With the development of Internet of Tings, the number of network devices is increasing, and the cloud data center load increases; some delay-sensitive services cannot be responded to timely, which results in a decreased quality of service (QoS). In this paper, we propose a method of resource estimation based on QoS in edge computing to solve this problem. Firstly, the resources are classifed and matched according to the weighted Euclidean distance similarity. Te penalty factor and Grey incidence matrix are introduced to correct the similarity matching function. Ten, we use regression-Markov chain prediction method to analyze the change of the load state of the candidate resources and select the suitable resource. Finally, we analyze the precision and recall of the matching method through simulation experiment, validate the efectiveness of the matching method, and prove that regression-Markov chain prediction method can improve the prediction accuracy.
1. Introduction
With the development of Internet of Tings (IoT) [1], more and more devices, especially mobile devices, constantly access the Internet. CISCO predicts that 50 billion devices will connect to the Internet by 2020 [2]. Tese devices will generate large amounts of data at the end of the network, which leads to the increment of the burden of cloud data center. Moreover, the remote distance between the mobile devices and cloud data center makes the transmission delay increase, which makes some delay-sensitive services can not get response and processing rapidly. IoT services, such as connected vehicle and video streaming, require high bandwidth and low latency content delivery to guarantee QoS. Edge computing [3] plays an important role by using network resources near the local network to provide a low latency service and improve QoS.
6. Conclusion and Future Work
With the rapid development of Internet of Tings and cloud computing, service QoS and user satisfaction become an important challenge. Local computing and storage capabilities of edge computing can reduce latency and improve user satisfaction. In this paper, we use weighted Euclidean distance similarity to classify multiple QoS attribute resources. We select the appropriate resources by similarity matching and regression-Markov chain prediction method. Since the QoS attributes system is extensible and the user QoS requirements are dynamic, the estimation method has certain scalability. On the basis of the existing work, we can design a reasonable method of resource estimation to balance the satisfaction between users and service providers and improve the utilization of resources.
