关键词： Mobile edge computing   Deep learning   Auction   Delay and energy sensitive tasks  

摘要： Mobile edge computing has emerged as a promising paradigm to complement the computing and energy resources of mobile devices. In this computing paradigm, mobile devices offload their computing tasks to nearby edge servers, which can potentially reduce their energy consumption and task completion delay. In exchange for processing the computing tasks, edge servers expect to receive a payment that covers their operating costs and allows them to make a profit. Unfortunately, existing works either ignore the payments to the edge servers, or ignore the task processing delay and energy consumption of the mobile devices. To bridge this gap, we propose an auction to allocate edge servers to mobile devices that is executed by a pair of deep neural networks. Our proposed auction maximizes the profit of the edge servers, and satisfies the task processing delay and energy consumption constraints of the mobile devices. The proposed deep neural networks also guarantee that the mobile devices are unable to unfairly affect the results of the auctions. Our extensive simulations show that our proposed auction mechanism increases the profit of the edge servers by at least 50% compared to randomized auctions, and satisfies the task processing delay and energy consumption constraints of mobile devices.

关键词： quality of service   mobile computing   greedy algorithms   genetic algorithms   resource allocation   cloud computing   metropolitan area networks   QoS-aware edge server placement method   wireless metropolitan area networks   mobile edge computing   load balancing problem   edge servers   greedy algorithm  

摘要： Mobile edge computing (MEC) is concerned with moving complex tasks from data sources to nearby computing resources, which can reduce computing latency and remote cloud workload. Although there has been significant research in the field of MEC, research on edge server placement in wireless metropolitan area networks (WMANs) is overlooked, and the load balancing problem of edge servers is seldom discussed. From a practical perspective, how to place edge servers efficiently in WMANs while considering load balancing between edge servers is studied. A greedy algorithm is proposed that can balance the workload of edge servers more effectively. However, the performance of the greedy algorithm as the number of servers placed increases is not ideal. Therefore, the authors combine the greedy algorithm with a genetic algorithm (GA) to minimise the number of edge servers while ensuring load balancing between edge servers and quality of service (QoS) requirements for mobile users. Finally, they conduct simulation experiments and compare the proposed algorithms with other algorithms. The improved GA proposed is superior to the greedy algorithm in terms of load balancing and the number of servers. The experimental results demonstrate that the algorithm has excellent performance.

关键词： Mobile edge computing  

摘要： In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realistic utility value for MESs. Moreover, we improve upon some general algorithms, used for resource allocation in MEC and cloud computing, by considering our proposed utility function. We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes. The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources. The utility function depends upon the UE requests and the distance between UEs and MES, and serves as a realistic means of comparison between different types of UE requests. Choosing (or selecting) an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task. We show that MES resource allocation is sub-optimal if CPU is the only resource considered. By taking into account the other resources, i.e., RAM, disk space, request time, and distance in the utility function, we demonstrate improvement in the resource allocation algorithms in terms of service rate, utility, and MES energy *** Codes 46Fxx © 2020, CC BY.

关键词： Mobile edge computing  

摘要： Mobile-edge computing (MEC) offloads computational tasks from wireless devices to network edge, and enables real-time information transmission and computing. Most existing work concerns a small-scale synchronous MEC system. In this paper, we focus on a large-scale asynchronous MEC system with random task arrivals, distinct workloads, and diverse deadlines. We formulate the offloading policy design as a restless multi-armed bandit (RMAB) to maximize the total discounted reward over the time horizon. However, the formulated RMAB is related to a PSPACE-hard sequential decision-making problem, which is intractable. To address this issue, by exploiting the Whittle index (WI) theory, we rigorously establish the WI indexability and derive a scalable closed-form solution. Consequently, in our WI policy, each user only needs to calculate its WI and report it to the BS, and the users with the highest indices are selected for task offloading. Furthermore, when the task completion ratio becomes the focus, the shorter slack time less remaining workload (STLW) priority rule is introduced into the WI policy for performance improvement. When the knowledge of user offloading energy consumption is not available prior to the offloading, we develop Bayesian learning-enabled WI policies, including maximum likelihood estimation, Bayesian learning with conjugate prior, and prior-swapping techniques. Simulation results show that the proposed policies significantly outperform the other existing policies. © 2020, CC BY-NC-ND.

