A Data-driven Platform for Simulating Vehicular Fog Computing Environment

10.36227/techrxiv.17829398 ◽

2022 ◽

Author(s):

Ozgur Umut Akgul ◽

Wencan Mao ◽

Byungjin Cho ◽

Yu Xiao

Keyword(s):

Capacity Planning ◽

Fog Computing ◽

Deep Understanding ◽

Temporal Variations ◽

Edge Computing ◽

Business Strategies ◽

Open Platform ◽

Trade Offs ◽

Spatio Temporal ◽

Computing Capacity

<div>Edge/fog computing is a key enabling technology in 5G and beyond for fulfilling the tight latency requirements of compute-intensive vehicular applications such as cooperative driving. Concerning the spatio-temporal variation in the vehicular traffic flows and the demand for edge computing capacity generated by connected vehicles, vehicular fog computing (VFC) has been proposed as a cost-efficient deployment model that complements stationary fog nodes with mobile ones carried by moving vehicles. Accessing the feasibility and the applicability of such hybrid topology, and further planning and managing the networking and computing resources at the edge, require deep understanding of the spatio-temporal variations in the demand and the supply of edge computing capacity as well as the trade-offs between achievable Quality-of-Services and potential deployment and operating costs. To meet such requirements, we propose in this paper an open platform for simulating the VFC environment and for evaluating the performance and cost efficiency of capacity planning and resource allocation strategies under diverse physical conditions and business strategies. Compared with the existing edge/fog computing simulators, our platform supports the mobility of fog nodes and provides a realistic modeling of vehicular networking with the 5G and beyond network in the urban environment. We demonstrate the functionality of the platform using city-scale VFC capacity planning as example. The simulation results provide insights on the feasibility of different deployment strategies from both technical and financial perspectives.</div>

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Environmentally induced spatio‐temporal variations in the fecundity of brown trout Salmo trutta L.: trade‐offs between egg size and number

Freshwater Biology ◽

10.1046/j.1365-2427.1997.00217.x ◽

1997 ◽

Vol 38 (2) ◽

pp. 277-288 ◽

Cited By ~ 80

Author(s):

JAVIER LOBON‐CERVIA ◽

CARMEN UTRILLA ◽

PEDRO RINCÓN ◽

FELIPE AMEZCUA

Keyword(s):

Brown Trout ◽

Salmo Trutta ◽

Egg Size ◽

Temporal Variations ◽

Trade Offs ◽

Brown Trout Salmo Trutta ◽

Spatio Temporal

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Data-Driven Capacity Planning for Vehicular Fog Computing

10.36227/techrxiv.14555103.v1 ◽

2021 ◽

Author(s):

Wencan Mao ◽

Ozgur Umut Akgul ◽

Abbas Mehrabidavoodabadi ◽

Byungjin Cho ◽

Yu Xiao ◽

...

Keyword(s):

Capacity Planning ◽

Temporal Dynamics ◽

Fog Computing ◽

Network Capacity ◽

Base Stations ◽

Data Driven ◽

Traffic Density ◽

Potential Cost ◽

Operational Costs ◽

Spatio Temporal

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, Vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities. In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles. Previous works of VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much capacity to deploy, remains an open and challenging issue. The complexity of this problem comes from the mobility of vehicles, the spatio-temporal dynamics of vehicular traffic, and the computing resource demand generated by varying vehicular applications. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile fog nodes to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatio-temporal variation in computing demand. Through real-world experiments, we analyze the cost efficiency potential of VFC in long term and demonstrate that the performance loss of VFC is below $6\%$ compared to stationary deployment with equal network capacity. We also analyze the impacts of traffic patterns on the potential cost saving. The results show when the traffic density is higher, more operational costs will be saved in the long run due to more dense deployment of mobile fog nodes.

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Edge and Fog Computing in Healthcare – A Review

Scalable Computing Practice and Experience ◽

10.12694/scpe.v20i2.1504 ◽

2019 ◽

Vol 20 (2) ◽

pp. 191-206 ◽

Cited By ~ 2

Author(s):

Sujata Dash ◽

Sitanath Biswas ◽

Debajit Banerjee ◽

Atta UR Rahman

Keyword(s):

Healthcare System ◽

Fog Computing ◽

Edge Computing ◽

Wireless Devices ◽

Privacy Concerns ◽

Health Care Informatics ◽

Trade Offs ◽

Architectural Framework ◽

Edge And Fog Computing ◽

Care Informatics

The architectural framework of Fog and edge computing reveals that the network components which lie between the cloud and devices computes application oriented operations. In this paper, an in-depth review of fog and mist computing in the area of health care informatics is analyzed, classified, and discussed various applications cited in the literature. For that purpose, applications are classified into different categories and a list of application-oriented tasks that can be handled by fog and edge computing are enlisted. It is further added that on which layer of the network system such fog and edge computing tasks can be computed and trade-offs with respect to requirements relevant to healthcare are provided. The review undertaken in this paper focuses on three important areas: firstly, the enormous amount of computing tasks of healthcare system can take mileage of these two computing principles; secondly, the limitation of wireless devices can be overcome by having higher network tiers which can execute tasks and aggregate the data; and thirdly, privacy concerns and dependability prevent computation tasks to completely move to the cloud. Another area which has been considered in the study is how Edge and Fog computing can make the security algorithms more efficient. The findings of the study provide evidence of the need for a logical and consistent approach towards fog and mist computing in healthcare system.

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Data-Driven Capacity Planning for Vehicular Fog Computing

10.36227/techrxiv.14555103 ◽

2021 ◽

Author(s):

Wencan Mao ◽

Ozgur Umut Akgul ◽

Abbas Mehrabidavoodabadi ◽

Byungjin Cho ◽

Yu Xiao ◽

...

Keyword(s):

Capacity Planning ◽

Temporal Dynamics ◽

Fog Computing ◽

Network Capacity ◽

Base Stations ◽

Data Driven ◽

Traffic Density ◽

Potential Cost ◽

Operational Costs ◽

Spatio Temporal

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, Vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities. In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles. Previous works of VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much capacity to deploy, remains an open and challenging issue. The complexity of this problem comes from the mobility of vehicles, the spatio-temporal dynamics of vehicular traffic, and the computing resource demand generated by varying vehicular applications. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile fog nodes to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatio-temporal variation in computing demand. Through real-world experiments, we analyze the cost efficiency potential of VFC in long term and demonstrate that the performance loss of VFC is below $6\%$ compared to stationary deployment with equal network capacity. We also analyze the impacts of traffic patterns on the potential cost saving. The results show when the traffic density is higher, more operational costs will be saved in the long run due to more dense deployment of mobile fog nodes.

Download Full-text