Abstract: A method of dynamically loading an encryption engine generates a relationship between encryption identifiers and information parameters. The information parameters includes information security levels, information sizes, and information access speeds. The encryption identifiers include a soft encryption identifier and a hard encryption identifier. A target encryption identifier of current to-be-encrypted information is obtained, and a target encryption mode of the current to-be-encrypted information is determined. An encryption engine corresponding to the current to-be-encrypted information is loaded according to the encryption mode. The method can reduce waste of resources, and improve an efficiency of encryption and decryption of information.

Abstract: Disclosed are a method and device for detecting an abnormal load. The method includes: collecting data of each load item in all hosts; pre-processing to obtain a second sequence corresponding to each load item; obtaining a coefficient matrix and a detail vector; computing a weighted mean of abnormal load probabilities of all the coefficient vectors, to obtain an abnormal load probability of each second sequence; comparing the abnormal load probability with a confidence interval, and judging whether there is an abnormal load, and judging that there is no abnormity in the corresponding second sequence if the abnormal load probability of each second sequence is in the confidence interval; judging whether there is an abnormal load according to data of all load items and the abnormal load probability of each second sequence; and finding a bearing host that has the abnormal load from the current online service.

Abstract: Disclosed are a method and device for detecting an abnormal load. The method includes: collecting data of each load item in all hosts; pre-processing to obtain a second sequence corresponding to each load item; obtaining a coefficient matrix and a detail vector; computing a weighted mean of abnormal load probabilities of all the coefficient vectors, to obtain an abnormal load probability of each second sequence; comparing the abnormal load probability with a confidence interval, and judging whether there is an abnormal load, and judging that there is no abnormity in the corresponding second sequence if the abnormal load probability of each second sequence is in the confidence interval; judging whether there is an abnormal load according to data of all load items and the abnormal load probability of each second sequence; and finding a bearing host that has the abnormal load from the current online service.