关键词： Mobile multimedia service   Mobile edge computing   Quality-of-Experience   Service instance deployment   Binary particle swarm optimization   User satisfaction   5G network  

摘要： Leveraging cloud infrastructure to the mobile edge computing helps the mobile users to get real time multimedia services in Fifth Generation (5G) network system. To ensure higher Quality-of-Experience (QoE), faster migration of mobile multimedia service instances is required to cope up with user mobility. By deploying the mobile multimedia service instances proactively in multiple edge nodes (ENs) helps the users to get higher QoE. However, excessive deployment of service replicas might increase the cost of the overall network. To establish trade-off between these two conflicting objectives, we have formulated the problem as a Multi-objective Integer Linear Programming (MILP) by integrating the users' path prediction model. This problem is proven to be an NP-hard one for large networks, thus we develop an artificial intelligence (AI) based meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to achieve near-optimal solution within polynomial time. The performance analysis results show the significant performance improvement in terms of QoE and user satisfaction as compared to other state-of-the-art works.

关键词： Computation offloading   Mobile edge computing   Machine learning   Reinforcement learning   Supervised learning   Unsupervised learning  

摘要： With the rapid developments in emerging mobile technologies, utilizing resource-hungry mobile applications such as media processing, online Gaming, Augmented Reality (AR), and Virtual Reality (VR) play an essential role in both businesses and entertainments. To soften the burden of such complexities incurred by fast developments of such serving technologies, distributed Mobile Edge Computing (MEC) has been developed, aimed at bringing the computation environments near the end-users, usually in one hop, to reach predefined requirements. In the literature, offloading approaches are developed to connect the computation environments to mobile devices by transferring resource-hungry tasks to the near servers. Because of some rising problems such as inherent software and hardware heterogeneity, restrictions, dynamism, and stochastic behavior of the ecosystem, the computation offloading issues consider as the essential challenging problems in the MEC environment. However, to the best of the author's knowledge, in spite of its significance, in machine learning-based (ML-based) computation offloading mechanisms, there is not any systematic, comprehensive, and detailed survey in the MEC environment. In this paper, we provide a review on the ML-based computation offloading mechanisms in the MEC environment in the form of a classical taxonomy to identify the contemporary mechanisms on this crucial topic and to offer open issues as well. The proposed taxonomy is classified into three main fields: Reinforcement learning-based mechanisms, supervised learning-based mechanisms, and unsupervised learning-based mechanisms. Next, these classes are compared with each other based on the essential features such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and weaknesses are discussed, as well. Finally, open issues and uncovered or inadequately covered future research challenges are argued, and the survey is concluded.

关键词： 5G and B5G communication   UAVs   Artificial intelligence   Machine learning   Mobile edge computing   Software-defined networks   Internet of Things   mmWave communication   Smart city   Joint optimization  

摘要： Recently, unmanned aerial vehicles (UAVs) have gained notable interest in various applications such as wireless coverage, aerial surveillance, precision agriculture, construction, power lines monitoring and blood delivery, etc. The UAVs implicit attributes e.g., rapid deployment, quick mobility, increase in flight duration, improvements in payload capacities, etc. , place it as an effective candidate for many applications in 5G and Beyond communications. The UAVs-assisted next-generation communications are determined to be highly influenced by various techniques and technologies like artificial intelligence (AI), machine learning (ML), deep reinforcement learning (DRL), mobile edge computing (MEC), and software-defined networks (SDN). In this article, we develop a review to investigate the UAVs joint optimization problems to enhance system efficiency. We classify the joint optimization problems based on the number of parameters used in proposed optimization problems. Moreover, we explore the impact of AI, ML, DRL, MEC, and SDN over UAVs joint optimization problems and present future research challenges and directions.

