关键词： Optimization   Streaming media   Transcoding   Servers   Artificial neural networks   Edge computing   Resource management   Mobile edge computing   small cell networks   computation offloading   resource allocation   in-network caching  

摘要： In this correspondence, we jointly optimize the caching decision and radio and computation resource allocation for video service in mobile edge computing (MEC) enabled network to maximize the system revenue. To tolerate the uncertainty in the network traffic and avoid "hard constraint" based on constant content request rate, we employ robust optimization to obtain optimal caching decisions, and then accordingly allocate the radio and computation resources for video transcoding. Simulation results demonstrate the effectiveness of the proposed scheme.

关键词： Device-to-device communications   Game theory   Security   Mobile Edge Computing  

摘要： The unique distributed architecture of device-to-device (D2D) networks makes its devices and users more vulnerable to security and privacy threats, especially from malware. To protect the D2D network from the malware, we designed and implemented a novel routing protocol, Cluster-head Centered Fast Secure Routing. The protocol requires all of the member nodes to pass data packets through the Secure Routes selected by the cluster-head that optimizes routing decisions to reduces the possibility of the nodes receiving packets with malware. After our analysis, the strategy is the optimal coping strategy of thedefenderwhenever theattackerusing the Nash game strategy, the Stackelberg game strategy, considering the case of both zero-sum and the non-zero sum. Moreover, to ensure the security of the cluster-head, we have set up the Mobile Edge Computing servers at the edge of the D2D networks. Simulation results demonstrate that the proposed routing protocol can balance network security and Quality of Service, thus confirming that our proposed routing algorithm has practical significance.

关键词： Task analysis   Servers   Energy consumption   NOMA   Optimization   Time-frequency analysis   Programming   Non-orthogonal multiple access   mobile edge computing   system computation energy efficiency  

摘要： Energy-efficient communication is one of the most pertaining issues in realizing mobile edge computing (MEC) networks. How to effectively use non-orthogonal multiple access (NOMA) to realize energy efficient MEC networks has been of high interest and calls for in-depth study. In particular, there has been yet no work to study the energy efficient communication scheme from system perspective in this area. In this work, we focus on the design of a system-centric computation energy efficiency (EE) scheme in a NOMA-based MEC network. The system computation EE is maximized by jointly optimizing the computing frequencies of the MEC server and the local users, the offloading time, the transmit powers of local users, as well as the execution time for the local users and the MEC server. Employing the fractional programming theory and introducing auxiliary variables, we develop an efficient Dinkelbach-based iterative scheme to tackle the formulated non-linear fractional programming problem. For obtaining instrumental insights, we further derive closed-form expressions for the partial solutions based on Karush-Kuhn-Tucker conditions. Simulation results are provided to verify the quick convergence of the proposed iterative algorithm, to obtain insights, as well as to demonstrate the superiority of the proposed resource allocation scheme in terms of the system computation EE by comparing it with benchmark schemes.

关键词： Task analysis   Servers   Channel allocation   Resource management   Stability analysis   Energy consumption   Delays   Mobile edge computing   computation offloading   network utility optimization   resource management   channel allocation  

摘要： To accommodate ever-increasing computational workloads while satisfying the requirements of delay-sensitive tasks, mobile edge computing (MEC) is proposed to offload the tasks to nearby edge servers. Nevertheless, it can introduce new technical issues in terms of transmission and computation overheads affected by the underlying offloading decisions. In this paper, we investigate the computation offloading problem in a hierarchical network architecture, where tasks can be offloaded to nearby micro-BS and further forwarded to macro-BS equipped with an MEC server. Specifically, we propose a scheme of joint channel allocation and resource management, named JCRM, to make offloading decisions and maximize the long-term network utility with considering stochastic task arrival/dispatch and dynamic changes in available resources. As the formulated utility maximization problem is a mixed-integer non-linear stochastic programming problem that is directly intractable, we, therefore, leverage the Lyapunov optimization technique to decouple the original problem into three separate sub-problems. Based on the solutions to those sub-problems, our proposed scheme can make optimal offloading-downloading decisions with maximizing the overall task offloading rate. Finally, we verify the long-term network stability and near-optimal performance of JCRM via both theoretical analysis and extensive simulations.

关键词： Mobile edge computing   Matching theory   Resource allocation   Task offloading scheduling  

摘要： Mobile edge computing (MEC) has emerged as an effective paradigm that delivers cloud services and functions to edge devices, with the objective to further enhance quality of service (QoS) of terminal users by offloading their computation-intensive tasks. In this article, a multi-user and multi-server MEC system is considered and each user can choose one MEC server to execute its computation task. We try to minimize the system delay (i.e., the maximum server delay). The problem is decomposed into task offloading problem and transmit power allocation problem which are solved by matching theory and a heuristic idea, respectively. The experimental results show that the proposed algorithm can not only obtain less delay, but also generate less energy consumption compared with the decomposed computation offloading and resource allocation algorithm, the shortest distance based scheduling algorithm and the random scheduling algorithm, especially when the data amount of tasks is the same but the workload is random. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Task analysis   Energy consumption   Servers   Network security   Physical layer   Resource management   Delays   Computation offloading   energy consumption   mobile edge computing   physical layer security   resource allocation  

摘要： Mobile edge computing (MEC) has become a promising technology for real-time communications. Mobile devices can reduce the energy consumption and prolong the lifetime significantly via offloading the computing tasks to the MEC server. Moreover, physical layer security techniques can ensure the secure transmission of the offloading data. This paper investigates a MEC system that consists of an access point, multiple mobile devices and a malicious eavesdropper. The tasks allocation, local central processor's frequency, offloading power, and offloading timeslots are optimized jointly to minimize the total energy consumption of the system. A difference of convex algorithm based scheme is proposed to solve the joint optimization problem. Moreover, a Karush Kuhn Tucker conditions based algorithm is also proposed to reduce the computational complexity. Numerical results show that the proposed algorithms are very effective. Moreover, the power consumption for secure offloading decreases with the increase of the distance between the mobile devices and the eavesdropper.

