Abstract
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 function (TSAF) 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.
Highlights
C ONSIDERING the significant increase of internet of things (IoT) devices, drones, autonomous driving vehicles, and various interactive video services and systems, such as eXtended Reality (XR), the amount of data that needs to be processed in real-time is rapidly increasing
When the ratio of malicious nodes performing security attacks on worker nodes reaches 0.5, Fig. 5 shows that the trusted streaming adaptive function (TSAF) scheme can still satisfy over 75% of the tasks within the target object time, which is significantly higher compared to the other big data processing schemes
Network security procedures commonly slow down the response time of big data systems
Summary
C ONSIDERING the significant increase of internet of things (IoT) devices, drones, autonomous driving vehicles, and various interactive video services and systems, such as eXtended Reality (XR), the amount of data that needs to be processed in real-time is rapidly increasing. In [1]–[4], the security of big data systems are maintained by calculating the trust for data collected from the IoT sensors, unmanned aerial vehicles (UAVs), autonomous driving vehicles, and IIoT network. In [5]–[7], algorithms to manage user or data trust levels using blockchains and authentication were proposed In these studies, trust of the worker nodes for big data processing is not considered. A trusted streaming adaptive function (TSAF) big data scheme is proposed that uses trust management to identify malicious nodes in Spark big data systems, exclude them from job/task processing assignments, and calculate the number of nodes that can satisfy the processing object time
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