关键词： Mobile edge computing  

摘要： The development of mobile services has impacted a variety of computation-intensive and time-sensitive applications, such as recommendation systems and daily payment methods. However, computing task competition involving limited resources increases the task processing latency and energy consumption of mobile devices, as well as time constraints. Mobile edge computing (MEC) has been widely used to address these problems. However, there are limitations to existing methods used during computation offloading. On the one hand, they focus on independent tasks rather than dependent tasks. The challenges of task dependency in the real world, especially task segmentation and integration, remain to be addressed. On the other hand, the multiuser scenarios related to resource allocation and the mutex access problem must be considered. In this paper, we propose a novel offloading approach, Com-DDPG, for MEC using multiagent reinforcement learning to enhance the offloading performance. First, we discuss the task dependency model, task priority model, energy consumption model, and average latency from the perspective of server clusters and multidependence on mobile tasks. Our method based on these models is introduced to formalize communication behavior among multiple agents;then, reinforcement learning is executed as an offloading strategy to obtain the results. Because of the incomplete state information, long short-term memory (LSTM) is employed as a decision-making tool to assess the internal state. Moreover, to optimize and support effective action, we consider using a bidirectional recurrent neural network (BRNN) to learn and enhance features obtained from agents’ communication. Finally, we simulate experiments on the Alibaba cluster dataset. The results show that our method is better than other baselines in terms of energy consumption, load status and latency. © 2020, CC BY.

关键词： Mobile edge computing   Cooperative computing   Internet of Things   Stochastic offloading control  

摘要： With the diversity of the communication technology and the heterogeneity of the computation resources at network edge, both the edge cloud and peer devices (collaborators) can be scavenged to provide computation resources for the resource-limited Internet-of-Things (IoT) devices. In this paper, a novel cooperative computing paradigm is proposed, in which the computation resources of IoT device, opportunistically idle collaborators and dedicated edge cloud are fully exploited. Computation/offloading assistance is provided by collaborators at idle/busy states, respectively. Considering the channel randomness and opportunistic computation resource share of collaborators, we study the stochastic offloading control for an IoT device, regarding how much computation load is processed locally, offloaded to the edge cloud and a collaborator. The problem is formulated into a finite horizon Markov decision problem with the objective of minimizing the expected total energy consumption of the IoT device and the collaborator, subject to satisfying the hard computation deadline constraint. Optimal offloading policy is derived based on the stochastic optimization theory, which demonstrates that the energy consumption can be reduced by a proportional factor through the cooperative computing. More energy saving is achieved with better wireless channel condition or higher computation energy efficiency of collaborators. Simulation results validate the optimality of the proposed policy and the efficiency of the cooperative computing between end devices and edge cloud, compared to several other offloading schemes.

关键词： Multiple unmanned aerial vehicles   Mobile edge computing   Trajectory planning   Differential evolution   k-means clustering algorithm   Greedy method  

摘要： We study a mobile edge computing system assisted by multiple unmanned aerial vehicles(UAVs),where the UAVs act as edge servers to provide computing services for Internet of Things *** goal is to minimize the energy consumption of this system by planning the trajectories of *** problem is difficult to address because when planning the trajectories,we need to consider not only the order of stop points(SPs),but also their deployment(including the number and locations)and the association between UAVs and *** tackle this problem,we present an energy-efficient trajectory planning algorithm(TPA)which comprises three *** the first phase,a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same *** the second phase,the k-means clustering algorithm is employed to group the given SPs into a set of clusters,where the number of clusters is equal to th at of UAVs and each cluster contains all SPs visited by the same *** the third phase,to quickly generate the trajectories of UAVs,we propose a low-complexity greedy method to construct the order of SPs in each *** with other algorithms,the effectiveness of TPA is verified on a set of instances at different scales.