关键词： mobile edge computing   unmanned aerial vehicles   computation offloading   Stackelberg game  

摘要： This paper studies an efficient computing resource offloading mechanism for UAV-enabled edge computing. According to the interests of three different roles: base station, UAV, and user, we comprehensively consider the factors such as time delay, operation, and transmission energy consumption in a multi-layer game to improve the overall system performance. Firstly, we construct a Stackelberg multi-layer game model to get the appropriate resource pricing and computing offload allocation strategies through iterations. Base stations and UAVs are the leaders, and users are the followers. Then, we analyze the equilibrium states of the Stackelberg game and prove that the equilibrium state of the game exists and is unique. Finally, the algorithm's feasibility is verified by simulation, and compared with the benchmark strategy, the Stackelberg game algorithm (SGA) has certain superiority and robustness.

关键词： Task analysis   Games   Cloud computing   Heuristic algorithms   Statistics   Sociology   Load management   Task offloading   population game   evolutionary dynamics   nash equilibrium   mobile edge computing   Internet of Things  

摘要： Mobile edge computing (MEC) is an emerging paradigm that provides radio access networks with augmented resources to meet the requirements of Internet of Things (IoT) services. MEC allows IoT devices to offload delay sensitive and computation intensive tasks to edge clouds deployed at base stations (BSs). Offloading tasks to edge clouds can alleviate the computing and battery limitations of IoT devices. However, task offloading in MEC for IoT may face serious transmission latency and computation latency problems with massive number of IoT devices. Moreover, some edge clouds can be overloaded due to the spatially inhomogeneous distributions of IoT tasks. To solve these problems, we investigate the workload balancing problems to minimize the transmission latency and computation latency in task offloading process while considering the limited bandwidth resources of BSs and computation resources in edge clouds. We formulate the workload balancing problem as a population game in order to analyze the aggregate offloading decisions. We analyze the aggregate offloading decisions of mobile users through evolutionary game dynamics and show that the game always achieves a Nash equilibrium (NE). We further propose two workload balancing algorithms based on evolutionary dynamics and revision protocols. Simulation results show that our proposed workload balancing algorithms can achieve better performance than existing solutions.

关键词： mobile edge computing   wireless metropolitan area network   workflow application   cloudlet placement   computation offloading  

摘要： With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud center is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific form of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on workflow applications (WAs) in wireless metropolitan area networks (WMANs). We devise a cloudlet placement strategy based on a particle swarm optimization algorithm using genetic algorithm operators with the encoding library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlet placement scheme. Compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04-44.99%.

关键词： QoS prediction   Tensor factorization   Time series   Collaborative filtering   Mobile edge computing  

摘要： Withthe rapid development of mobile Internet and wireless technologies, edge servers in mobile edge computing (MEC) provide a large number of mobile services to develop advanced and complex mobile applications. Quality of Service (QoS) is an essential indicator for recommendation and selection from such a huge number of candidate mobile services. However, QoS records are sparse in reality because users only invoke certain mobile services. How to predict missing QoS values is a key issue in operations related to mobile services. Some collaborative filtering (CF) methods have been used to solve QoS prediction problem of traditional Web services. Neverless, the high dimensionality, high sparsity and dynamic variability of QoS data in MEC limit the application of these methods. This paper proposes a temporal regularized tensor factorization method based on hybrid CF for QoS prediction of mobile services. The overall structure of QoS data is represented by constructing a user-service-time tensor model. As QoS data possesses certain characteristics of time series, a temporal regular term is introduced into CP decomposition to mine the relationship between QoS values in adjacent time intervals. The alternating least square (ALS) algorithm is used to optimize the approximate tensor iteratively. The low-rank estimation of the target tensor is obtaind to predict missing values. Extensive experimental studies on the large-scale QoS dataset WS-Dream show that our method has significantly better prediction accuracy than the other nine state-of-the-art methods under different data densities. Especially in the case of sparse data, the prediction effect is more prominent, and the accuracy increases up to 62.4% at most.

关键词： Servers   Task analysis   Resource management   Delays   Computational modeling   Minimization   Smart glasses   Mobile edge computing   partial program offloading   program partitioning   resource allocation  

摘要： This paper considers a practical mobile edge computing (MEC) system, where edge server does not pre-install the program required to perform user offloaded computing tasks. A partial program offloading (PPO) scheme is proposed, which can divide a user program into two parts, where the first part is executed by the user itself and the second part is transferred to an edge server for remote execution. However, the execution of the latter part requires the results of the previous part (called intermediate result) as the input. We aim to minimize the overall time consumption of a multi-server MEC system to complete all user offloaded tasks. It is modeled as a mixed integer nonlinear programming (MINLP) problem which considers user-and-server association, program partitioning, and communication resource allocation in a joint manner. An effective algorithm is developed to solve the problem by exploiting its structural features. First, the task completion time of a single server is minimized given the computing workload and available resource. Then, the working time of the edge servers are balanced by updating user-and-server association and communication resource allocation. Numerical results show that significant performance improvement can be achieved by the proposed scheme.

关键词： Servers   Task analysis   Pricing   Games   Energy consumption   Resource management   Dynamic scheduling   Mobile edge computing   computation offloading   resources pricing   Stackelberg game  

摘要： Owing to the limited computing resources at the MEC server, reasonable strategies of resources pricing and task offloading are necessary to be designed. In this letter, a scenario of multi-users and single MEC server with limited computing resources is studied, where each end-user has a divisible task which could be offloaded portion to the MEC server for execution, both local and offloaded portions of a task cannot be instantiated for execution at the same time, and a stackelberg game based pricing and offloading is investigated. The game is established based on the relationship between the MEC server resources pricing and task offloading quantities, and the existence of the stackelberg equilibrium is proved. Differential evolution algorithm is then used to find the best strategies of the resources pricing for the MEC server and task offloading quantities for end-users. Simulation results show that the investigations can improve the profit of the MEC server and the utility of end-users, which result in the win-win situation.

