Capacity Planning of Fog Computing Infrastructures for Smart Monitoring

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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A Data-driven Platform for Simulating Vehicular Fog Computing Environment

10.36227/techrxiv.17829398.v1 ◽

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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Stochastic performance model for web server capacity planning in fog computing

The Journal of Supercomputing ◽

10.1007/s11227-020-03218-w ◽

2020 ◽

Vol 76 (12) ◽

pp. 9533-9557

Author(s):

Paulo Pereira ◽

Jean Araujo ◽

Matheus Torquato ◽

Jamilson Dantas ◽

Carlos Melo ◽

...

Keyword(s):

Capacity Planning ◽

Fog Computing ◽

Web Server ◽

Performance Model

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Capacity Planning of Fog Computing Infrastructures under Probabilistic Delay Guarantees

2019 IEEE International Conference on Fog Computing (ICFC) ◽

10.1109/icfc.2019.00030 ◽

2019 ◽

Cited By ~ 2

Author(s):

Ioanna Stypsanelli ◽

Olivier Brun ◽

Samir Medjiah ◽

Balakrishna J. Prabhu

Keyword(s):

Capacity Planning ◽

Fog Computing ◽

Delay Guarantees

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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>

Download Full-text

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