Distributed buffer memory strategy adaptive switching method based on machine learning and system thereof

Abstract

The invention relates to a distributed buffer memory strategy adaptive switching method based on machine learning and a system thereof. The method comprise the following steps: carrying out evaluation on each buffer memory strategy based on a reference test, and confirming scene elements influencing buffer memory strategy performance appearance; collecting data set of each buffer memory strategy under different scene elements conditions; training the data set to obtain a buffer memory performance model; and deciding an optimal buffer memory strategy based on buffer memory performance model and on-line monitoring data in cluster environment, and executing buffer memory strategy switching when present buffer memory strategy is inconsistent with the optimal strategy. In the invention, machine learning method is employed to establish the buffer memory performance model which can accurately describes present scene, the model is updated periodically through constructing a performance data warehouse to improve the precision of the model further. Thus the distributed buffer memory strategy adaptive switching method in the invention is adapted to environmental dynamic change well to enhance flexibility and adaptability of buffer memory service.

Description

Technical field [0001] The invention belongs to the field of software technology, and relates to a distributed caching strategy self-adaptive switching method and a system thereof, in particular to a method and a system for constructing a caching performance model through machine learning and then deciding an optimal caching strategy. Background technique [0002] In the cloud computing environment, in order to better cope with the challenges brought by massive data and user requests, and solve the bottleneck problem of large-scale data access faced by traditional databases, distributed caching technology has been introduced to provide users with high performance, high availability, and scalability. data caching service. Distributed cache shortens the distance between clustered object data and applications, and is an important means for cloud platforms to improve application performance. Searchsoa believes that for data-intensive Web applications, if the support of the key ...

AI Technical Summary

Problems solved by technology

There are two shortcomings of this strategy: first, the memory usage is high

Second, when the frequency of data update operations is performed, the communication overhead is large

The advantage of this method is that it is simple to implement, and the execution efficiency of the strategy selection process is high; the disadvantage is that the use of fixed rules to select the optimal caching strategy lacks sufficient flexibility and adaptability, and it is difficult to adapt to the dynamic changes of the environment and requirements. Most of the rules are formulated manually. Complete, easy to introduce the influence of subjective factors

Images