Considering that in the process of job scheduling, the cluster load should be prebalanced rather than remedied when the load is seriously unbalanced; therefore, in this paper, the task scheduling flow of the Hadoop cluster is analyzed deeply. On the Hadoop platform, a self-dividing algorithm is proposed for load balancing. An intelligent optimization algorithm is used to solve load balance. A dynamic feedback load balancing scheduling method is proposed from the point of view of task scheduling. In order to solve the shortcoming of the fair scheduling algorithm, this paper proposes two ways to improve the resource utilization and overall performance of Hadoop. When the mapping task is completed and the tasks to be reduced are assigned, the task assignment is based on the performance of the nodes to be reduced. It gives full play to the advantages of the ant colony algorithm and the hive colony algorithm so that the fusion algorithm can better deal with load balance. Then, three existing scheduling algorithms are introduced in detail: single queue scheduling, capacity scheduling, and fair scheduling. On this basis, an improved task scheduling strategy based on genetic algorithm is proposed to allocate and execute application tasks to reduce task completion time. The experiment verifies the effectiveness of the algorithm. The LBNP algorithm greatly improves the efficiency of reducing task execution and job execution. The delay capacity scheduling algorithm can ensure that most tasks can achieve localization scheduling, improve resource utilization, improve load balance, and speed up job completion time.
With the continuous updating and application of software, the current problems in software are becoming more and more serious. Aiming at this phenomenon, the application and testing methods of componentized software based on deep adversarial networks are discussed. The experiments show that: (1) some of the software has a high fusion rate, reaching an astonishing 95% adaptability. The instability and greater potential of component-based software are solved through GAN and gray evaluation. With the evaluation system, people are dispelled. Trust degree. (2) According to the data in the graph and table, the deep learning adversarial network solves the vulnerability and closedness of the general network, and the built-in test method with experimental data reaching an average accuracy rate of 90% is the best test method for this system. With the deep learning adversarial network, the average test level of component-based software reaches level 7, which makes the new software industry of component-based software have a long way to go.
With the continuous progress of society, computer technology and information technology are also experiencing rapid development. Especially in recent years, the application of computer technology has rapidly entered into people's daily life. As people’s lives become richer, these applications have become particularly complex. For some large software, tens of thousands of function points or millions of lines of source code may be triggered to support it when performing related tasks. As a result, the security of such a complicated and excellent software becomes quite essential. The most effective way to ensure software security is to test the security of software products during the development process. A precise and effective security testing process is the basis for ensuring that software is tested for security. Without a detailed scientific software security testing model to guide software development for security testing, software security testing will become very difficult. This not only wastes more time and money but also does not guarantee the security of the software. A great security testing methodology should be able to find security problems that may be hidden deep within the software. In addition, a scientific process management can greatly facilitate the implementation of software security testing. As a result, it is relatively meaningful to establish a complete software security testing process model, generate excellent security test cases, and develop security process management tools for software security testing. At the same time, in recent years, deep learning has gradually entered more and more people's lives. However, the widespread application of deep learning systems can bring convenience to human life but also bring some hidden dangers. Hence, deep neural networks must be adequately tested to eliminate as many security risks as possible in some safety-critical software that involves personal and property safety. As the foundation of deep learning systems, deep neural networks should be adequately tested for security. However, deep learning systems are fundamentally different from traditional software testing, so traditional software testing techniques cannot be directly applied to deep neural network testing. In recent years, many scholars in related fields have proposed coverage guidelines based on deep learning testing, but the usefulness of these guidelines is still debatable. Based on the complexity of the large software development process and the fact that the interrelationship between nodes often constitutes a complex network of collaborative relationships, this study applies coverage-based testing in deep neural networks to test the security of software. To be specific, this research applies metrics such as peak coverage, speed to peak, and computational speed to evaluate coverage criteria and to investigate the feasibility of using coverage to guide test case selection to select solutions for security testing.
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