In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.

Fog Computing: A Taxonomy, Survey and Future Directions

Survey on fog computing

Cloud computing with its three key facets (i.e., Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service level agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices, or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This paper presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as tactile Internet.

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A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named “Fog computing” has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features. We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.

Fog computing is an emerging paradigm that extends computation, communication, and storage facilities toward the edge of a network. Compared to traditional cloud computing, fog computing can support delay-sensitive service requests from end-users (EUs) with reduced energy consumption and low traffic congestion. Basically, fog networks are viewed as offloading to core computation and storage. Fog nodes in fog computing decide to either process the services using its available resource or send to the cloud server. Thus, fog computing helps to achieve efficient resource utilization and higher performance regarding the delay, bandwidth, and energy consumption. This survey starts by providing an overview and fundamental of fog computing architecture. Furthermore, service and resource allocation approaches are summarized to address several critical issues such as latency, and bandwidth, and energy consumption in fog computing. Afterward, compared to other surveys, this paper provides an extensive overview of state-of-the-art network applications and major research aspects to design these networks. In addition, this paper highlights ongoing research effort, open challenges, and research trends in fog computing.

Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges

Challenging 5G requirements have been proposed to enable the provision of more satisfactory services to users. One of the most critical 5G enhancements is extremely low latency compared with that of 4G. Various 5G applications will be supported by Cloud computing services. However, Cloud services sometimes have a long communication distance because of the physical location of data centers. Fog computing architecture is being introduced in 5G cellular networks to shorten the latency arising from the location of data centers. The communication delay as well as the computing delay must be considered for latency-sensitive applications. A Fog server serving many users will probably have a longer computing delay than a Fog server with a lighter workload. To clarify the computing delay and communication delay in Fog architecture, we define a mathematical model of a Fog network and the important related parameters. We also analyze results from a model used to evaluate three different policies for selecting the target Fog server for each task. We conducted simulations to evaluate this model.

Analysis of fog model considering computing and communication latency in 5G cellular networks

Title Simulation Study of Low Latency Network Architecture Using Mobile Edge Computing Author(s) INTHARAWIJITR, Krittin; IIDA, Katsuyoshi; KOGA, Hiroyuki Citation IEICE Transactions on Information and Systems, E100.D(5), 963-972 https://doi.org/10.1587/transinf.2016NTP0003 Issue Date 2017-05 Doc URL http://hdl.handle.net/2115/71035 Rights Copyright ©2018 The Institute of Electronics, Information and Communication Engineers Type article File Information E100.D_2016NTP0003.pdf

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Simulation Study of Low Latency Network Architecture Using Mobile Edge Computing

Most of latency-sensitive mobile applications require computational resources provided through a cloud computing service. The problem of relying on cloud computing is that, sometimes, the physical locations of cloud servers are distant from mobile users and the communication latency is long. As a result, the concept of distributed cloud service, called mobile edge computing (MEC), is being introduced in the 5G network. However, MEC can reduce only the communication latency. The computing latency in MEC must also be considered to satisfy the required total latency of services. In this research, we study the impact of both latencies in MEC architecture with regard to latency-sensitive services. We also consider a centralized model, in which we use a controller to manage flows between users and mobile edge resources to analyze MEC in a practical architecture. The simulation results show that the controller interval and hop delay lead some blocking and error in the system. However the permissive system which relaxes latency constrains and chooses an edge server by the lowest total latency can improve the system performance impressively.

Practical Enhancement and Evaluation of a Low-Latency Network Model Using Mobile Edge Computing

Most of latency-sensitive mobile applications depend on computational resources provided by a cloud computing service. The problem of relying on cloud computing is that, sometimes, the physical locations of cloud servers are distant from mobile users and the communication latency is long. As a result, the concept of distributed cloud service, called mobile edge computing (MEC), is being introduced in the 5G network. However, MEC can reduce only the communication latency. The computing latency in MEC must also be considered to satisfy the required total latency of services. In this research, we study the impact of both latencies in MEC architecture with regard to latency-sensitive services. We also consider a centralized model, in which we use a controller to manage flows between users and mobile edge resources to analyze MEC in a practical architecture. Simulations show that the interval and controller latency trigger some blocking and error in the system. However, the permissive system which relaxes latency constraints and chooses an edge server by the lowest total latency can improve the system performance impressively. key words: mobile edge computing, low-latency network, orchestrator, processor sharing, simulation

Simulation Study of Low-Latency Network Model with Orchestrator in MEC

The Internet of Things (IoT) with its support for cyberphysical systems (CPS) will provide many latency-sensitive services that require very fast responses from network services. Mobile edge computing (MEC), one of the distributed computing models, is a promising component of the low-latency network architecture. In network architectures with MEC, mobile devices will offload heavy computing tasks to edge servers. There exist numbers of researches about low-latency network architecture with MEC. However, none of the existing researches simultaneously satisfy the followings: (1) guarantee the latency of computing tasks and (2) implement a real system. In this paper, we designed and implemented an MEC based network architecture that guarantees the latency of offloading tasks. More specifically, we first estimate the total latency including computing and communication ones at the centralized node called orchestrator. If the estimated value exceeds the latency requirement, the task will be rejected. We then evaluated its performance in terms of the blocking probability of the tasks. To analyze the results, we compared the performance between obtained from experiments and simulations. Based on the comparisons, we clarified that the computing latency estimation accuracy is a significant factor for this system. key words: mobile edge computing, low-latency network, prototype, and experiment

Krittin Intharawijitr

Papers

Analysis of fog model considering computing and communication latency in 5G cellular networks

Simulation Study of Low Latency Network Architecture Using Mobile Edge Computing

Practical Enhancement and Evaluation of a Low-Latency Network Model Using Mobile Edge Computing

Simulation Study of Low-Latency Network Model with Orchestrator in MEC

Empirical Study of Low-Latency Network Model with Orchestrator in MEC