Cloud-Link: January-March 2017 Issue

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Cloud-Link: Special Issue on Fog Computing


Fog computing seeks to extend cloud computing to the network edge. This facilitates data storage, computation and processing at network devices close to the users to enhance efficiency and to reduce latency. This issue of Cloud-Link is about fog computing, for which six recent articles have been selected to cover different aspects.


To support fog computing, the article “Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System” studies how to schedule tasks, manage resources and handle input/output interrupt requests for software-defined embedded system. The article “Foggy Clouds and Cloudy Fogs: a Real Need for Coordinated Management of Fog-to-Cloud Computing Systems” presents a layered architecture for fog-to-cloud computing systems and discusses the research challenges. The article “Fog Computing: Helping the Internet of Things Realize Its Potential” studies how fog computing can facilitate Internet of Things, particularly to handle the large amount of data. The article “Fog-computing-based Radio Access Networks: Issues and Challengesdiscusses a Fog-computing-based radio access networks for supporting the fifth generation wireless communications systems including the research challenges. The article “Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures” introduces a vehicular fog computing system based on the use of communications and computing resources of collaborative vehicles. Last but not least, the article “Fog Computing May Help to Save Energy in Cloud Computing” investigates how nano data centers can work with centralized data centers for energy saving purposes.


We hope that this issue of Cloud-Link can provide you with useful references to explore this important and interesting topic further. Articles have been selected based on various considerations (e.g., variety, relevancy, anticipated readers' interests) and unavoidably there are many other useful and insightful articles that have not been included. You are also encouraged to search through IEEE Xplore and other databases for further reading.

We are looking for topics for the upcoming issues. If you have any suggestions, please email them to

Henry Chan, Victor Leung, Jens Jensen and Tomasz Wiktorski



Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System


Deze Zeng; Lin Gu; Song Guo; Zixue Cheng; Shui Yu


Published in IEEE Transactions on Computers, December 2016


Traditional standalone embedded system is limited in their functionality, flexibility, and scalability. Fog computing platform, characterized by pushing the cloud services to the network edge, is a promising solution to support and strengthen traditional embedded system. Resource management is always a critical issue to the system performance. In this paper, we consider a fog computing supported software-defined embedded system, where task images lay in the storage server while computations can be conducted on either embedded device or a computation server. It is significant to design an efficient task scheduling and resource management strategy with minimized task completion time for promoting the user experience. To this end, three issues are investigated in this paper: 1) how to balance the workload on a client device and computation servers, i.e., task scheduling, 2) how to place task images on storage servers, i.e., resource management, and 3) how to balance the I/O interrupt requests among the storage servers. They are jointly considered and formulated as a mixed-integer nonlinear programming problem. To deal with its high computation complexity, a computation-efficient solution is proposed based on our formulation and validated by extensive simulation based studies.



Foggy Clouds and Cloudy Fogs: a Real Need for Coordinated Management of Fog-to-Cloud Computing Systems


Xavi Masip-Bruin; Eva Marín-Tordera; Ghazal Tashakor; Admela Jukan; Guang-Jie Ren


Published in IEEE Wireless Communications, October 2016

The recent advances in cloud services technology are fueling a plethora of information technology innovation, including networking, storage, and computing. Today, various flavors have evolved of IoT, cloud computing, and so-called fog computing, a concept referring to capabilities of edge devices and users' clients to compute, store, and exchange data among each other and with the cloud. Although the rapid pace of this evolution was not easily foreseeable, today each piece of it facilitates and enables the deployment of what we commonly refer to as a smart scenario, including smart cities, smart transportation, and smart homes. As most current cloud, fog, and network services run simultaneously in each scenario, we observe that we are at the dawn of what may be the next big step in the cloud computing and networking evolution, whereby services might be executed at the network edge, both in parallel and in a coordinated fashion, as well as supported by the unstoppable technology evolution. As edge devices become richer in functionality and smarter, embedding capacities such as storage or processing, as well as new functionalities, such as decision making, data collection, forwarding, and sharing, a real need is emerging for coordinated management of fog-to-cloud (F2C) computing systems. This article introduces a layered F2C architecture, its benefits and strengths, as well as the arising open and research challenges, making the case for the real need for their coordinated management. Our architecture, the illustrative use case presented, and a comparative performance analysis, albeit conceptual, all clearly show the way forward toward a new IoT scenario with a set of existing and unforeseen services provided on highly distributed and dynamic compute, storage, and networking resources, bringing together heterogeneous and commodity edge devices, emerging fogs, as well as conventional clouds. 7721750



FogComputing: Helping the Internet of Things Realize Its Potential

Amir Vahid Dastjerdi;Rajkumar Buyya


Published in IEEE Computer, August 2016

The Internet of Things (IoT) could enable innovations that enhance the quality of life, but it generates unprecedented amounts of data that are difficult for traditional systems, the cloud, and even edge computing to handle. Fog computing is designed to overcome these limitations. 7543455



Fog-computing-based Radio Access Networks: Issues and Challenges


Mugen Peng; Shi Yan; Kecheng Zhang; Chonggang Wang


Published in IEEE Network, July-August 2016

An F-RAN is presented in this article as a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency. The core idea is to take full advantage of local radio signal processing, cooperative radio resource management, and distributed storing capabilities in edge devices, which can decrease the heavy burden on front haul and avoid large-scale radio signal processing in the centralized baseband unit pool. This article comprehensively presents the system architecture and key techniques of F-RANs. In particular, key techniques and their corresponding solutions, including transmission mode selection and interference suppression, are discussed. Open issues in terms of edge caching, software-defined networking, and network function virtualization are also identified. 7513863


Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

Xueshi Hou; Yong Li; Min Chen; Di Wu; Depeng Jin; Sheng Chen


Published in IEEE Transactions on Vehicular Technology, June 2016


With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information 7415983



Fog Computing May Help to Save Energy in Cloud Computing


Fatemeh Jalali; Kerry Hinton; Robert Ayre; Tansu Alpcan; Rodney S. Tucker


Published in IEEE Journal on Selected Areas in Communications, May 2016


Tiny computers located in end-user premises are becoming popular as local servers for Internet of Things (IoT) and Fog computing services. These highly distributed servers that can host and distribute content and applications in a peer-to-peer (P2P) fashion are known as nano data centers (nDCs). Despite the growing popularity of nano servers, their energy consumption is not well-investigated. To study energy consumption of nDCs, we propose and use flow-based and time-based energy consumption models for shared and unshared network equipment, respectively. To apply and validate these models, a set of measurements and experiments are performed to compare energy consumption of a service provided by nDCs and centralized data centers (DCs). A number of findings emerge from our study, including the factors in the system design that allow nDCs to consume less energy than its centralized counterpart. These include the type of access network attached to nano servers and nano server's time utilization (the ratio of the idle time to active time). Additionally, the type of applications running on nDCs and factors such as number of downloads, number of updates, and amount of preloaded copies of data influence the energy cost. Our results reveal that number of hops between a user and content has little impact on the total energy consumption compared to the above-mentioned factors. We show that nano servers in Fog computing can complement centralized DCs to serve certain applications, mostly IoT applications for which the source of data is in end-user premises, and lead to energy saving if the applications (or a part of them) are off-loadable from centralized DCs and run on nDCs. 7439752


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