关键词： Trajectory   Task analysis   Energy consumption   Resource management   Trajectory planning   Genetic algorithms   Processor scheduling   Mobile edge computing   unmanned aerial vehicle   evolutionary algorithm   multi-chrome genetic algorithm  

摘要： This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs (variable number of UAVs) are deployed to serve Internet of Things devices (IoTDs). We aim to minimize the sum of hovering and flying energies of UAVs by optimizing the trajectories of UAVs. The problem is very complicated as we have to consider the deployment of stop points (SPs), the association between UAVs and SPs, and the order of SPs for UAVs. To solve the problem, this paper proposed a novel genetic trajectory planning algorithm with variable population size (GTPA-VP), which consists of two phases. In the first phase, a genetic algorithm (GA) with a variable population size is used to update the deployment of SPs. Accordingly, a multi-chrome GA is adopted to find the association between UAVs and SPs, an optimal number of UAVs, and the optimal order of SPs for UAVs. The effectiveness of the proposed GTPA-VP is demonstrated through several experiments on a set of ten instances with up to 200 IoTDs. It is evident from the experimental results that the proposed GTPA-VP outperforms the benchmark algorithms in terms of the energy consumption of the system.

关键词： Streaming media   Protocols   Internet   Servers   Quality of service   Quality of experience   Overlay networks   Internet of Multimedia Things   multihoming   multipath   mobile edge computing   fog computing   VR 360 video  

摘要： The focus of this survey is to study the protocols, mechanisms, and the latest standards proposed in the literature for improving the performance and quality of video content in multipath and multihomed overlay networks. Multipath is a broader term, but in the context of this survey, we define multipath as enhancing network routing technique by using various paths that are not necessarily completely disjoint. Multipath can furnish a variety of advantages such as reliability,connection persistence, increased perceived throughput and load balancing. On the other hand, multihoming is the ability to use multiple network interfaces when connecting to the Internet to increase reliability, resilience, and performance. Most existing surveys are specialized in one specific domain area related to multipath or multihoming. This study covers the research proposals at the different layers/sublayers of an overlay network from transport to the application and extends to cover the latest technologies like machine learning, Fog and Mobile Edge computing, VR 360 video, and the Internet of Multimedia Things (IoMT). As such, our work tries to be as comprehensive as possible to relate multipath and multihoming research solutions for video streaming to the current and emerging video streaming technologies.

关键词： Task analysis   Cloud computing   Processor scheduling   Scheduling   Energy consumption   Computational modeling   Wireless communication   Mobile edge computing   task offloading   resource scheduling   optimization algorithm   deep reinforcement learning  

摘要： Computation-intensive mobile applications are explosively increasing and cause computation overload for smart mobile devices (SMDs). With the assistance of mobile edge computing and mobile cloud computing, SMDs can rent computation resources and offload the computation-intensive applications to edge clouds and remote clouds, which reduces the application completion delay and energy consumption of SMDs. In this paper, we consider the mobile applications with task call graphs and investigate the task offloading and resource scheduling problem in hybrid edge-cloud networks. Due to the interdependency of tasks, time-varying wireless channels, and stochastic available computation resources in the hybrid edge-cloud networks, it is challenging to make task offloading decisions and schedule computation frequencies to minimize the weighted sum of energy, time, and rent cost (ETRC). To address this issue, we propose two efficient algorithms under different conditions of system information. Specifically, with full system information, the task offloading and resource scheduling decisions are determined based on semidefinite relaxation and dual decomposition methods. With partial system information, we propose a deep reinforcement learning framework, where the future system information is inferred by long short-term memory networks. The discrete offloading decisions and continuous computation frequencies are learned by a modified deep deterministic policy gradient algorithm. Extensive simulations evaluate the convergence performance of ETRC with various system parameters. Simulation results also validate the superiority of the proposed task offloading and resource scheduling algorithms over baseline schemes.

ISBN: (纸本)3030839435;9783030839437

关键词： Deep Reinforcement Learning   Mobile Edge Computing   Power Allocation   Task Offloading   UAV  

摘要： Mobile edge computing (MEC) can provide computing services for mobile users (MUs) by offloading computing tasks to edge clouds through wireless access networks. Unmanned aerial vehicles (UAVs) are deployed as supplementary edge clouds to provide effective MEC services for MUs with poor wireless communication condition. In this paper, a joint task offloading and power allocation (TOPA) optimization problem is investigated in UAV-assisted MEC system. Since the joint TOPA problem has a strong non-convex characteristic, a method based on deep reinforcement learning is proposed. Specifically, the joint TOPA problem is modeled as Markov decision process. Then, considering the large state space and continuous action space, a twin delayed deep deterministic policy gradient algorithm is proposed. Simulation results show that the proposed scheme has lower smoothing training cost than other optimization methods.

关键词： green energy   mobile edge computing   MEC   solar panel placement   mobile cloud computing  

摘要： Mobile edge computing (MEC) enhances smart phone computational and storage capabilities by offloading tasks from mobile phones to the edge of the network where servers are placed near base stations. Since data centres consume electricity more and more each year, we consider locating renewable energy sources such as solar panels near each MEC. We tackle these challenges by strategically placing solar panels near MECs to minimise the energy costs. We formulate and solve the optimisation problem and present two algorithms to solve the optimisation problem. Moreover, we analyse the workload of each MEC during past years, and we present an algorithm to predict future workload of different MECs. We evaluate our solution using real Google traces collected over a 29-day period from a Google cluster. Simulation results show that with the help of our optimisation algorithms, we can effectively reduce the electricity costs and increase the green energy consumption.

