Abstract
This article first established a university network education system model based on physical failure repair behavior at the big data infrastructure layer and then examined in depth the complex common causes of multiple data failures in the big data environment caused by a single physical machine failure, all based on the principle of mobile edge computing. At the application service layer, a performance model based on queuing theory is first established, with the amount of available resources as a conditional parameter. The model examines important events in mobile edge computing, such as queue overflow and timeout failure. The impact of failure repair behavior on the random change of system dynamic energy consumption is thoroughly investigated, and a system energy consumption model is developed as a result. The network education system in colleges and universities includes a user login module, teaching resource management module, student and teacher management module, online teaching management module, student achievement management module, student homework management module, system data management module, and other business functions. Later, the theory of mobile edge computing proposed a set of comprehensive evaluation indicators that characterize the relevance, such as expected performance and expected energy consumption. Based on these evaluation indicators, a new indicator was proposed to quantify the complex constraint relationship. Finally, a functional use case test was conducted, focusing on testing the query function of online education information; a performance test was conducted in the software operating environment, following the development of the test scenario, and the server’s CPU utilization rate was tested while the software was running. The results show that the designed network education platform is relatively stable and can withstand user access pressure. The performance ratio indicator can effectively assist the cloud computing system in selecting a more appropriate option for the migrated traditional service system.
Highlights
With the rapid expansion of the Internet, a new generation of information technology represented by cloud computing and big data processing technology continues to achieve the integration and sharing of various resources, thereby building a new large-scale complex IT system (LSCITS) [1]
Many emerging service models have been derived on the basis that cloud computing facilitates big data technology to shield the underlying heterogeneity, such as Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS), making Service-Oriented Computing widely used [10,11,12]
We present two benchmark schemes, a random edge caching approach and a noncooperative edge caching strategy, to validate the performance of the network education caching strategy based on mobile edge computing described in this research
Summary
With the rapid expansion of the Internet, a new generation of information technology represented by cloud computing and big data processing technology continues to achieve the integration and sharing of various resources, thereby building a new large-scale complex IT system (LSCITS) [1]. In comparison to traditional IT systems, it needs to effectively manage large-scale, heterogeneous, and complex infrastructure resources, but it needs to meet diversified application requirements, application requirements for reliable computing, high-performance computing, energy savings, and emission reduction [2,3,4]. System index evaluation based on theoretical models is critical for achieving reliable, efficient, and energy-saving optimized scheduling management for university network education under large-scale complex systems. The Bionic Autonomic Nervous Systems are used in the design of the dispatch management system, and the optimized dispatch management technology with comprehensive consideration of reliability, performance, and energy consumption is further studied based on the established correlation model
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