to reduce the transmission delay and bandwidth waste for mobile multimedia services. This paper focuses on a multi-service scenario in the MEC systems, where the MEC server can provide three multimedia services including live streaming, buffered streaming and low latency enhanced mobile broadband applications for edge users at the same time. In order to satisfy various quality of service (QoS) requirements for different multimedia applications, the 5G QoS model is applied. Notably, the packets from the multimedia applications with the same or similar requirements are mapped into the same QoS flow, and each QoS flow is processed individually. Therefore, how to effectively schedule the limited radio resource for QoS flows is an intractable problem. To address the problem above, a QoS evaluation model is designed, and a QoS maximization problem is formulated. Furthermore, a deep reinforcement learning method, deep-Q-network, is adopted to make decisions to allocate radio resource dynamically. Compared with round-robin and priority-based scheduling algorithms, the simulation results validate that the proposed algorithm outperforms other resource scheduling algorithms for multi-service scenario.

关键词： Computing Capabilities   Hybrid Firefly Algorithm   Mobile edge computing   Mobile Switching Center  

摘要： Mobile edge computing (MEC) is a recent technology that brings the computing resources too closer to the network edge with reduced response rate in computationally intensive applications. With increasing mobile devices with limited computing and wireless capabilities, the response time of MSC deteriorates. Therefore it is necessary to jointly manage the computing and wireless resources in order to avoid deterioration due to limited capabilities. Also, the joint optimisation techniques limits its capability due to computational complexity and incompatible devices. To avoid all these limitations, in this paper, we propose a joint optimisation model that suitably optimizes the cloud and wireless resources in an optimal way, thereby reducing its response time. A game base theoretical model is developed to maintain the equilibrium between the resources and computational capabilities. To improve the resources allocation capability, we use the Bees Swarm Optimisation (BSO) model for better allocation of resources in MEC. The performance is compared with other existing methods to test the effectiveness of the model. © 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved.

关键词： Mobile edge computing   communication and computation cooperation   joint optimization   convex optimization   block coordinate descent  

摘要： The advantages of mobile edge computing (MEC) in lower energy consumption, improved bandwidth and reduced delay have attracted extensive studies. We consider a multiple smart wearable devices (SWDs) single smart mobile device (SMD) MEC system where the computation-intensive tasks can be offloaded to SMD and MEC server to improve resources utilization in this paper. To further minimize the energy consumption and satisfy the delay constraints of SWDs, SMD cooperation is introduced and we jointly optimize two-tier computation and communication resources in this system to achieve our objective. The formulated problem is a non-convex optimization problem where we design an efficient iterative algorithm by integrating the block coordinate descent and convex optimization techniques together. The proposed algorithm ensures convergence to a suboptimal solution based on the properties of convex optimization problem. In particular, the original non-convex optimization problem is decomposed into two convex optimization subproblems. Furthermore, we simplify the first subproblem using Lagrange method which is transformed into solving a set of non-linear equations. The extensive simulation results indicate that the proposed algorithm by jointly optimizing two-tier computation and communication resources can reduce energy consumption compared with other baseline methods. The solution of proposed algorithm is close to the optimal solution and can be generated in a shorter time.

关键词： mobile edge computing   wireless power transfer   joint optimization   convex optimization   BFGS  

摘要： Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency.

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   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 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： 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： 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.