A Comprehensive Survey on Architecture for Big Data Processing in Mobile Edge Computing Environments

2018 ◽  
pp. 33-49 ◽  
Author(s):  
Maninder Jeet Kaur
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Big Data Processing ◽  
Comprehensive Survey ◽  
Computing Environments

scholarly journals RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing Environments

2017 ◽  
Vol 6 (3) ◽  
pp. 17 ◽  
Author(s):  
Muhammad Habib ur Rehman ◽  
Prem Jayaraman ◽  
Saif Malik ◽  
Atta Khan ◽  
Mohamed Medhat Gaber
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Big Data Processing ◽  
Computing Environments

Big Data Processing in Cloud Computing Environments

2017 ◽  
Vol 23 (11) ◽  
pp. 11092-11095 ◽  
Author(s):  
A Noraziah ◽  
Mohammed Adam Ibrahim Fakherldin ◽  
Khalid Adam ◽  
Mazlina Abdul Majid
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Big Data Processing ◽  
Computing Environments

Fog computing: from architecture to edge computing and big data processing

2018 ◽  
Vol 75 (4) ◽  
pp. 2070-2105 ◽  
Author(s):  
Simar Preet Singh ◽  
Anand Nayyar ◽  
Rajesh Kumar ◽  
Anju Sharma
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Fog Computing ◽  
Edge Computing ◽  
Big Data Processing

scholarly journals Resource Management and Scheduling for Big Data Applications in Cloud Computing Environments

2019 ◽  
pp. 1-23 ◽  
Author(s):  
Muhammed Tawfiqul Islam ◽  
Rajkumar Buyya
Keyword(s):  
Big Data ◽  
Resource Management ◽  
Data Processing ◽  
Job Scheduling ◽  
Big Data Processing ◽  
Big Data Applications ◽  
Art Practices ◽  
Core Components ◽  
Management Techniques ◽  
Computing Environments

This chapter presents software architectures of the big data processing platforms. It also provides in-depth knowledge on resource management techniques involved while deploying big data processing systems in the cloud environment. It starts from the very basics and gradually introduce the core components of resource management which are divided into multiple layers. It covers the state-of-art practices and researches done in SLA-based resource management with a specific focus on the job scheduling mechanisms.

scholarly journals Construction of 3D GIS based on VR and Edge Computing

2021 ◽  
Vol 2138 (1) ◽  
pp. 012027
Author(s):  
Fei Wang ◽  
Chao Zhou ◽  
Yanlong Chen ◽  
Xindi Jing
Keyword(s):  
Virtual Reality ◽  
Big Data ◽  
Data Processing ◽  
Performance Optimization ◽  
Spatial Information ◽  
Three Dimensional ◽  
Geographic Information ◽  
Edge Computing ◽  
3D Gis ◽  
Big Data Processing

Abstract Geographic Information System (GIS) is a subject that studies spatial information science and computer system for collecting, storing, retrieving, modeling, analyzing and representing geospatial data. GIS not only manages attribute information based on numbers and text, but also manages spatial information based on visual graphics and images. With the rapid development of computer technology, space technology and information technology, the expression form of map is gradually changing from two-dimensional to three-dimensional. 3D GIS can express 3D spatial information in detail and has powerful multi-dimensional spatial analysis function. However, 3D visualization technology can not be immersed in geographic information. The process of requesting information by each end user is energy intensive. If big data processing is adopted, it will greatly increase the computing pressure of big data center. The paper presents a 3D GIS construction method based on Virtual Reality (VR) and edge computing (gis-ve). Specifically, firstly, virtual reality equipment is deployed at the user terminal to realize the organic integration of real scene and virtual object in three-dimensional space, and build a real and perfect three-dimensional real world space. Secondly, under the edge computing architecture, virtual reality applications are deployed on edge computing nodes to reduce the number of device hops transmitted between virtual reality application data and end users, reduce network processing delay and reduce the computing pressure of big data processing center. Then, the deployment problem of GIS is transformed into a performance optimization problem, and the delay and cost are used as evaluation indexes. Finally, an improved artificial bee colony algorithm is proposed to solve the optimal GIS construction scheme. In order to verify the feasibility and effectiveness of this method, the paper compares this method with two other advanced 3D-GIS construction methods, and experiments show that this method performs well in terms of delay and cost.

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