MTTF (Mean time to failure) forecasting method of SOA (Service Oriented Architecture) service

Abstract

The invention discloses an MTTF (Mean time to failure) forecasting method of an SOA (Service Oriented Architecture) service, belonging to the field of forecasting software service quality. The invention provides the MTTF forecasting method of the SOA service based on increment transfer probability analysis on the basis of analyzing tendency variation by deeply researching quantization characteristics when the SOA services are in operation. With the adoption of the MTTF forecasting method of the SOA service provided by the invention, the defects that the traditional SOA property forecasting model and method depend on an SLA (Service Level Agreement) standard and assumption in which a property parameter is constant too much can be avoided; a model support and an analysis means can be provided for credible research of the SOA service; and the forecasting accuracy and precision can be greatly improved.

Description

technical field [0001] The invention belongs to the field of software service quality prediction, and in particular relates to a method for predicting SOA service fault interval time. Background technique [0002] As a new type of distributed component model, SOA service is playing an increasingly important role in the fields of e-commerce and enterprise application integration. It is considered to be one of the core technologies in SOA (Service Oriented Architecture). one. SOA services run in a dynamically changing network environment, and their performance changes frequently. This dynamicity makes the runtime service quality of SOA services more difficult to predict, control and intervene than traditional software. One of the most important indicators of SOA service quality of service is MTTF (Mean Time To Failure, mean time between failures), which represents the mathematical expectation of the time interval between recovery from the previous failure and the occurrence o...

AI Technical Summary

Problems solved by technology

This static analysis method has great defects: due to the rapidly changing network and system environment on which SOA services are running, various parameters that affect the running quality of SOA services, such as bandwidth, packet loss rate, link failure rate, and message fields It is impossible to maintain a constant length, etc., which has a great deviation from the assumption that the relevant parameters are constant; since the parameters given in the service level agreement are upper and lower limits, the actual operating value may be higher The SLA-based forecasting method overestimates or underestimates the actual performance of SOA services

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