关键词： Task analysis   Quality of service   Resource management   Optimization   Base stations   Servers   Energy consumption   Mobile edge computing   Internet of Things (IoT)   quality of service (QoS)   service provisioning   multi-path routing   high level petri nets  

摘要： Recent development in modern wireless applications and services, such as augmented reality, image processing, and network gaming requires persistent computing on average commercial wireless devices to perform complex tasks with low latency. The traditional cloud systems are unable to meet those requirements solely. In the said perspective, Mobile Edge Computing (MEC) serves as a proxy between the things (devices) and the cloud, pushing the computations at the edge of the network. The MEC provides an effective solution to fulfill the demands of low-latency applications and services by executing most of the tasks within the proximity of users. The main challenge, however, is that too many simultaneous service requests created by wireless access produce severe interference, resulting in a decreased rate of data transmission. In this paper, we made an attempt to overcome the aforesaid limitation by proposing a user-centric QoS-aware multi-path service provisioning approach. A densely deployed base station MEC environment has overlapping coverage regions. We exploit such regions to distribute the service requests in a way that avoid hotspots and bottlenecks. Our approach is adaptive and can tune to different parameters based on service requirements. We performed several experiments to evaluate the effectiveness of our approach and compared it with the traditional Greedy approach. The results revealed that our approach improves the network state by 26.95% and average waiting time by 35.56% as compared to the Greedy approach. In addition, the QoS violations were also reduced by the fraction of 16.

关键词： Device-to-device communication   Resource management   5G mobile communication   Interference   Quality of service   Optimization   Edge computing   Network slicing   5G cellular networks   mobile edge computing   device-to-device   distributed optimization   consensus algorithm  

摘要： Fifth Generation (5G) cellular networks aim to overcome the pressing demands posed by dynamic Quality of Service (QoS) constraints, which have primarily remained unaddressed using conventional network infrastructure. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that readily meet throughput requirements. The idea of combining Device-to-Device (D2D), Mobile Edge Computing (MEC), and Network slicing (NS) can improve spectrum utilization with better performance and scalability. This work presents a spectrum efficiency optimization problem in D2D based 5G-Heterogeneous Cellular Network (5G-HCN) with NS. Owing to the shortage of resources, we propose an underlay model where macro-cell users (MUs), small-cell users (SUs), and D2D users (DUs) reuse the resources while considering the effects of interference. The goal is to maximize the average network spectrum efficiency (SE) and throughput without degrading the system performance. The problem at hand is naturally a non-convex mixed-integer non-linear programming (MINLP) problem that is intractable. Therefore, we have suggested a distributed resource allocation strategy with an edge computing (DRA-EC) approach to find the sub-optimal solution. In distributed augmented Lagrange method, each edge router located at BS will solve its problem locally, and the consensus algorithm will find the global solution using these local estimates. The central slice controller will cut the customized network slices according to the bandwidth requirements of each user type with optimized spectrum information. The simulation outcomes prove that our proposed method is near the central optimization scheme with low computational complexity. It is much better because it reduces the computational time and system overhead.

关键词： Tactical edge network   federated learning   mobile edge computing   weighted average solution   constant elasticity substitution solution  

摘要： In this study, we focus on the federated learning (FL) based tactical edge network platform to cooperatively operate computation-hungry tasks as efficiently as possible. Based on the incentive of FL model training, each individual device makes offloading decisions for their tactical tasks in resource-constrained network environments. According to the ideas of two different bargaining solutions - weighted average solution and constant elasticity substitution solution - edge server and IoT devices work together in a coordinated manner to approximate a well-balanced system performance. Therefore, we can reach an agreement while exploring the mutual benefits to leverage a reciprocal consensus between different viewpoints. The main novelty of our approach is to investigate the dual-interactive bargaining process based on the interdependent relationship between IoT devices and the tactical edge server. To the best of our knowledge, this is the first work that jointly considers different bargaining solutions to handle tactical edge-assisted task offloading services. Based on the numerical simulation, it is demonstrated that the proposed approach can increase the system throughput, device payoff and device fairness up to 10%, 15% and 20%, respectively, in comparison with existing protocols.

关键词： Task scheduling   mobile edge computing   cloud computing   QoS-aware  

摘要： Since the limitations of the user equipment, they do not have enough computing power to process large amounts of data. At the same time, it's unbearable for users to spend a long time uploading data to the remote cloud center. In order to solve these problems above, the concept of mobile edge computing(MEC) is proposed. The computing and storage resources are placed close to the end equipment, reducing the transmission delay. MEC can meet the high real-time requirements of the user equipment. In the real-time face recognition application scenario, a three-layer hierarchy MS-CE is proposed for the shortcoming of the traditional centralized cloud center. And the distributed MEC servers are utilized at the MEC layer to provide parallel computing capabilities. Aiming at the problem of how to perform task scheduling in a geographically distributed MEC server, we propose a task scheduling based queue algorithm(TSBQ), which considers the data transmission delay and server load, and carries out a reasonable task allocation policy. We evaluate the MS-CE and TSBQ through simulation experiments. We can find the MS-CE architecture are better than others and TSBQ is more effective than Corral and Greedy.