Fuzzing is an effective technology in software testing and security vulnerability detection. Unfortunately, fuzzing is an extremely compute-intensive job, which may cause thousands of computing hours to find a bug. Current novel works generally improve fuzzing efficiency by developing delicate algorithms. In this paper, we propose another direction of improvement in this field, i.e., leveraging parallel computing to improve fuzzing efficiency. In this way, we develop P-fuzz, a parallel fuzzing framework that can utilize massive, distributed computing resources to fuzz. P-fuzz uses a database to share the fuzzing status such as seeds, the coverage information, etc. All fuzzing nodes get tasks from the database and update their fuzzing status to the database. Also, P-fuzz handles some data races and exceptions in parallel fuzzing. We compare P-fuzz with AFL and a parallel fuzzing framework Roving in our experiment. The result shows that P-fuzz can easily speed up AFL about 2.59× and Roving about 1.66× on average by using 4 nodes.

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During the operation of the substation, the main reason for the heating of the switchgear is the eddy current loss. In the daily operation and maintenance of the substation and loss statistics, for some switchgears in the area of direct conductive equipment, there is an abnormal local overheating phenomenon. The main reason for this phenomenon is the eddy current loss in the substation, which seriously affects the safe operation of substation equipment due to the heating defect. This paper analyzes the local overheating problem in substation switchgear and proposes the distribution of eddy current loss and its influencing factors of optimization strategy from the aspect of loss source design. Based on the Maxwell equation, the loss calculation model is established. The optimization method of eddy current loss is comprehensively evaluated according to the distribution characteristics of eddy current loss. The temperature of the bushing surface under different voltage levels is measured, and the error between the calculated results and the measured results is within 2%. Therefore, within the allowable error range, the simulation results and the formula calculation results can fit the measured values well, which verifies the correctness of the established wall bushing heating simulation model and temperature calculation formula. The optimal solution is made to provide a reliable basis for the actual engineering construction. The electric field energy of the bushing is relatively evenly distributed under the actual operating conditions, and the insulating materials are fully utilized to ensure the reliability under high ambient temperature and high current operating conditions.

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