scholarly journals Study on network architecture of big data center for the efficient control of huge data traffic

2014 ◽  
Vol 11 (3) ◽  
pp. 1113-1126 ◽  
Author(s):  
Hyoung Park ◽  
Il Yeo ◽  
Jongsuk Lee ◽  
Haengjin Jang
Keyword(s):  
Big Data ◽  
Data Center ◽  
Traffic Congestion ◽  
Network Architecture ◽  
Traffic Management ◽  
Data Centers ◽  
Data Service ◽  
Paradigm Shifts ◽  
Data Traffic ◽  
Huge Data

The network architecture of typical data centersis characterized bytiered networks and aggregation-based traffic controls. The emergence of big data makes it difficult for these data centers to incorporate big data service. The tier and aggregation based traffic management systems can magnify the seriousness of the traffic congestion and extend the congested region when big data moves around in the data center. As a consequence, big data has been forcing data centers to change their architecture dramatically. In this paper, we first address the important paradigm shifts of network architecture caused by big data traffic. We then show the new network architecture which resulted from our experience of the CERN LHC data service. Finally, we illustrate the effect of the throughput improvements of the proposed network architecture using a NS2 simulation.

The Challenges, Technologies, and Role of Fog Computing in the Context of Industrial Internet of Things

2021 ◽  
pp. 1-16
Author(s):  
Sasikala Chinthakunta ◽  
Shoba Bindu Chigarapalle ◽  
Sudheer Kumar E.
Keyword(s):  
Data Storage ◽  
Smart Grids ◽  
Traffic Management ◽  
Fog Computing ◽  
Poor Quality ◽  
Data Traffic ◽  
Cloud Data ◽  
Security Issues ◽  
Huge Data ◽  
Iot Devices

Typically, the analysis of the industrial big data is done at the cloud. If the technology of IIoT is relying on cloud, data from the billions of internet-connected devices are voluminous and demand to be processed within the cloud DCs. Most of the IoT infrastructures—smart driving and car parking systems, smart vehicular traffic management systems, and smart grids—are observed to demand low-latency, real-time services from the service providers. Since cloud includes data storage, processing, and computation only within DCs, huge data traffic generated from the IoT devices probably experience a network bottleneck, high service latency, and poor quality of service (QoS). Hence, the placement of an intermediary node that can perform tasks efficiently and effectively is an unavoidable requirement of IIoT. Fog can be such an intermediary node because of its ability and location to perform tasks at the premise of an industry in a timely manner. This chapter discusses challenges, need, and framework of fog computing, security issues, and solutions of fog computing for IIoT.

scholarly journals Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center

2019 ◽  
Vol 9 (16) ◽  
pp. 3223
Author(s):  
Jargalsaikhan Narantuya ◽  
Taejin Ha ◽  
Jaewon Bae ◽  
Hyuk Lim
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Principal Component ◽  
Service Performance ◽  
Data Traffic ◽  
Vm Placement ◽  
Matching Method ◽  
Placement Method ◽  
Underlying Network

In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating additional VMs. If highly dependent VMs are placed in different physical machines (PMs), the data traffic increases in the underlying physical network of the data center. To reduce the amount of data traffic in the underlying network and improve the service performance, we propose a traffic-dependency-based strategy for VM placement in software-defined data center (SDDC). The traffic dependencies between the VMs are analyzed by principal component analysis, and highly dependent VMs are grouped by gravity-based clustering. Each group of highly dependent VMs is placed within an appropriate PM based on the Hungarian matching method. This strategy of dependency-based VM placement facilitates reducing data traffic volume of the data center, since the highly dependent VMs are placed within the same PM. The results of the performance evaluation in SDDC testbed indicate that the proposed VM placement method efficiently reduces the amount of data traffic in the underlying network and improves the data center performance.

