关键词： Mobile edge computing  

摘要： With the ever-increasing popularity of resourceintensive mobile applications, Mobile Edge Computing (MEC), e.g., offloading computationally expensive tasks to the cellular edge, has become a prominent technology for the next generation wireless networks. Despite its great performance in terms of delay and energy, MEC suffers from restricted power allowance and computational capability of the edge nodes. Therefore, it is imperative to develop distributed mechanisms for computation offloading, so that not only the computational servers are utilized at their best capacity, but also the users' latency constraints are fulfilled. In this paper, by using the theory of Minority Games, we develop a novel distributed server activation mechanism for computational offloading. Our scheme guarantees energy-efficient activation of servers as well as satisfaction of users' quality-ofexperience (QoE) requirements in terms of latency. Copyright © 2017, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： While mobile edge computing (MEC) alleviates the computation and power limitations of mobile devices, additional latency is incurred when offloading tasks to remote MEC servers. In this work, the power-delay tradeoff in the context of task offloading is studied in a multi-user MEC scenario. In contrast with current system designs relying on average metrics (e.g., the average queue length and average latency), a novel network design is proposed in which latency and reliability constraints are taken into account. This is done by imposing a probabilistic constraint on users' task queue lengths and invoking results from extreme value theory to characterize the occurrence of lowprobability events in terms of queue length (or queuing delay) violation. The problem is formulated as a computation and transmit power minimization subject to latency and reliability constraints, and solved using tools from Lyapunov stochastic optimization. Simulation results demonstrate the effectiveness of the proposed approach, while examining the power-delay tradeoff and required computational resources for various computation intensities. Copyright © 2017, The Authors. All rights reserved.

关键词： Mobile edge computing   MPEG-DASH   network function virtualization   prefetching   quality of experience (QoE)   video on demand (VoD)  

摘要： Internet video streaming applications have been demanding more bandwidth and higher video quality, especially with the advent of virtual reality and augmented reality applications. While adaptive strea ming protocols like MPEG-DASH (dynamic adaptive streaming over HTTP) allows video quality to be flexibly adapted, e.g., degraded when mobile network condition deteriorates, this is not an option if the application itself requires guaranteed 4K quality at all time. On the other hand, conventional end-to-end transmission control protocol (TCP) has been struggling in supporting 4K video delivery across long-distance Internet paths containing both fixed and mobile network segments with heterogeneous characteristics. In this paper, we present a novel and practically feasible system architecture named MVP(mobile edge virtualization with adaptive prefetching), which enables content providers to embed their content intelligence as a virtual network function into the mobile network operator's infrastructure edge. Based on this architecture, we present a context-aware adaptive video prefetching scheme in order to achieve quality of experience (QoE)-assured 4K video on demand (VoD) delivery across the global Internet. Through experiments based on a real LTE-A network infrastructure, we demonstrate that our proposed scheme is able to achieve QoE-assured 4K VoD streaming, especially when the video source is located remotely in the public Internet, in which case none of the state-of-the-art solutions is able to support such an objective at global Internet scale.

关键词： Virtual machine migrations   data replication   hyper-graph partitioning   network optimization   mobile edge computing   cloud computing  

摘要： Several virtual machine (VM) placement algorithms have been proposed and studied in the literature with various scopes such as server consolidation or network cost minimization. In most cases, decisions on VM migrations are taken without factoring in directly the data access cost by VMs. In this paper, we investigate the use of data replication in conjunction with the VM assignment problemand target on developing algorithms that decide both on which data should be replicated where and whichVM must be migrated so as to minimize the network overhead among traditional cloud and mobile cloud systems. We discuss both the un-capacitated case and the more realistic case whereby datacenters (for the traditional cloud case) and micro-datacenters (for the mobile cloud case) have limited storage and computing capacity. We propose an algorithm based on hyper-graph partitioning to solve the aforementioned problemin an optimal way regarding the unconstrained case and extend it to capture storage and computing capacity constraints. Experimental evaluation shows that the proposed algorithm yields up to 53 percent network overhead reduction when compared to state-of-the-art algorithms found in the literature.

