关键词： mobile edge computing   low-latency network   prototype   experiment  

摘要： The Internet of Things (IoT) with its support for cyber-physical systems (CPS) will provide many latency-sensitive services that require very fast responses from network services. Mobile edge computing (MEC), one of the distributed computing models, is a promising component of the low-latency network architecture. In network architectures with MEC, mobile devices will offload heavy computing tasks to edge servers. There exist numbers of researches about low-latency network architecture with MEC. However, none of the existing researches simultaneously satisfy the followings: (1) guarantee the latency of computing tasks and (2) implement a real system. In this paper, we designed and implemented an MEC based network architecture that guarantees the latency of offloading tasks. More specifically, we first estimate the total latency including computing and communication ones at the centralized node called orchestrator. If the estimated value exceeds the latency requirement, the task will be rejected. We then evaluated its performance in terms of the blocking probability of the tasks. To analyze the results, we compared the performance between obtained from experiments and simulations. Based on the comparisons, we clarified that the computing latency estimation accuracy is a significant factor for this system.

关键词： Fiber-Wireless access network   mobile edge computing   energy efficiency   two-layer Stackelberg game  

摘要： Carbon footprints of both computation and communications infrastructures attract growing public attention, raising concerns about global warming and motivating research efforts in developing energy-efficient solutions. Since Fiber-Wireless (FiWi) access networks have succeeded in interconnecting our digitalized world and mobile/multi-access edge computing (MEC) further removes unnecessary data transmission from optical backbone networks, MEC enhanced FiWi emerges as the win-win architecture for resource-hungry and delay-sensitive mobile apps. However, the assumption of unlimited and free resources offered by FiWi infrastructure is impractical, and energy-efficient offloading mechanisms for MEC enhanced FiWi still remain underexplored, conflicting with globally advocated green communications. This motivates us to design the Two-layer Stackelberg Game based Offloading (TSGO) strategy, including the FiWi layer and mobile edge computation offloading (MeCo) layer subgames capturing bandwidth allocation and offloading decision respectively. Then the backward induction and an iterative algorithm are exploited to acquire the Nash equilibrium of the two-layer Stackelberg game. Furthermore, we propose three energy efficiency benchmarks from both user-side and system-wide perspectives to evaluate mechanisms proposed for, but not limited to, FiWi, MEC, or a combination thereof. Simulation results validate the proposed TSGO strategy, which echoes the profound notion of green communications.

关键词： Mobile edge computing   computation offloading   energy harvesting   game theory   queueing theory   Karush-Kuhn-Tucker condition  

摘要： Energy harvesting aided mobile edge computing (MEC) has gained much attention for its widespread application in the computation-intensive, latency-sensitive and energy-hungry scenario. Computation offloading, which leverages powerful MEC servers (MEC-ss) to augment the computing capability of less powerful mobile devices (MDs), is intrinsically a distributed computing over heterogeneous MEC networks. In this article, computation offloading from multi-MD to multi-MEC-s in heterogeneous MEC systems with energy harvesting is investigated from a game theoretic perspective. The objective is to minimize the average response time of an MD that consists of communication time, waiting time and processing time. M/G/1 queueing models are established for MDs' computation generation and MEC-ss' computation task receiving. The interference among MDs, the randomness in computation task generation, harvested energy arrival, wireless channel state, queueing at the MEC-s, and the power budget constraint of an MD are taken into consideration. A noncooperative computation offloading game is formulated. The action is a vector that denotes the amount of computation tasks offloaded to all MEC-ss (the element value can be zero) and local process. We give the definition and existence analysis of the Nash equilibrium (NE). Furthermore, we reconstruct the optimization problem of an MD. A 2-step decomposition is presented and performed. Thereby, we arrive at a one-dimensional search problem and a greatly shrunken sub-problem. The sub-problem is nonconvex, but its Karush-Kuhn-Tucker (KKT) conditions have finite solutions. We can obtain the optimal solution of the sub-problem by seeking the finite solutions. Thereafter, a distributive NE-orienting iterated best-response algorithm is designed. Simulations are carried out to illustrate the convergence performance and effectiveness of the proposed algorithm.

