关键词： Call graph   delay jitter   mobile edge computing   scheduling   stochastic delay  

摘要： We propose a new efficient and effective task scheduling approach with stochastic time cost for computation offloading in mobile edge computing. We developed an optimization model that minimizes the maximum tolerable delay (MTD) by considering both the average delay and delay jitter. We also proposed an efficient conservative heterogeneous earliest-finishtime algorithm to solve the MTD-minimization problem. Numerical results obtained with our proposed approach demonstrate its effectiveness over previously proposed techniques.

关键词： Blockchain   video transcoding   mobile edge computing  

摘要： Blockchain-based video streaming systems aim to build decentralized peer-to-peer networks with flexible monetization mechanisms for video streaming services. On these blockchain-based platforms, video transcoding, which is computationally intensive and time-consuming, is still a major challenge. Meanwhile, the block size of the underlying blockchain has significant impacts on the system performance. Therefore, this paper proposes a novel blockchain-based framework with an adaptive block size for video streaming with mobile edge computing (MEC). First, we design an incentive mechanism to facilitate collaboration among content creators, video transcoders, and consumers. In addition, we present a block size adaptation scheme for blockchain-based video streaming. Moreover, we consider two offloading modes, i.e., offloading to the nearby MEC nodes or a group of device-to-device (D2D) users, to avoid the overload of MEC nodes. Then, we formulate the issues of resource allocation, scheduling of offloading, and adaptive block size as an optimization problem. We employ a low-complexity alternating direction method of the multipliers-based algorithm to solve the problem in a distributed fashion. Simulation results are presented to show the effectiveness of the proposed scheme.

关键词： Computation offloading   mobile edge computing   Nash equilibria   decentralized algorithms  

摘要： Offloading computation to a mobile cloud is a promising solution to augment the computation capabilities of mobile devices. In this paper, we consider selfish mobile devices in a dense wireless network, in which individual mobile devices can offload computations through multiple access points or through the base station to a mobile cloud so as to minimize their computation costs. We provide a game theoretical analysis of the problem, prove the existence of pure strategy Nash equilibria, and provide an efficient decentralized algorithm for computing an equilibrium. For the case when the cloud computing resources scale with the number of mobile devices, we show that all improvement paths are finite. Furthermore, we provide an upper bound on the price of anarchy of the game, which serves as an upper bound on the approximation ratio of the proposed decentralized algorithms. We use simulations to evaluate the time complexity of computing Nash equilibria and to provide insights into the price of anarchy of the game under realistic scenarios. Our results show that the equilibrium cost may be close to optimal, and the convergence time is almost linear in the number of mobile devices.

关键词： 5 G Mobile Communication   Augmented Reality   Internet   Long Term Evolution   Mobile Computing   Mobile Radio   Mobile Users   AR VR Technologies   Research Community   Tactile Internet Applications   Massive Computational Capability   High Communication Bandwidth   Ultra Low Latency   Fifth Generation Networks   Edge Caching   Mobile Edge Computing   AR VR Applications   Mobile AR VR   Augmented Reality   Virtual Reality Applications   Wireless Mobile Networks   Long Term Evolution Networks   5 G Technologies   Tactile Internet   Edge Computing   Videos   Haptic Interfaces   Cloud Computing   5 G Mobile Communication   Servers  

摘要： As a result of increasing popularity of augmented reality and virtual reality (AR/VR) applications, there are significant efforts to bring AR/VR to mobile users. Parallel to the advances in AR/VR technologies, tactile internet is gaining interest from the research community. Both AR/VR and tactile internet applications require massive computational capability, high communication bandwidth, and ultra-low latency that cannot be provided with the current wireless mobile networks. By 2020, long term evolution (LTE) networks will start to be replaced by fifth generation (5G) networks. Edge caching and mobile edge computing are among the potential 5G technologies that bring content and computing resources close to the users, reducing latency and load on the backhaul. The aim of this survey is to present current state-of-the-art research on edge caching and computing with a focus on AR/VR applications and tactile internet and to discuss applications, opportunities and challenges in this emerging field.

关键词： Mobile edge computing   computation offloading   multi-server resource allocation   distributed systems  

摘要： Mobile-edge computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this paper, an MEC enabled multi-cell wireless network is considered where each base station (BS) is equipped with a MEC server that assists mobile users in executing computation-intensive tasks via task offloading. The problem of joint task offloading and resource allocation is studied in order to maximize the users' task offloading gains, which is measured by a weighted sum of reductions in task completion time and energy consumption. The considered problem is formulated as a mixed integer nonlinear program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the combinatorial nature of this problem, solving for optimal solution is difficult and impractical for a large-scale network. To overcome this drawback, we propose to decompose the original problem into a resource allocation (RA) problem with fixed task offloading decision and a task offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem. We address the RA problem using convex and quasi-convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users' offloading utility over traditional approaches.

