关键词： Mobile edge computing  

摘要： Merging Mobile Edge Computing (MEC), which is an emerging paradigm to meet the increasing computation demands from mobile devices, with the dense deployment of Base Stations (BSs), is foreseen as a key step towards the next generation mobile networks. However, new challenges arise for designing energy efficient networks since radio access resources and computing resources of BSs have to be jointly managed, and yet they are complexly coupled with traffic in both spatial and temporal domains. In this paper, we address the challenge of incorporating MEC into dense cellular networks, and propose an efficient online algorithm, called ENGINE (ENErgy constrained offloadINg and slEeping) which makes joint computation offloading and BS sleeping decisions in order to maximize the quality of service while keeping the energy consumption low. Our algorithm leverages Lyapunov optimization technique, works online and achieves a close-to-optimal performance without using future information. Our simulation results show that our algorithm can effectively reduce energy consumption without sacrificing the user quality of service. Copyright © 2017, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Merging mobile edge computing with the dense deployment of small cell base stations promises enormous benefits such as a real proximity, ultra-low latency access to cloud functionalities. However, the envisioned integration creates many new challenges and one of the most significant is mobility management, which is becoming a key bottleneck to the overall system performance. Simply applying existing solutions leads to poor performance due to the highly overlapped coverage areas of multiple base stations in the proximity of the user and the co-provisioning of radio access and computing services. In this paper, we develop a novel user-centric mobility management scheme, leveraging Lyapunov optimization and multi-armed bandits theories, in order to maximize the edge computation performance for the user while keeping the user's communication energy consumption below a constraint. The proposed scheme effectively handles the uncertainties present at multiple levels in the system and provides both short-term and long-term performance guarantee. Simulation results show that our proposed scheme can significantly improve the computation performance (compared to state of the art) while satisfying the communication energy constraint. Copyright © 2017, The Authors. All rights reserved.

标准号: ETSI GS MEC-IEG 006

关键词： Mobile edge computing   fog computing   medical cyber physical system   cost efficiency  

摘要： With the recent development in information and communication technology, more and more smart devices penetrate into people's daily life to promote the life quality. As a growing healthcare trend, medical cyber-physical systems (MCPSs) enable seamless and intelligent interaction between the computational elements and the medical devices. To support MCPSs, cloud resources are usually explored to process the sensing data from medical devices. However, the high quality-of-service of MCPS challenges the unstable and long-delay links between cloud data center and medical devices. To combat this issue, mobile edge cloud computing, or fog computing, which pushes the computation resources onto the network edge (e.g., cellular base stations), emerges as a promising solution. We are thus motivated to integrate fog computation and MCPS to build fog computing supported MCPS (FC-MCPS). In particular, we jointly investigate base station association, task distribution, and virtual machine placement toward cost-efficient FC-MCPS. We first formulate the problem into a mixed-integer non-linear linear program and then linearize it into a mixed integer linear programming (LP). To address the computation complexity, we further propose an LP-based two-phase heuristic algorithm. Extensive experiment results validate the high-cost efficiency of our algorithm by the fact that it produces near optimal solution and significantly outperforms a greedy algorithm.

关键词： Mobile edge computing  

摘要： Mobile-Edge Computing (MEC) is a novel and sustainable network architecture that enables energy conservation with cloud computing and network services offloading at the edge of mobile cellular networks. However, how to efficiently manage various real-time changing security functions is an essential issue which hinders the future MEC development. To address this problem, we propose a fuzzy security service chaining approach for MEC. In particular, a new architecture is designed to decouple the required security functions with the physical resources. Based on this, we present a security proxy to support compatibility to traditional security functions. Furthermore, to find the optimal order of the required security functions, we establish a fuzzy inference system (FIS) based mechanism to achieve multiple optimal objectives. Much work has been done to implement a prototype, which is used to analyze the performance by comparing with a widely used method. The results prove that the proposed FIS mechanism achieves an improved performance in terms of Inverted Generational Distance (IGD) values and execution time with respect to the compared solution.

关键词： MEC   Multi-Access Edge Computing   Mobile Edge Computing   5G Mobile Communications   Edge Computing   Application Virtualization   Network Function Virtualization   Software Defined Networking   Network Softwarization  

摘要： Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.

关键词： Cloud   fog   mobile edge computing   ontology   overlay   Petri net   service composition   workflow net  

摘要： Content delivery through cloud networks has gained popularity due to the cloud's ability to provide on-demand services. However, composite services, such as customized multimedia content, introduce both delays and resource limitations if traditional cloud solutions are used. With recent advances in mobile edge computing, customized media delivery can be achieved through compositions of service specific overlays (SSOs). This paper presents a work flow-net-based mechanism for mobile edge node cooperation in fog-cloud networks to form guaranteed SSOs. The proposed solution uses a mathematical cooperation operator to turn the SSO composition problem expressed as workflow nets into algebraic representations. In turn, the minimal cost cooperative path from the workflow net is determined such that it guarantees the delivery of the requested composite media services to clients. Experimental results show that the composition process can be adequately established and carried out in a timely manner.

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

摘要： 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. [ABSTRACT FROM AUTHOR]

关键词： Mobile edge computing   fog computing   mobile cloud computing   computation offloading   resource management   green computing  

摘要： Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

关键词： Task scheduling   mobile edge computing   overhead analysis   potential game   optimal strategy  

摘要： In this paper, an overhead-optimizing multi-device task scheduling strategy for ad-hoc-based mobile edge computing system is proposed. This task scheduling strategy takes the opportunity consumption, time delay, energy consumption, and monetary cost into account, aiming at minimizing the overhead of each mobile device. First, a system model for ad-hoc-based mobile edge computing is presented and the overhead of mobile device is analyzed. Second, the task scheduling problem is formulated as a distributed multi-device task scheduling game. Then, by constructing a potential function, the task scheduling game is proved to be a potential game, which possesses a property of finite improvement and always owns a Nash equilibrium. Next, an overhead-optimizing multi-device task scheduling algorithm is designed and the computational complexity is analyzed. Finally, simulations are conducted to evaluate the effectiveness of the proposed strategy. The results show that the proposed task scheduling strategy can effectively minimize the overhead of the mobile device and successfully complete the tasks.