scholarly journals Edge Computing-Empowered Large-Scale Traffic Data Recovery Leveraging Low-Rank Theory

2020 ◽  
Vol 7 (4) ◽  
pp. 2205-2218 ◽  
Author(s):  
Chaocan Xiang ◽  
Zhao Zhang ◽  
Yuben Qu ◽  
Dongyu Lu ◽  
Xiaochen Fan ◽  
...  
Keyword(s):  
Large Scale ◽  
Edge Computing ◽  
Data Recovery ◽  
Low Rank ◽  
Traffic Data

scholarly journals A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Liang Fu Lu ◽  
Zheng-Hai Huang ◽  
Mohammed A. Ambusaidi ◽  
Kui-Xiang Gou
Keyword(s):  
Network Traffic ◽  
Large Scale ◽  
Low Rank ◽  
Prototype System ◽  
Traffic Data ◽  
Proximal Gradient Method ◽  
Large Scale Network ◽  
Network Operation ◽  
Efficient Data ◽  
Scale Network

With the rapid growth of data communications in size and complexity, the threat of malicious activities and computer crimes has increased accordingly as well. Thus, investigating efficient data processing techniques for network operation and management over large-scale network traffic is highly required. Some mathematical approaches on flow-level traffic data have been proposed due to the importance of analyzing the structure and situation of the network. Different from the state-of-the-art studies, we first propose a new decomposition model based on accelerated proximal gradient method for packet-level traffic data. In addition, we present the iterative scheme of the algorithm for network anomaly detection problem, which is termed as NAD-APG. Based on the approach, we carry out the intrusion detection for packet-level network traffic data no matter whether it is polluted by noise or not. Finally, we design a prototype system for network anomalies detection such as Probe and R2L attacks. The experiments have shown that our approach is effective in revealing the patterns of network traffic data and detecting attacks from large-scale network traffic. Moreover, the experiments have demonstrated the robustness of the algorithm as well even when the network traffic is polluted by the large volume anomalies and noise.

scholarly journals Joint optimization of network selection and task offloading for vehicular edge computing

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Access Point ◽  
Edge Computing ◽  
Network Selection ◽  
Network Access ◽  
Task Execution ◽  
Point Selection ◽  
Vehicle Movement ◽  
Task Offloading

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

scholarly journals A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

2020 ◽  
Vol 2 (1) ◽  
pp. 92
Author(s):  
Rahim Rahmani ◽  
Ramin Firouzi ◽  
Sachiko Lim ◽  
Mahbub Alam
Keyword(s):  
Large Scale ◽  
Cluster Head ◽  
Edge Computing ◽  
Computing Technology ◽  
Proximal Algorithm ◽  
Data Intensive ◽  
Distributed Ledger ◽  
Smart Contract ◽  
Distributed Ledger Technology ◽  
Iot Devices

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.

When Taxi Meets Bus: Night Bus Stop Planning over Large-Scale Traffic Data

2016 ◽  
Author(s):  
Luyan Xiao ◽  
Xiaopeng Fan ◽  
Haixia Mao ◽  
Chengzhong Xu ◽  
Ping Lu ◽  
...  
Keyword(s):  
Large Scale ◽  
Traffic Data ◽  
Bus Stop

hIPPYlib

2021 ◽  
Vol 47 (2) ◽  
pp. 1-34
Author(s):  
Umberto Villa ◽  
Noemi Petra ◽  
Omar Ghattas
Keyword(s):  
Inverse Problems ◽  
Large Scale ◽  
Low Rank ◽  
Scalable Algorithms ◽  
Low Rank Approximation ◽  
Dimensional Parameter ◽  
Infinite Dimensional ◽  
Bayesian Inverse Problems ◽  
Rank Approximation ◽  
Extensible Software

We present an extensible software framework, hIPPYlib, for solution of large-scale deterministic and Bayesian inverse problems governed by partial differential equations (PDEs) with (possibly) infinite-dimensional parameter fields (which are high-dimensional after discretization). hIPPYlib overcomes the prohibitively expensive nature of Bayesian inversion for this class of problems by implementing state-of-the-art scalable algorithms for PDE-based inverse problems that exploit the structure of the underlying operators, notably the Hessian of the log-posterior. The key property of the algorithms implemented in hIPPYlib is that the solution of the inverse problem is computed at a cost, measured in linearized forward PDE solves, that is independent of the parameter dimension. The mean of the posterior is approximated by the MAP point, which is found by minimizing the negative log-posterior with an inexact matrix-free Newton-CG method. The posterior covariance is approximated by the inverse of the Hessian of the negative log posterior evaluated at the MAP point. The construction of the posterior covariance is made tractable by invoking a low-rank approximation of the Hessian of the log-likelihood. Scalable tools for sample generation are also discussed. hIPPYlib makes all of these advanced algorithms easily accessible to domain scientists and provides an environment that expedites the development of new algorithms.

scholarly journals Observing the Tropical Atmosphere in Moisture Space

2018 ◽  
Vol 75 (10) ◽  
pp. 3313-3330 ◽  
Author(s):  
Hauke Schulz ◽  
Bjorn Stevens
Keyword(s):  
Large Scale ◽  
Deep Convection ◽  
Circulation Pattern ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Low Rank ◽  
Substantial Part ◽  
Tropical Atmosphere ◽  
Shallow Clouds ◽  
Self Aggregation

Measurements from the Barbados Cloud Observatory are analyzed to identify the processes influencing the distribution of moist static energy and the large-scale organization of tropical convection. Five years of water vapor and cloud profiles from a Raman lidar and cloud radar are composed to construct the structure of the observed atmosphere in moisture space. The large-scale structure of the atmosphere is similar to that now familiar from idealized studies of convective self-aggregation, with shallow clouds prevailing over a moist marine layer in regions of low-rank humidity, and deep convection in a nearly saturated atmosphere in regions of high-rank humidity. With supplementary reanalysis datasets the overall circulation pattern is reconstructed in moisture space, and shows evidence of a substantial lower-tropospheric component to the circulation. This shallow component of the circulation helps support the differentiation between the moist and dry columns, similar to what is found in simulations of convective self-aggregation. Radiative calculations show that clear-sky radiative differences can explain a substantial part of this circulation, with further contributions expected from cloud radiative effects. The shallow component appears to be important for maintaining the low gross moist stability of the convecting column. A positive feedback between a shallow circulation driven by differential radiative cooling and the low-level moisture gradients that help support it is hypothesized to play an important role in conditioning the atmosphere for deep convection. The analysis suggests that the radiatively driven shallow circulations identified by modeling studies as contributing to the self-aggregation of convection in radiative–convective equilibrium similarly play a role in shaping the intertropical convergence zone and, hence, the large-scale structure of the tropical atmosphere.

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