EZLogic

Fusionskye's intelligent log paradigm solution calculates the similarity between log texts through an AI algorithm model, aggregates logs with high similarity into a category, and extracts definitions of their common characteristics. First, the log features and vectors are extracted based on natural language processing, and then the log is clustered using the similarity of the log text to form a log parsing format template, called a log paradigm model for short. Through log clustering, we can divide massive log data into several categories or dozens of categories through AI algorithms. Operation and maintenance personnel can also adjust the accuracy of clustering according to the actual situation of the business, control the number of clustered categories, speed up the review of the log paradigm output results, release the heavy work of operators and maintenance personnel in formalized language writing and optimization, and greatly improve the efficiency of centralized log management and analysis. We also proposed the goal of “data sharing, analytical autonomy”. “Tenant” is a concept borrowed from SAAS. Different tenants' parsing of logs is isolated and does not affect each other. Supporting multiple tenants is an essential feature of a log parsing system, because logs are an important basic data, and users have different internal departments. According to their respective application scenarios, their log parsing requirements vary greatly, and this requirement may change constantly, making it difficult to rely on a centralized management department (such as a data center department) to quickly respond to this diverse demand. As a result, the autonomy of various departments in log parsing has become a strong requirement. Compared to the multi-tenant function of ordinary SAAS services for the purpose of data autonomy, this kind of multi-tenant system does not simply copy and distribute the original log according to the application scenario. Otherwise, log parsing will repeat the workload, and after the number of tenants becomes more, it will put a large additional load on the system and affect the scalability of the system. Then, we can input a type of log into a custom parameterized standard output according to multiple tenants (scenarios), so as to achieve effective parameterized content output for 1 TON tenant (scenario) logs to meet the needs of more users and operation and maintenance scenarios.