关键词： Charging Infrastructure   Electric Vehicle   Electric Vehicle Supply Equipment   Fog Computing   Mobile Edge Computing  

摘要： 화석 연료의 고갈 및 환경오염의 증가로 인하여 전기 에너지를 사용하는 전기 자동차가 차세대 교통수단으로 주목 받고 있으며 전 세계적으로 인기를 끌고 있다. 전기 자동차의 보급률 및 관심이 높아짐에 따라 V2G (Vehicle to Grid) 및 IT 기술을 이용한 충전 인프라에 대한 연구가 활발히 진행되고 있다. 특히, 전기 자동차의 안정적인 충전 및 부하 관리를 위하여 그리드 네트워크와의 통신은 가장 중요한 요소이다. 그러나 기존의 중앙 집중형 인프라 구조의 경우 제어 메시지 요청이 증가할 경우 느린 응답속도로 인하여 충전 인프라가 효율적으로 작동하지 못하는 문제점들이 존재한다. 본 논문에서는 분산형 클라우드 컴퓨팅 기술을 무선 기지국에 적용하여 충전 인프라에 혼잡을 줄이고 지연시간을 줄이기 위해 MEC (Mobile Edge Computing)를 활용한 새로운 전기자동차 충전 인프라 구조를 제안한다. 성능 평가를 통해 본 논문에서 제안한 저 지연시간을 가지는 충전 인프라가 기존에 존재하는 충전 인프라보다 효율적으로 전력 피크 상황에 대처함을 확인하였다. Due to the depletion of fossil fuels and the increase in environmental pollution, electric vehicles are attracting attention as next-generation transportation and are becoming popular all over the world. As the interest in electric vehicles and the penetration rate increase, studies on the charging infrastructure with vehicle-to-grid (V2G) technology and information technology are actively under way. In particular, communication with the grid network is the most important factor for stable charging and load management of electric vehicles. However, with the existing centralized infrastructure, there are problems whencontrol-message requests increase and the charging infrastructure cannot efficiently operate due to slow response speed. In this paper, we propose a new charging infrastructure using mobile edge computing (MEC) that mitigates congestion and provides low latency by applying distributed cloud computing technology to wireless base stations. Through aperformance evaluation, we confirm that the proposed charging infrastructure (with low latency) can cope with peak conditions more efficiently than the existing charging infrastructure.

关键词： mobile edge computing   software-defined systems   LTE and WiFi   ubiquitous Computing   mobile cloud computing  

摘要： Mobile Edge Computing (MEC) enables ubiquitous and efficient cloud services to mobile users which facilitates mobile cloud computing (MCC) more easily by providing storage and processing capacity within the access range of the mobile devices. To achieve the goals of MEC, Mobile Edge (ME) servers are co-placed with the mobile network base station (at the edge of the mobile network). This eliminates the need to move computation and storage intensive tasks from a mobile device to a centralized cloud server. This in turn reduces the network communication load and delay and it also enhances the quality of service provided for the mobile end users. Applications, such as smart grid applications, content delivery networks, crowed sourcing, traffic management and E-health, will greatly benefit from such deployment. Unfortunately, a large-scale deployment of ME servers that is needed to make hundreds of applications available to millions of users, comes with great management complexity. The emerging technique of Software-Defined Systems (SDSys) abstracts the management complexities of many systems at different layers by utilizing software components. In this paper, we build a software-defined based framework that enable efficient and ubiquitous MCC services by integrating different SDSys components with the MEC system. The integrated framework is implemented and it is evaluated for its feasibility, flexibility and potential superiority.

