The invention relates to the technical field of
data processing, in particular to an
edge computing method for
mass data analysis. According to the method, when massive
Web server access logs are processed, a log field structure, field complexity and data magnitude are combined, resource configuration requirements are defined,
accurate estimation of threads, memories and IO calling is achieved, a behavior sequence is further constructed on the basis of
response time delay, the
completion rate and the interruption condition in
server behavior characteristics, and the service life of the
server is prolonged. A dynamic evaluation standard for scheduling capability is formed, a
grey correlation scoring method between resource requirements and behavior capability is fused in candidate node screening, and sorting optimization is performed in combination with node real-time resource occupancy states, so that a task allocation path has better balance in the aspect of multi-dimensional
resource utilization; and
parallel processing is realized through log fragment mapping after task scheduling, and
structured analysis output is formed through edge side summarization, so that the fitness of
edge node scheduling and the precision of task assignment are improved.