Journal
IEEE ACCESS
Volume 9, Issue -, Pages 156372-156379Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3129885
Keywords
Trust management; big data; time bound optimization; real-time processing; security
Categories
Funding
- Ministry of Science and ICT (MSIT), South Korea, under the Information Technology Research Center (ITRC) support Program through the Institute for Information and Communications Technology Planning and Evaluation (IITP) [IITP-2021-2018-0-01799]
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Security and processing speed are key challenges in big data systems, necessitating coordinated design of security and performance techniques to counter various attacks. The TSAF scheme provides enhanced processing performance while protecting big data systems from malicious node attacks, ensuring fulfillment of processing requirements within 80% of the task completion time.
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 similar to 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.
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