关键词： Cooperative caching   mobile edge computing   multi-bitrate video transcoding   adaptive bitrate streaming   multicarrier   NOMA   wireless network virtualization   cache refreshment  

摘要： Cooperative video caching and transcoding in mobile edge computing (MEC) networks is a new paradigm for future wireless networks, e.g., 5G and beyond-5G, to reduce scarce and expensive backhaul resource usage by prefetching video files within radio access networks (RANs). Integration of this technique with other advent technologies, such as wireless network virtualization and multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible video delivery opportunities, which leads to enhancements both for the network's revenue and for the end-users' service experience. In this regard, we propose a two-phase RAF for a parallel cooperative joint multi-bitrate video caching and transcoding in heterogeneous virtualized MEC (HV-MEC) networks. In the cache placement phase, we propose novel proactive delivery-aware cache placement strategies (DACPSs) by jointly allocating physical and radio resources based on the network stochastic information to exploit flexible delivery opportunities. Then, for the delivery phase, we propose a delivery policy based on user requests and network channel conditions. The optimization problems corresponding to both phases aim to maximize the total revenue of network slices, i.e., virtual networks. Both problems are non-convex and suffer from high-computational complexities. For each phase, we show how the problem can be solved efficiently. We also propose a low-complexity RAF, in which the complexity of the delivery algorithm is significantly reduced. A delivery-aware cache refreshment strategy (DACRS) in the delivery phase is also proposed to tackle the dynamical changes of network stochastic information. The extensive numerical assessments demonstrate a performance improvement of up to 30% for our proposed DACPSs and DACRS over traditional approaches.

关键词： Mobile edge computing   content caching   content popularity   live streaming  

摘要： Mobile Edge Computing Caching System (MECCS) realizes low-latency and high-bandwidth content access and enables seamless 4K Ultra High Definition (UHD) video streaming by caching content in advance at edge-servers of a cellular network. The objective of MECCS is to maximize cache hit by caching highly popular video content while utilizing the storage capacity efficiently in edge-servers. Most of existing caching schemes estimate the popularity of each content based on content request history in off-line or on-line manners, considering the characteristics of Video-on-Demand (VoD) content which has long-term time-varying popularity. However, since live streaming follows Short-term Time-Varying (STV) characteristics, estimating popularity based on content request history do not guarantee acceptable performance on cache hit for live streaming. In this paper, we propose a request model to estimate the popularity distribution considering STV characteristics. Also, we propose a STV request model-based chunk caching scheme to cache highly popular content and enhance cache hit in multiple live channels, utilizing the storage capacity of collaborative edge-servers efficiently. Experimental results show that the proposed scheme outperforms existing schemes regarding cache hit and backhaul traffic.

关键词： Computation offloading   Energy optimization   Mobile edge computing   Simulated annealing  

摘要： With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to provide near computing and storage capabilities to smart mobile devices. In addition, mobile devices are most of the time battery dependent and energy constrained while they are characterized by their limited processing and storage capacities. Accordingly, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstances, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, we must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and that retains a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate our solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. Moreover, our solution performs the offloading decisions within an acceptable and feasible timeframes. Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved.

关键词： Mobile edge computing   network coding   wireless relay   intelligent cache   cooperative communications  

摘要： As an important technology in next-generation networks, mobile edge computing (MEC) can bring high-speed intelligent network services to users. However, planning MEC file transmission represents a problem to be urgently solved. In addition, network coding has been an emerging technology in recent years, and certain forms of cooperation with wireless relays can improve the transmission efficiency of network data. In this paper, we focus on cooperation in MEC intelligent cache with network coding. First, we built a MEC intelligent cache analysis platform that includes multiple algorithms;it can predetermine which streaming media files should be stored in the limited storage space of the MEC device to provide users the most efficient intelligent network experience. The platform's decision algorithm includes an innovative composite recommendation algorithm that is more comprehensive than the traditional recommendation algorithm. The MEC file extracted from the core network is then simultaneously transmitted to the edge with the real-time normal communication file using an innovative network coding and wireless relay cooperation method;with that, the files stored in the MEC can be cached to the edge. The normal network communication process does not require additional slot costs. The simulation results show that the proposed cooperation scheme can effectively improve network data transmission, improve users' average access rate, and offer users better intelligent network services.

关键词： Deep reinforcement learning   video quality selection   radio bearer control   short video applications   mobile edge computing  

摘要： With the rapid development of mobile communication technology, short video applications, which combine the features of both social and multimedia applications, have become more and more popular. However, the transmission of short videos poses great challenges to the existing mobile networks. In this paper, mobile edge computing is adopted to provide content caching of short videos close to end users. To improve the quality of service, we take both the quality level of the video and the long-term wireless network transmission performance into consideration. The joint video quality selection and radio bearer control optimization problem is formulated as a Markov decision process, aiming at maximizing the long-term video quality profit and minimizing the cost of bearers and the penalty of latency. Deep reinforcement learning is used as the solution and the policy gradient based quality selection and radio bearer control method is proposed. The REINFORCE algorithm with baseline is used to train the policy network, and the episodic simulations are built to obtain the training samples. Different weight coefficients of the objective function are configured. Training results show that the proposed method can achieve the best accumulated value among all the comparison methods. When the weight coefficients are changed, the training processes can lead the policy networks to obtain proper trade-off between different objective factors. Moreover, the performance of the trained policy network is evaluated with different short video request arriving rates. Testing results show that the proposed method performs well when the arriving rates vary in a certain range.