关键词： 5G   mobile edge computing   V2X communication   cooperative perception   intelligent transport systems  

摘要： Vehicle-to-everything (V2X) communications enable real-time information exchange between vehicles and infrastructure, which extends the perception range of vehicles beyond the limits of on-board sensors and, thus, facilitating the realisation of cooperative, connected, and automated mobility (CCAM) services that will improve road safety and traffic efficiency. In the context of CCAM, the successful deployments of cooperative intelligent transport system (C-ITS) use cases, with the integration of advanced wireless communication technologies, are effectively leading to make transport safer and more efficient. However, the evaluation of multi-vendor and multi-protocol based CCAM service architectures can become challenging and complex. Additionally, conducting on-demand field trials of such architectures with real vehicles involved is prohibitively expensive and time-consuming. In order to overcome these obstacles, in this paper, we present the development of a standards-compliant experimental vehicular on-board unit (OBU) that supports the integration of multiple V2X protocols from different vendors to communicate with heterogeneous cloud-based services that are offered by several original equipment manufacturers (OEMs). We experimentally demonstrate the functionalities of the OBU in a real-world deployment of a cooperative collision avoidance service infrastructure that is based on edge and cloud servers. In addition, we measure end-to-end application-level latencies of multi-protocol supported V2X information flows to show the effectiveness of interoperability in V2X communications between different vehicle OEMs.

关键词： Task analysis   Bayes methods   Games   Resource management   Machine learning   Protocols   Bayesian methods   blockchains   deep learning   game theory   incomplete information   machine learning   mobile edge computing   partially-observable Markov decision process   reinforcement learning   resource management  

摘要： We present a novel game-theoretic, Bayesian reinforce-ment learning (RL) and deep learning (DL) framework to represent interactions of miners in public and consortium blockchains with mobile edge computing (MEC). Within the framework, we formulate a stochastic game played by miners under incomplete information. Each miner can offload its block operations to one of the base stations (BSs) equipped with the MEC server. The miners select their offloading BSs and block processing rates simultaneously and independently, without informing other miners about their actions. As such, no miner knows the past and current actions of others and, hence, constructs its belief about these actions. Accordingly, we devise a Bayesian RL algorithm based on the partially-observable Markov decision process for miner's decision making that allows each miner to dynamically adjust its strategy and update its beliefs through repeated interactions with each other and with the mobile environment. We also propose a novel unsupervised Bayesian deep learning algorithm where the uncertainties about unobservable states are approximated with Bayesian neural networks. We show that the proposed Bayesian RL and DL algorithms converge to the stable states where the miners' actions and beliefs form the perfect Bayesian equilibrium (PBE) and myopic PBE, respectively.

关键词： Mobile edge computing   Offloading   Precedence constraints   Resource utilization  

摘要： Efficient task scheduling improves offloading performance in mobile edge computing (MEC) environment. The jobs offloaded by different users would have different dependent tasks with diverse resource demands at different times. Meanwhile, due to the heterogeneity of edge servers configurations in MEC, offloaded jobs may frequently have placement constraints, restricting them to run on a particular class of edge servers meeting specific software running settings. This spatio-temporal information gives the opportunity to improve the resource utilization of the computing system. In this paper, we study the scheduling method for the jobs consisting of dependent tasks offloaded by different users in MEC. A new task offloading scheduler, Horae, is proposed to not only improve the resource utilization of MEC environment but also guarantees to select the edge server which could satisfy placement constraints for each offloaded task. Concretely, considering the fact that each job would experience slack time as a result of competing for limited resource with other jobs in MEC, Horae minimizes the sum of all slack time values of all the jobs while guaranteeing placement constraints, and therefore improve the resource utilization of the system. Horae was validated for its feasibility and efficiency by means of extensive experiments, which are presented in this paper.

关键词： Computation offloading   energy consumption   execution time   fog computing   heuristic algorithm   mobile edge computing   mobile user   performance evaluation  

摘要： The investigation in this article makes the following important contributions to combinatorial optimization of computation offloading in fog computing. First, we rigorously define the two problems of optimal computation offloading with energy constraint and optimal computation offloading with time constraint. We do this in such a way that between execution time and energy consumption, we can fix one and minimize the other. We prove that our optimization problems are NP-hard, even for very special cases. Second, we develop a unique and effective approach for solving the proposed combinatorial optimization problems, namely, a two-stage method. In the first stage, we generate a computation offloading strategy. In the second stage, we decide the computation speed and the communication speeds. This method is applicable to both optimization problems. Third, we use a simple yet efficient greedy method to produce a computation offloading strategy by taking all aspects into consideration, including the properties of the communication channels, the power consumption models of computation and communication, the tasks already assigned and allocated, and the characteristics of the current task being considered. Fourth, we experimentally evaluate the performance of our heuristic algorithms. We observe that while various heuristics do exhibit noticeably different performance, there can be a single and simple heuristic that can perform very well. Furthermore, the method of compound algorithm can be applied to obtain slightly improved performance. Fifth, we emphasize that our problems and algorithms can be easily extended to study combined performance and cost optimization (such as cost-performance ratio and weighted cost-performance sum optimization) and to accommodate more realistic and complicated fog computing environments (such as preloaded mobile edge servers and multiple users) with little extra effort. To the best of our knowledge, there has been no similar study in the exis

