A cost-aware auto-scaling approach using the workload prediction in service clouds

Autor:	Jingqi Yang, Lisha Niu, Junliang Chen, Bo Cheng, Yanlei Shang, Zexiang Mao, Chunhong Liu, Chuanchang Liu
Rok vydání:	2013
Předmět:	Service (business) Computer Networks and Communications Computer science Distributed computing Workload Theoretical Computer Science Service-level agreement Resource (project management) Need to know Scale (social sciences) Scalability Scaling Software Information Systems
Zdroj:	Information Systems Frontiers. 16:7-18
ISSN:	1572-9419 1387-3326
DOI:	10.1007/s10796-013-9459-0
Popis:	Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6d73b35488e6ce8f3a6eaef88a425061 https://doi.org/10.1007/s10796-013-9459-0 Zobrazit plný text záznamu Full text from SpringerLink