关键词： Mobile edge computing  

摘要： Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service provision latency and saving backhaul network bandwidth. Although computation offloading has been extensively studied in the literature, service caching is an equally, if not more, important design topic of MEC, yet receives much less attention. Service caching refers to caching application services and their related data (libraries/databases) in the edge server, e.g. MEC-enabled Base Station (BS), enabling corresponding computation tasks to be executed. Since only a small number of services can be cached in resource-limited edge server at the same time, which services to cache has to be judiciously decided to maximize the system performance. In this paper, we investigate collaborative service caching in MEC-enabled dense small cell (SC) networks. We propose an efficient decentralized algorithm, called CSC (Collaborative Service Caching), where a network of small cell BSs optimize service caching collaboratively to address a number of key challenges in MEC systems, including service heterogeneity, spatial demand coupling, and decentralized coordination. Our algorithm is developed based on parallel Gibbs sampling by exploiting the special structure of the considered problem using graphing coloring. The algorithm significantly improves the time efficiency compared to conventional Gibbs sampling, yet guarantees provable convergence and optimality. CSC is further extended to the SC network with selfish BSs, where a coalitional game is formulated to incentivize collaboration. A coalition formation algorithm is developed by employing the merge-and-split rules and ensures the stability of the SC coalitions. Systematic simulations are carried out to evaluate the efficacy and performance of the proposed algorithm. The results show that our algorithm can effectively reduce edge system operational cost for both cooperative and

关键词： Mobile edge computing  

摘要： —Mobile edge computing (MEC) is a promising approach for enabling cloud-computing capabilities at the edge of cellular networks. Nonetheless, security is becoming an increasingly important issue in MEC-based applications. In this paper, we propose a deep-learning-based model to detect security threats. The model uses unsupervised learning to automate the detection process, and uses location information as an important feature to improve the performance of detection. Our proposed model can be used to detect malicious applications at the edge of a cellular network, which is a serious security threat. Extensive experiments are carried out with 10 different datasets, the results of which illustrate that our deep-learning-based model achieves an average gain of 6% accuracy compared with state-of-the-art machine learning algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC), affords service to the vicinity of mobile devices (MDs), has become a key technology for future network. Offloading big data to the MEC server for preprocessing is a attractive choice of MDs. In the paper, we investigate data offloading from MDs to MEC servers. A coalitional game based pricing scheme is proposed. We apply coalitional game to depict the offloading relationship between MDs and MEC servers, and utilize pricing as the stimuli for the offloading. A scheduled MD chooses one MEC server within the same coalition for offloading, and pays the selected MEC server for the MEC service. We formulate a coalitional game, where MDs and MEC servers are players and their utilities are respectively defined. Next, we analyze the formulated game. Specially, the core is studied. Finally, utility performance of the proposed scheme under the 2-MD and 2-MEC- server scenario are demonstrated. Copyright © 2017, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： —Merging mobile edge computing (MEC) functionality with the dense deployment of base stations (BSs) provides enormous benefits such as a real proximity, low latency access to computing resources. However, the envisioned integration creates many new challenges, among which mobility management (MM) is a critical one. Simply applying existing radio access oriented MM schemes leads to poor performance mainly due to the co-provisioning of radio access and computing services of the MEC-enabled BSs. In this paper, we develop a novel user-centric energy-aware mobility management (EMM) scheme, in order to optimize the delay due to both radio access and computation, under the long-term energy consumption constraint of the user. Based on Lyapunov optimization and multi-armed bandit theories, EMM works in an online fashion without future system state information, and effectively handles the imperfect system state information. Theoretical analysis explicitly takes radio handover and computation migration cost into consideration and proves a bounded deviation on both the delay performance and energy consumption compared to the oracle solution with exact and complete future system information. The proposed algorithm also effectively handles the scenario in which candidate BSs randomly switch on/off during the offloading process of a task. Simulations show that the proposed algorithms can achieve close-to-optimal delay performance while satisfying the user energy consumption constraint. Copyright © 2017, The Authors. All rights reserved.