A Novel Reactive-Predictive Hybrid Resource Provision Method in Cloud Datacenter
Dynamic resource provisioning is an important way of ensuring performance and Service Level Agreement (SLA) guarantees for applications under changing workload. However, it is always hard to meet exactly the amount of resources required at every second. Thus, how to optimize the resource provision becomes the key problem. In this paper, we propose a Reactive-Predictive Hybrid Resource Provision Method (RPHRPM), which combines reactive and predictive methods together to benefit from both. We take advantage of ARIMA model to predict the workload and get resources pre-provisioned. Meanwhile, a reactive method is also enabled to deal with the unpredictable situations. More importantly we describe a novel mechanism which will be involved when conflicts between these two methods happen. It can help to keep better performance when encounter could burst. The experiment results show that RPHRPM not only has better performance compared with other provision schemes, but also be energy-efficient.
KeywordsProvision Reactive method Prediction model Cloud datacenter
This paper work is based on the Fudan-Hitachi Innovative Software Technology Joint Laboratory project-cloud virtualized resource management system. We would like to give our sincere thanks to Hitachi for all the support and advice. This work is also supported by 2014–2016 PuJiang Program of Shanghai under Grant No. 14PJ1431100 and 2015–2017 Shanghai Science and Technology Innovation Action Plan Project under Grant No. 15511107000.
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