基于灰色预测模型的优先级队列缓存管理策略

Buffer Management Strategy of Priority Queue Based on Gray Prediction Model

为解决企业服务总线(ESB)集成平台中的服务队列管理问题,在考虑队列优先级因素的基础上,提出一种优先级消息服务队列缓存管理策略。将不同优先级的业务数据封装为消息服务放入不同队列中,按照消息优先级顺序对其进行服务管理,在下一次业务消息到达前,使用灰色预测模型实时预测优先级队列的缓存分配情况,使得队列缓存分配更合理。实验结果表明,该策略能保障ESB集成平台中高优先级业务和低优先级业务的正常运行,并降低高优先级业务的平均等待时间、平均停留时间及消息队列拥塞的风险。

To solve the service queue management problem in the Enterprise Service Bus（ESB） integration platform, the buffer management strategy of priority message service queue is proposed. This strategy puts the different priorities business data into different queues. The Business is serviced according to the order of priority packets. Before the next packets arrive, the strategy uses gray prediction to make a real-time prediction about the priority queue＇ s buffer size which can be assigned, makes the queue＇ s buffer allocation more reasonable. Experimental results show that the proposed strategy not only can guarantee high priority and low priority traffic to run smoothly in ESB integration platform,but also can reduce the average waiting time, the average residence time for high priority traffic and the risk of message queue congestion.