Fog Computing: Focusing on Mobile Users at the Edge

With smart devices, particular smartphones, becoming our everyday companions, the ubiquitous mobile Internet and computing applications pervade people daily lives. With the surge demand on high-quality mobile services at anywhere, how to address the ubiquitous user demand and accommodate the explosive growth of mobile traffics is the key issue of the next generation mobile networks. The Fog computing is a promising solution towards this goal. Fog computing extends cloud computing by providing virtualized resources and engaged location-based services to the edge of the mobile networks so as to better serve mobile traffics. Therefore, Fog computing is a lubricant of the combination of cloud computing and mobile applications. In this article, we outline the main features of Fog computing and describe its concept, architecture and design goals. Lastly, we discuss some of the future research issues from the networking perspective.Comment: 11 pages, 6 figure

Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities

The ever-increasing mobile data demands have posed significant challenges in the current radio access networks, while the emerging computation-heavy Internet of things (IoT) applications with varied requirements demand more flexibility and resilience from the cloud/edge computing architecture. In this article, to address the issues, we propose a novel air-ground integrated mobile edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and assist the communication, caching, and computing of the edge network. In specific, we present the detailed architecture of AGMEN, and investigate the benefits and application scenarios of drone-cells, and UAV-assisted edge caching and computing. Furthermore, the challenging issues in AGMEN are discussed, and potential research directions are highlighted.Comment: Accepted by IEEE Communications Magazine. 5 figure

An Analysis of Fog Computing Data Placement Algorithms

This work evaluates three Fog Computing dataplacement algorithms via experiments carried out with theiFogSim simulator. The paper describes the three algorithms(Cloud-only, Mapping, Edge-ward) in the context of an Internetof Things scenario, which has been based on an e-Health systemwith variations in applications and network topology. Resultsachieved show that edge placement strategies are beneficial toassist cloud computing in lowering latency and cloud energyexpenditure

Machine Learning for Heterogeneous Ultra-Dense Networks with Graphical Representations

Heterogeneous ultra-dense network (H-UDN) is envisioned as a promising solution to sustain the explosive mobile traffic demand through network densification. By placing access points, processors, and storage units as close as possible to mobile users, H-UDNs bring forth a number of advantages, including high spectral efficiency, high energy efficiency, and low latency. Nonetheless, the high density and diversity of network entities in H-UDNs introduce formidable design challenges in collaborative signal processing and resource management. This article illustrates the great potential of machine learning techniques in solving these challenges. In particular, we show how to utilize graphical representations of H-UDNs to design efficient machine learning algorithms

Multi-Dimensional Auction Mechanisms for Crowdsourced Mobile Video Streaming

Crowdsourced mobile video streaming enables nearby mobile video users to aggregate network resources to improve their video streaming performances. However, users are often selfish and may not be willing to cooperate without proper incentives. Designing an incentive mechanism for such a scenario is challenging due to the users' asynchronous downloading behaviors and their private valuations for multi-bitrate coded videos. In this work, we propose both single-object and multi-object multi-dimensional auction mechanisms, through which users sell the opportunities for downloading single and multiple video segments with multiple bitrates, respectively. Both auction mechanisms can achieves truthfulness (i.e, truthful private information revelation) and efficiency (i.e., social welfare maximization). Simulations with real traces show that crowdsourced mobile streaming facilitated by the auction mechanisms outperforms noncooperative stream ing by 48.6% (on average) in terms of social welfare. To evaluate the real-world performance, we also construct a demo system for crowdsourced mobile streaming and implement our proposed auction mechanism. Experiments over the demo system further show that those users who provide resources to others and those users who receive helps can increase their welfares by 15.5% and 35.4% (on average) via cooperation, respectively

When Social Sensing Meets Edge Computing: Vision and Challenges

This paper overviews the state of the art, research challenges, and future opportunities in an emerging research direction: Social Sensing based Edge Computing (SSEC). Social sensing has emerged as a new sensing application paradigm where measurements about the physical world are collected from humans or from devices on their behalf. The advent of edge computing pushes the frontier of computation, service, and data along the cloud-to-things continuum. The merging of these two technical trends generates a set of new research challenges that need to be addressed. In this paper, we first define the new SSEC paradigm that is motivated by a few underlying technology trends. We then present a few representative real-world case studies of SSEC applications and several key research challenges that exist in those applications. Finally, we envision a few exciting research directions in future SSEC. We hope this paper will stimulate discussions of this emerging research direction in the community.Comment: This manuscript has been accepted to ICCCN 201

Energy and Information Management of Electric Vehicular Network: A Survey

The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN

Exploiting Moving Intelligence: Delay-Optimized Computation Offloading in Vehicular Fog Networks

Future vehicles will have rich computing resources to support autonomous driving and be connected by wireless technologies. Vehicular fog networks (VeFN) have thus emerged to enable computing resource sharing via computation task offloading, providing wide range of fog applications. However, the high mobility of vehicles makes it hard to guarantee the delay that accounts for both communication and computation throughout the whole task offloading procedure. In this article, we first review the state-of-the-art of task offloading in VeFN, and argue that mobility is not only an obstacle for timely computing in VeFN, but can also benefit the delay performance. We then identify machine learning and coded computing as key enabling technologies to address and exploit mobility in VeFN. Case studies are provided to illustrate how to adapt learning algorithms to fit for the dynamic environment in VeFN, and how to exploit the mobility with opportunistic computation offloading and task replication.Comment: 7 pages, 4 figures, accepted by IEEE Communications Magazin

ECHO: An Adaptive Orchestration Platform for Hybrid Dataflows across Cloud and Edge

The Internet of Things (IoT) is offering unprecedented observational data that are used for managing Smart City utilities. Edge and Fog gateway devices are an integral part of IoT deployments to acquire real-time data and enact controls. Recently, Edge-computing is emerging as first-class paradigm to complement Cloud-centric analytics. But a key limitation is the lack of a platform-as-a-service for applications spanning Edge and Cloud. Here, we propose ECHO, an orchestration platform for dataflows across distributed resources. ECHO's hybrid dataflow composition can operate on diverse data models -- streams, micro-batches and files, and interface with native runtime engines like TensorFlow and Storm to execute them. It manages the application's lifecycle, including container-based deployment and a registry for state management. ECHO can schedule the dataflow on different Edge, Fog and Cloud resources, and also perform dynamic task migration between resources. We validate the ECHO platform for executing video analytics and sensor streams for Smart Traffic and Smart Utility applications on Raspberry Pi, NVidia TX1, ARM64 and Azure Cloud VM resources, and present our results.Comment: 17 pages, 5 figures, 2 tables, submitted to ICSOC-201

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper