Extensive use of multimedia services and Internet Data Center applications demand distributed deployment of these applications. It is implemented using edge computing with server clusters. To increase the availability of the services, applications are deployed redundantly in server clusters. In this situation, an efficient server allocation strategy is essential to improve execution fairness in server cluster. Categorizing the incoming traffic at server cluster is desired for the improvement of QoS. The traditional traffic classification models categorize the incoming traffic according to their applications’ type. They are ineffective in selection of suitable server, as they do not consider the characteristics of the server. Hence this paper proposes a classifier to assist the dispatcher to distribute the requests to appropriate server in server cluster. The proposed deep learning classification model based on incoming traffic characteristics and server status is reinforced with extended labelling using correlation based approach. The experimental results of the proposed classifier have shown considerable performance enhancement in terms of classification measures and waiting time of the requests compared to existing machine learning models.

多媒体服务和Internet数据中心应用程序的广泛使用要求对这些应用程序进行分布式部署。它是通过边缘计算和服务器群集来实现的。为了提高服务的可用性，将应用程序冗余地部署在服务器群集中。在这种情况下，有效的服务器分配策略对于提高服务器群集中的执行公平性至关重要。需要对服务器群集处的传入流量进行分类，以提高QoS。传统流量分类模型根据传入应用的类型对传入流量进行分类。它们没有选择服务器的特性，因此在选择合适的服务器时效果不佳。因此，本文提出了一种分类器，以帮助调度程序将请求分发到服务器集群中的适当服务器。所提出的基于传入流量特征和服务器状态的深度学习分类模型通过使用基于相关方法的扩展标签得到了增强。与现有的机器学习模型相比，提出的分类器的实验结果显示出在分类方法和请求的等待时间方面的性能显着提高。