Adaptive Trust Management and Data Process Time Optimization for Real-Time Spark Big Data Systems

Applications supporting businesses, smart systems, social networks, and advanced video applications such as eXtended Reality (XR) require large amounts of data processing to be provided in real-time. Therefore, the processing speed of big data systems is more important than ever. On the other hand,...

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Autores principales:	Seungwoo Seo, Jong-Moon Chung
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Trust management big data time bound optimization real-time processing security Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/7fba25351f6a4acca44955fbf4ebfcc2
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spelling	oai:doaj.org-article:7fba25351f6a4acca44955fbf4ebfcc22021-12-02T00:00:51ZAdaptive Trust Management and Data Process Time Optimization for Real-Time Spark Big Data Systems2169-353610.1109/ACCESS.2021.3129885https://doaj.org/article/7fba25351f6a4acca44955fbf4ebfcc22021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9623558/https://doaj.org/toc/2169-3536Applications supporting businesses, smart systems, social networks, and advanced video applications such as eXtended Reality (XR) require large amounts of data processing to be provided in real-time. Therefore, the processing speed of big data systems is more important than ever. On the other hand, protecting a big data system is not easy, as various types of nodes and clusters are supported by various wired and wireless networks. Commonly security procedures slow down the response time of big data networks, and therefore, enhanced security and performance speed techniques need to be co-designed into the system. In this paper, a trusted streaming adaptive failure-compensation (TSAF) scheme is proposed that uses a trust management scheme to identify malicious nodes in Spark big data systems, exclude them from job/task processing, and calculate the number of nodes that can satisfy the process’s object completion time. The TSAF scheme shows an improved processing performance when there are attacks on the big data system compared to other existing real-time big data processing schemes. For the case of no security attack, the results show that the processing time of TSAF is faster by about 1 ~ 2% compared to the existing big data processing schemes when the process completion object time is set to 0.5 s. Even when the ratio of malicious nodes performing security attacks on worker nodes reaches 0.5, the results show that TSAF can satisfy over 75% of the tasks within the object time, which is significantly higher compared to the existing big data processing schemes.Seungwoo SeoJong-Moon ChungIEEEarticleTrust managementbig datatime bound optimizationreal-time processingsecurityElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156372-156379 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Trust management big data time bound optimization real-time processing security Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Trust management big data time bound optimization real-time processing security Electrical engineering. Electronics. Nuclear engineering TK1-9971 Seungwoo Seo Jong-Moon Chung Adaptive Trust Management and Data Process Time Optimization for Real-Time Spark Big Data Systems
description	Applications supporting businesses, smart systems, social networks, and advanced video applications such as eXtended Reality (XR) require large amounts of data processing to be provided in real-time. Therefore, the processing speed of big data systems is more important than ever. On the other hand, protecting a big data system is not easy, as various types of nodes and clusters are supported by various wired and wireless networks. Commonly security procedures slow down the response time of big data networks, and therefore, enhanced security and performance speed techniques need to be co-designed into the system. In this paper, a trusted streaming adaptive failure-compensation (TSAF) scheme is proposed that uses a trust management scheme to identify malicious nodes in Spark big data systems, exclude them from job/task processing, and calculate the number of nodes that can satisfy the process’s object completion time. The TSAF scheme shows an improved processing performance when there are attacks on the big data system compared to other existing real-time big data processing schemes. For the case of no security attack, the results show that the processing time of TSAF is faster by about 1 ~ 2% compared to the existing big data processing schemes when the process completion object time is set to 0.5 s. Even when the ratio of malicious nodes performing security attacks on worker nodes reaches 0.5, the results show that TSAF can satisfy over 75% of the tasks within the object time, which is significantly higher compared to the existing big data processing schemes.
format	article
author	Seungwoo Seo Jong-Moon Chung
author_facet	Seungwoo Seo Jong-Moon Chung
author_sort	Seungwoo Seo
title	Adaptive Trust Management and Data Process Time Optimization for Real-Time Spark Big Data Systems
title_short	Adaptive Trust Management and Data Process Time Optimization for Real-Time Spark Big Data Systems
title_full	Adaptive Trust Management and Data Process Time Optimization for Real-Time Spark Big Data Systems
title_fullStr	Adaptive Trust Management and Data Process Time Optimization for Real-Time Spark Big Data Systems
title_full_unstemmed	Adaptive Trust Management and Data Process Time Optimization for Real-Time Spark Big Data Systems
title_sort	adaptive trust management and data process time optimization for real-time spark big data systems
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/7fba25351f6a4acca44955fbf4ebfcc2
work_keys_str_mv	AT seungwooseo adaptivetrustmanagementanddataprocesstimeoptimizationforrealtimesparkbigdatasystems AT jongmoonchung adaptivetrustmanagementanddataprocesstimeoptimizationforrealtimesparkbigdatasystems
_version_	1718403994674528256

Adaptive Trust Management and Data Process Time Optimization for Real-Time Spark Big Data Systems

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