Intelligent front-end drainage system realized by combining container technology

Abstract

The invention discloses an intelligent front-end drainage system realized by combining a container technology. The intelligent front-end drainage system comprises a load balancing module, a strategy management module, a data analysis module, a container management module and a service execution environment, the load balancing strategy is dynamically variable, the back-end service node can pull updifferent numbers of service processing programs based on container operation according to front-end system drainage, and better load balancing and service processing resource configuration are achieved through dynamic cooperation of the front end and the back end. And the data analysis module has a machine learning capability, performs algorithm calculation on the working condition data, realizestrend prediction and corrects an execution strategy in advance.

Description

technical field [0001] The invention belongs to the technical field of server load balancing processing, in particular to an intelligent front-end drainage system realized by combining container technology. Background technique [0002] In the field of industrial Internet, with the rapid growth of access devices, the amount of concurrent processing often increases geometrically. The original load balancing processing method often relies on simple processing in a distributed manner. On the contrary, there is no good balance between bandwidth and processing capacity, especially in some application scenarios, some service nodes may still be running other business programs, and a part of system resources must be divided for use by other programs. In this way, if the traditional load balancing mode is adopted, the CPU of one of the service nodes may be in a high-load state for a long time, resulting in obvious processing delays, which in turn triggers the bucket effect and lowers...

AI Technical Summary

Problems solved by technology

The polling method is to assign requests to service nodes in turn, which cannot guarantee the rationality of the assigned tasks, and cannot allocate tasks according to the capacity of the service nodes; the random method is to randomly select a service node to assign tasks, ensuring the dispersion of requests to achieve The purpose of balancing, but it can not achieve enough decentralization. As the amount of tasks increases, the effect tends to poll after some shortcomings of the polling algorithm; the minimum connection method is to assign tasks to the task with the minimum number of connections at this time The node is a dynamic load balancing algorithm, which is suitable for when the processing performance of each node is similar, but when the server performance gap is large, the expected effect cannot be achieved, and the service node with a small number of connections and poor availability may It is not as good as a server with a large number of connections and good availability, and cannot be accurately allocated to nodes with strong remaining processing capabilities

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