Load balancing network middleware implementation method based on extensible protocol

Abstract

The invention discloses a load balancing network middleware implementation method based on an extensible protocol. The load balancing network middleware implementation method based on the extensible protocol comprises the following steps: step 1, a server side receives a multi-frame message of a client; step 2, a message type is obtained after parsing the multi-frame message into single-frame messages; step 3, reading messages are put into the reading thread pool and writing messages are put into the writing thread pool, wherein small size messages use the on-stack allocation mode, and large size messages use the on-heap allocation mode; step 4, the reading thread pool and the writing thread pool call a load balancing algorithm separately to select specific processing threads, and put themessage content into the corresponding thread message queue; and step 5, for writing messages, the writing thread writes data to disk files and returns confirmation messages; and for reading messages,the reading thread queries the corresponding disk files according to the index, reads the data into the cache, and returns assembled messages to the corresponding client. The load balancing network middleware implementation method based on the extensible protocol has the advantages of high performance, scalable protocol and load balancing, and can effectively save network traffic and improve response speed.

Description

technical field [0001] The invention relates to a method for implementing a load balancing network middleware based on an extensible protocol. Background technique [0002] Time-series data processing technology is widely used in wide area measurement system (WAMS), substation monitoring, dispatching, DC, stability control and other power automation systems to meet the needs of storing massive and high-frequency data. Time-series databases are mainly used to process high-frequency data at the second and millisecond levels. Each storage cycle (milliseconds or seconds) will generate a batch of data, so the amount of data is huge, and the read and write performance of network traffic and network middleware is extremely demanding. High, this requirement is different from traditional network middleware. It is necessary to customize and develop network middleware suitable for time-series databases, and optimize it for message, network and storage characteristics. [0003] Traditi...

AI Technical Summary

Problems solved by technology

First of all, these frameworks are general-purpose frameworks, and the key-value pair characteristics for time-series data cannot be optimized, which makes the communication protocol too large and wastes resources; second, the general framework needs to consider too many scenarios, so the middleware is too complicated, such as CORBA. A heavyweight framework will drag down the deployment of the entire time-series database; again, traditional middleware shields the underlying operating system dependencies very well, achieving versatility and cross-platform, but this is based on the loss of certain system performance, so Unable to meet the requirements of large data volume and low response delay of time series data

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