Elastic edge cloud resource management based on horizontal and vertical scaling
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Elastic edge cloud resource management based on horizontal and vertical scaling Chunlin Li1,2,3 · Jianhang Tang1 · Youlong Luo1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The resources in the edge cloud are numerous and complex, and elastic scaling services can make efficient use of these resources. However, the elastic scaling services need to suspend the user’s application tasks forcibly when carrying out resource redistribution, which brings a poor sense of experience to the user. In view of the above problems, a dynamic elastic scaling model based on load prediction is proposed, which improves resource utilization and reduces scaling costs without affecting user experience. The model is divided into two parts. In terms of load prediction, on the one hand, according to the historical features and current trends of the load, the load prediction model based on the improved cloud model is used to predict the load demand at the next moment. On the other hand, the correlation between CPU and memory is considered. In terms of elastic scaling, integer programming algorithm is proposed to expand and release the corresponding resources with the minimum cost of horizontal scaling (HS) and vertical scaling (VS). In order to verify the superiority of elastic scaling model based on load prediction, corresponding comparative experiments are conducted, which show that the proposed model can improve the accuracy of load prediction and resource utilization with low scaling costs. Especially, the cost of elastic scaling proposed by this paper is lower than horizontal and vertical scaling. Compared with HS, the elastic scaling method proposed in this paper reduces the cost by 14%. Compared with VS, this method reduces the cost by 11%. Keywords Edge cloud · Load prediction · Elastic scaling · Cloud model · Integer programming algorithm * Chunlin Li [email protected] 1
Department of Computer Science, Wuhan University of Technology, Wuhan 430063, People’s Republic of China
2
Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, People’s Republic of China
3
Anhui Province Key Laboratory of Big Data Analysis and Application, Hefei, People’s Republic of China
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C. Li et al.
1 Introduction The cloud platform has the characteristics of large scale, high reliability and dynamic scaling [1]. It is complex and easy to cause resource waste and scheduling difficulties. Virtualization software such as Kernel-based Virtual Machine (KVM) can help cloud computing allocate resources flexibly [2]. However, in the application, there is still a big difference between the actual demand of users for resources and resources provided by cloud service providers. So, there is still a deficiency in the research about elastic scaling. On the one hand, the demand for resources increases rapidly, but the resources need a certain period of time to be allocated, which resulting in the insufficient allocation of resources in a certai
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