Big Data in Telecommunications

2015 ◽  
pp. 67-81
Author(s):  
Yen Pei Tay ◽  
Vasaki Ponnusamy ◽  
Lam Hong Lee
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Traffic Congestion ◽  
Big Data Analytics ◽  
Mobile Network ◽  
Consumer Electronics ◽  
Data Traffic ◽  
Network Discovery ◽  
Crowd Intelligence ◽  
4G Lte

The meteoric rise of smart devices in dominating worldwide consumer electronics market complemented with data-hungry mobile applications and widely accessible heterogeneous networks e.g. 3G, 4G LTE and Wi-Fi, have elevated Mobile Internet from a ‘nice-to-have' to a mandatory feature on every mobile computing device. This has spurred serious data traffic congestion on mobile networks as a consequence. The nature of mobile network traffic today is more like little Data Tsunami, unpredictable in terms of time and location while pounding the access networks with waves of data streams. This chapter explains how Big Data analytics can be applied to understand the Device-Network-Application (DNA) dimensions in annotating mobile connectivity routine and how Simplify, a seamless network discovery solution developed at Nextwave Technology, can be extended to leverage crowd intelligence in predicting and collaboratively shaping mobile data traffic towards achieving real-time network congestion control. The chapter also presents the Big Data architecture hosted on Google Cloud Platform powering the backbone behind Simplify in realizing its intelligent traffic steering solution.

Parallel CNN based big data visualization for traffic monitoring

2020 ◽  
Vol 39 (3) ◽  
pp. 2679-2691
Author(s):  
G. Madhukar Rao ◽  
Dharavath Ramesh
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Temporal Dynamics ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Traffic Prediction ◽  
Data Traffic ◽  
Traffic Condition

In a real-time application such as traffic monitoring, it is required to process the enormous amount of data. Traffic prediction is essential for intelligent transportation systems (ITSs), traffic management authorities, and travelers. Traffic prediction has become a challenging task due to various non-linear temporal dynamics at different locations, complicated underlying spatial dependencies, and more extended step forecasting. To accommodate these instances, efficient visualization and data mining techniques are required to predict and analyze the massive amount of traffic big data. This paper presents a deep learning-based parallel convolutional neural network (Parallel-CNN) methodology to predict the traffic conditions of a specific region. The methodology of deep learning contains multiple processing layers and performs various computational strategies, which is used to learn representations of data with multilevel abstraction. The data has captured from the department of transportation; thus, the size of data is vast, and it can be analyzed to get the behavior of the traffic condition. The purpose of this paper is to monitor traffic behavior, which enables the user to make decisions to build the traffic-free cities. Experimental results show that the proposed methodology outperforms other existing methods such as KNN, CNN, and FIMT-DD.

scholarly journals Traffic management of Udayana University Sudirman campus intersection using Vissim software

2019 ◽  
Vol 276 ◽  
pp. 03006
Author(s):  
Putu Alit Suthanaya ◽  
Ngurah Upadiana
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Signal Timing ◽  
Data Traffic ◽  
University Campus ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Traffic Flow Management ◽  
The City

The city of Denpasar is the capital of Bali Province, as well as the centre of various activities including government offices, hospitals, schools/ universities, trade and services, as well as tourism. Traffic congestion in the city of Denpasar is increasing from year to year, especially at the point of the intersection node, such as at the signalised intersection of Udayana University Sudirman Campus. The high trip attraction to Udayana University Campus has exacerbated the congestion of the intersection. The purpose of this study was to examine an alternative to managing the traffic flow using the Vissim software. The required data included the intersection geometric data, traffic volume and signal timing. The calculation of the intersection’s performance was conducted based on the Indonesian Highway Capacity Manual (IHCM). The simulation of the traffic flow management was conducted with the help of the Vissim software. The results of the performance analysis of the intersection conduced using Vissim Software indicated that the application of Vissim software was valid. The traffic management proposed has reduced both queue length and delay time.

Infrastructure of data centers for transferring big data traffic: A survey research

2015 ◽  
Author(s):  
Emad Soltani Nejad ◽  
Mohammad Reza Majma ◽  
Bardia Izadpanahi ◽  
Seyed Bagher Hashemi Natanzi ◽  
Hamid Reza Navaei
Keyword(s):  
Big Data ◽  
Survey Research ◽  
Data Centers ◽  
Data Traffic
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