scholarly journals Data-Driven Capacity Planning for Vehicular Fog Computing

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.

scholarly journals Data-Driven Capacity Planning for Vehicular Fog Computing

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.

scholarly journals A Data-driven Platform for Simulating Vehicular Fog Computing Environment

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>

scholarly journals Data driven analysis of low frequency spatio-temporal dynamics in resting state MRI (rsMRI) data

2012 ◽  
Vol 13 (S1) ◽  
Author(s):  
Martha Willis ◽  
Lukas Hoffman ◽  
Alessio Medda ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Temporal Dynamics ◽  
Low Frequency ◽  
Data Driven ◽  
Spatio Temporal

STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging

2015 ◽  
Vol 25 (04) ◽  
pp. 1550016 ◽  
Author(s):  
Ke Liu ◽  
Zhu Liang Yu ◽  
Wei Wu ◽  
Zhenghui Gu ◽  
Yuanqing Li
Keyword(s):  
Empirical Bayes ◽  
Temporal Dynamics ◽  
A Priori ◽  
Data Driven ◽  
Great Promise ◽  
Source Imaging ◽  
Scalable Algorithm ◽  
Space Modeling ◽  
Spatially Extended ◽  
Spatio Temporal

For M/EEG-based distributed source imaging, it has been established that the L2-norm-based methods are effective in imaging spatially extended sources, whereas the L1-norm-based methods are more suited for estimating focal and sparse sources. However, when the spatial extents of the sources are unknown a priori, the rationale for using either type of methods is not adequately supported. Bayesian inference by exploiting the spatio-temporal information of the patch sources holds great promise as a tool for adaptive source imaging, but both computational and methodological limitations remain to be overcome. In this paper, based on state-space modeling of the M/EEG data, we propose a fully data-driven and scalable algorithm, termed STRAPS, for M/EEG patch source imaging on high-resolution cortices. Unlike the existing algorithms, the recursive penalized least squares (RPLS) procedure is employed to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering/smoothing. Furthermore, the coefficients of the multivariate autoregressive (MVAR) model characterizing the spatial-temporal dynamics of the patch sources are estimated in a principled manner via empirical Bayes. Extensive numerical experiments demonstrate STRAPS's excellent performance in the estimation of locations, spatial extents and amplitudes of the patch sources with varying spatial extents.

scholarly journals A Data-driven Platform for Simulating Vehicular Fog Computing Environment

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>

Spatial and temporal dynamics of Pacific capelin Mallotus catervarius in the Gulf of Alaska: implications for ecosystem-based fisheries management

2020 ◽  
Vol 637 ◽  
pp. 117-140 ◽  
Author(s):  
DW McGowan ◽  
ED Goldstein ◽  
ML Arimitsu ◽  
AL Deary ◽  
O Ormseth ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Marine Ecosystem ◽  
Gulf Of Alaska ◽  
Data Series ◽  
Limited Information ◽  
Important Species ◽  
Multiple Data ◽  
Spatial And Temporal Dynamics ◽  
Spawning Areas ◽  
Spatio Temporal

Pacific capelin Mallotus catervarius are planktivorous small pelagic fish that serve an intermediate trophic role in marine food webs. Due to the lack of a directed fishery or monitoring of capelin in the Northeast Pacific, limited information is available on their distribution and abundance, and how spatio-temporal fluctuations in capelin density affect their availability as prey. To provide information on life history, spatial patterns, and population dynamics of capelin in the Gulf of Alaska (GOA), we modeled distributions of spawning habitat and larval dispersal, and synthesized spatially indexed data from multiple independent sources from 1996 to 2016. Potential capelin spawning areas were broadly distributed across the GOA. Models of larval drift show the GOA’s advective circulation patterns disperse capelin larvae over the continental shelf and upper slope, indicating potential connections between spawning areas and observed offshore distributions that are influenced by the location and timing of spawning. Spatial overlap in composite distributions of larval and age-1+ fish was used to identify core areas where capelin consistently occur and concentrate. Capelin primarily occupy shelf waters near the Kodiak Archipelago, and are patchily distributed across the GOA shelf and inshore waters. Interannual variations in abundance along with spatio-temporal differences in density indicate that the availability of capelin to predators and monitoring surveys is highly variable in the GOA. We demonstrate that the limitations of individual data series can be compensated for by integrating multiple data sources to monitor fluctuations in distributions and abundance trends of an ecologically important species across a large marine ecosystem.

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