关键词： Servers   Task analysis   Mobile handsets   Delays   Computational modeling   Processor scheduling   Energy consumption   Conflict graphs   genetic algorithms   mobile edge computing   parallel offloading   sequential offloading  

摘要： In this paper, we consider parallel and sequential task offloading to multiple mobile edge computing servers. The task consists of a set of inter-dependent sub-tasks, which are scheduled to servers to minimize both offloading latency and failure probability. Two algorithms are proposed to solve the scheduling problem, which are based on genetic algorithm and conflict graph models, respectively. Simulation results show that these algorithms provide performance close to the optimal solution, which is obtained through exhaustive search. Furthermore, although parallel offloading uses orthogonal channels, results demonstrate that the sequential offloading yields a reduced offloading failure probability when compared to the parallel offloading. On the other hand, parallel offloading provides less latency. However, as the dependency among sub-tasks increases, the latency gap between parallel and sequential schemes decreases.

关键词： radio access networks   cloud computing   cellular radio   mobile computing   resource allocation   virtual machines   convergence   optimisation   joint optimisation problem   separate resource allocation   communication resources   virtual machine selection algorithm   multihop cloud   mobile edge computing   mobile devices   base station   wireless resources   high transmission delays   multihop path   edge computing resources   cloud radio access network   wireless self-organising network   task delay   elite selection   improved krill herd algorithm  

摘要： For the problem that mobile devices that are far away from the base station obtain limited wireless resources, which causes high transmission delays, the study proposes a multi-hop path to assist users who are far away from the base station to use edge computing resources. And it combines cloud radio access network and wireless self-organising network. The modelling aims to minimise the total energy consumption under the constraints of task delay considering the mobility of mobile devices. Meanwhile, it selects the joint optimisation problem of the path for data transmission and the virtual machine for calculation. This study also introduces the krill herd algorithm and analyses its advantages and disadvantages. The author enhances the global search ability of the algorithm by defining perturbation factors in the random diffusion behaviour and introduces a new strategy of elite selection and retention into the iterative process to improve the convergence accuracy. Finally, the improved krill herd algorithm is used to solve the joint optimisation problem and a better allocation result than the separate resource allocation of calculation and communication is obtained. The experiment proves that the selection algorithm combining virtual machine and routing proposed in this study can achieve the expected results.

关键词： Mobile edge computing   computation offloading   resources allocation   power control  

摘要： In this paper, we study the joint radio and computational resource allocation in the ultra-dense mobile-edge computing networks. In which, the scenario which including both computation offloading and communication service is discussed. That is, some mobile users ask for computation offloading, while the others ask for communication with the minimum communication rate requirements. We formulate the problem as a joint channel assignment, power control and computational resource allocation to minimize the offloading cost of computing offloading, with the precondition that the transmission rate of communication nodes are satisfied. Since the formulated problem is a mixed-integer nonlinear programming (MINLP), which is NP-hard. By leveraging the particular mathematical structure of the problem, i.e., the computational resource allocation variable is independent with other variables in the objective function and constraints, and then the original problem is decomposed into a computational resource allocation subproblem and a joint channel assignment and power allocation subproblem. Since the former is a convex programming, the KKT (Karush-Kuhn-Tucker) conditions can be used to find the closed optimal solution. For the latter, which is still NP-hard, is further decomposed into two subproblems, i.e., the power allocation and the channel assignment, to optimize alternatively. Finally, two heuristic algorithms are proposed, i.e., the Co-channel Equal Power allocation algorithm (CEP) and the Enhanced CEP (ECEP) algorithm to obtain the suboptimal solutions. Numerical results are presented at last to verify the performance of the proposed algorithms.

关键词： Mobile edge computing  

摘要： Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile, Federated learning (FL) has emerged as a promising privacy-preserving approach to facilitating AI applications. However, it remains a big challenge to optimize the efficiency and effectiveness of FL when it is integrated with the MEC architecture. Moreover, the unreliable nature (e.g., stragglers and intermittent drop-out) of end devices significantly slows down the FL process and affects the global model's quality in such circumstances. In this paper, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture. HybridFL adopts two levels (the edge level and the cloud level) of model aggregation enacting different aggregation strategies. Moreover, in order to mitigate stragglers and end device drop-out, we introduce regional slack factors into the stage of client selection performed at the edge nodes using a probabilistic approach without identifying or probing the state of end devices (whose reliability is agnostic). We demonstrate the effectiveness of our method in modulating the proportion of clients selected and present the convergence analysis for our protocol. We have conducted extensive experiments with machine learning tasks in different scales of MEC system. The results show that HybridFL improves the FL training process significantly in terms of shortening the federated round length, speeding up the global model's convergence (by up to 12×) and reducing end device energy consumption (by up to 58%). © 2020, CC BY-NC-ND.