关键词： Task analysis   Servers   NOMA   Resource management   Collaboration   Energy consumption   Silicon carbide   Multiple radio access   mobile edge computing   nonorthogonal multiple access   resource allocation   task offloading  

摘要： Multi-access mobile edge computing (MEC) allows each user to offload partial task bits to multiple MEC servers via multi-access radio transmission, enabling the collaboration of edge computing. By leveraging the advantage of nonorthogonal multiple access (NOMA) in improving the transmission efficiency, it is expected to effectively reduce the energy consumption of users in multi-access MEC with the aid of NOMA. However, considering the co-channel interference of NOMA and computation collaboration among MEC servers, the joint task offloading and resource allocation is a challenging problem. In this article, with the objective to minimize the weighted sum energy of users, we first propose optimal task offloading and resource allocation algorithms for the special single-user (OTORA-SU) and general multi-user (OTORA-MU) cases, by exploiting the convex and layered structure of the formulated problem. Interestingly, it is found that the single user always preferentially offloads task bits to the MEC servers with better channel gains, regardless of the computation capacity of each MEC server. Then, a low-complexity algorithm (LTORA-MU) is proposed for the general multi-user case, which converges fast and achieves near-optimal performances. Considering the channel estimation error, the impacts of imperfect channel state information (CSI) and successive interference cancellation (SIC) are also investigated. Simulation results demonstrate that for both perfect and imperfect CSI and SIC, 1) the proposed OTORA-SU and LTORA-MU outperform the local computing, full offloading, FDMA-based offloading and non-collaborative MEC schemes;2) as the number of MEC servers grows, the ratio of offloading task bits increases while the energy consumption is decreased.

关键词： mobile edge computing   Wireless Power Transmission (WPT)   task offloading   joint optimization   resource allocation  

摘要： The combination of mobile edge computing (MEC) and wireless power transfer (WPT) is recognized as a promising technology to solve the problem of limited battery capacities and insufficient computation capabilities of mobile devices. This technology can transfer energy to users by radio frequency (RF) in wireless powered mobile edge computing. The user converts the harvested energy, stores it in the battery, and utilizes the harvested energy to execute corresponding local computing and offloading tasks. This paper adopts the Frequency Division Multiple Access (FDMA) technique to achieve task offloading from multiple mobile devices to the MEC server simultaneously. Our objective is to study multiuser dynamic joint optimization of computation and wireless resource allocation under multiple time blocks to solve the problem of maximizing residual energy. To this end, we formalize it as a nonconvex problem that jointly optimizes the number of offloaded bits, energy harvesting time, and transmission bandwidth. We adopt convex optimization technology, combine with Karush-Kuhn-Tucker (KKT) conditions, and finally transform the problem into a univariate constrained convex optimization problem. Furthermore, to solve the problem, we propose a combined method of Bisection method and sequential unconstrained minimization based on Reformulation-Linearization Technique (RLT). Numerical results demonstrate that the performance of our joint optimization method outperforms other benchmark schemes for the residual energy maximization problem. Besides, the algorithm can maximize the residual energy, reduce the computation complexity, and improve computation efficiency.

关键词： Delay effects   Energy consumption   Resource management   Task analysis   Precoding   MIMO communication   Servers   Computation offloading   mobile edge computing   MU-MIMO   precoding   resource allocation  

摘要： Mobile edge computing is considered as a promising method to release the computation burden of mobile devices (MDs) by transferring the computation tasks to the nearby edge server. In this paper, we address the computation offloading problem by jointly optimizing offloading-decision making, multi-user multiple input and multiple output (MU-MIMO) precoding and computation resource allocation. The optimization problem is formulated as the minimization of the weighted sum of energy consumption and time delay of MDs, which is a mixed-integer non-linear programming problem. Due to the complexity of offloading time delay, we consider two special cases namely, the lower bound and upper bound of offloading time delay for the original problem, and exploit semidefinite relaxation and rounding methods to obtain the offloading decisions. Specially, we adopt the quadratic transform based fractional programming and the weighted minimum mean square error methods to solve the MU-MIMO precoding design problem for the two cases of offloading time delay, respectively. Simulation results confirm the effectiveness of the proposed method, and show that the application of multi-antenna MU-MIMO communication into MEC can sufficently reduce the energy consumption and time delay during computation offloading.