关键词： multimedia applications   resource allocation   mobile edge computing  

摘要： Due to the advancements of information technologies and the Internet of Things (IoT), the number of distributed sensors and IoT devices in the social IoT (SIoT) systems is proliferating. This has led to various multimedia applications, face recognition and augmented reality (AR). These applications are computation-intensive and delay-sensitive and have become popular in our daily life. However, IoT devices are well-known for their constrained computational resources, which hinders the execution of these applications. Mobile edge computing (MEC) has appeared and been deemed a prospective paradigm to solve this issue. Migrating the applications of IoT devices to be executed in the edge cloud can not only provide computational resources to process these applications but also lower the transmission latency between the IoT devices and the edge cloud. In this paper, computation resource allocation and multimedia applications offloading in MEC-assisted SIoT systems are investigated. We aim to optimize the resource allocation and application offloading by jointly minimizing the execution latency of multimedia applications and the consumed energy of IoT devices. The studied problem is a formulation of the total computation overhead minimization problem by optimizing the computational resources in the edge servers. Besides, as the technology of dynamic voltage scaling (DVS) can offer more flexibility for the MEC system design, we incorporate it into the application offloading. Since the studied problem is a mixed-integer nonlinear programming (MINP) problem, an efficient method is proposed to address it. By comparing with the baseline schemes, the theoretic analysis and simulation results demonstrate that the proposed multimedia applications offloading method can improve the performances of MEC-assisted SIoT systems for the most part.

关键词： Mobile edge computing   Quality of service   Multi-layer optimization   Data caching   Resource allocation  

摘要： Mobile edge computing (MEC) is a key feature of next generation heterogeneous networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. In this research we investigated on connection management in multi-access edge computing systems using intelligent offloading and caching strategy. This paper presents joint QoS-aware resource allocation and mobile edge computing in multi-access heterogeneous networks in order to maximize total system's energy efficiency in addition to guaranty the users QoS requirements. Firstly, based on the multi-server MEC scenario, a new goal function is designed considering calculation and communication models, in order to decreasing the completion time of all computing tasks and achieving optimal energy efficiency under delay constraints. Then, the continues carrier allocation and user association variables in addition to the interference coordination incorporated in the goal function, modifies the primary optimization problem to a mixed integer nonlinear programming (MINLP). Also, considering user's minimum data rate and maximum transmission power constraints, a carrier-matching algorithm is introduced to obtain the optimal channel allocation strategy, which first matches the user with the channel and subsequently, the Dinkelbach-like method is applied to obtain the optimal resource allocation. Based on the simulations, the proposed approach not only achieves higher energy efficiency but also enhances the total network throughput in multiple-sources scenarios.

关键词： Task offloading   mobile edge computing   Internet of Vehicles   task classification   offloading nodes selection  

摘要： The Mobile Edge Computing (MEC)-based task offloading in the Internet of Vehicles (IoV) scenario, which transfers computational tasks to mobile edge nodes and fixed edge nodes with available computing resources, has attracted interest in recent years. The MEC-based task offloading can achieve low latency and low operational cost under the tasks delay constraints. However, most existing research generally focuses on how to divide and migrate these tasks to the other devices. This research ignores delay constraints and offloading node selection for different tasks. In this article, we design the MEC-enabled IoV architecture, in which all vehicles and MEC servers act as offloading nodes. Mobile offloading nodes (i.e., vehicles) and fixed offloading nodes (i.e., MEC servers) provide low latency offloading services cooperatively through roadside units. Then we propose the task offloading scheme that considers task classification and offloading nodes selection (TO-TCONS). Our goal is to minimize the total execution time of tasks. In TO-TCONS Scheme, we divide the task offloading into the same region offloading mode and cross-region offloading mode, which is based on the delay constraints of tasks and the travel time of the target vehicle. Moreover, we propose the mobile offloading nodes selection strategy to select offloading nodes for each task, which evaluates offloading candidates for each task based on computing resources and transmission rates. Simulation results demonstrate that TO-"ICONS Scheme is indeed capable of reducing total latency of tasks execution under the delay constraints in MEC-enabled IoV.

关键词： Task analysis   Servers   6G mobile communication   Costs   Optimization   Computer architecture   Stochastic processes   Mobile edge computing   cybertwin   task offloading   hybrid energy supply   stochastic optimization  

摘要： With the development of 6G wireless communication technologies, various resource-intensive and delay-sensitive vehicle application tasks are generated. These application tasks can be offloaded to Mobile Edge Computing (MEC) which deploys computing resources at the edge of networks. Besides, the recent proposed Cybertwin, as the digital representation of the complicated physical end-systems, can help the terminals obtain the required services from networks. Vehicles enabled by Cybertwin can offload their tasks to MEC and achieve better performance. In this paper, we focus on the study of a hybrid energy-powered multi-server MEC system with Cybertwin. Vehicles enabled by Cybertwin and edge servers send the current network status and unprocessed vehicle application tasks to the macro base station (MBS) to achieve the better allocation of resources. Energy harvesting (EH) devices are deployed on edge servers to form a "green energy-grid" hybrid energy supply model. We formulate a stochastic offloading optimization problem, and the goal is to minimize the system cost. The stochastic optimization problem is decomposed into three sub-problems. Then, we design an efficient multi-vehicle task offloading (EMT) algorithm to achieve the trade-off between system cost and task queue length. Theoretical analysis shows that EMT algorithm can optimize the total cost of the MEC system and guarantee the system performance. According to experimental evaluation, we verify the performance of the EMT algorithm.