Optimization of virtual resource management for cloud applications to cope with traffic burst

• We present a framework of dynamic resource management to cope with traffic burst.
• The prediction of traffic burst is based on Gompertz Curve and Moving Average model.
• VM scheduler involves VM Provisioning, VM Placement and VM Recycling.
• High availability and cost-effectiveness are achieved by the proposed framework.

Being the latest computing paradigm, cloud computing has proliferated as many IT giants started to deliver resources as services. Thus application providers are free from the burden of the low-level implementation and system administration. Meanwhile, the fact that we are in an era of information explosion brings certain challenges. Some websites may encounter a sharp rising workload due to some unexpected social concerns, which make these websites unavailable or even fail to provide services in time. Currently, a post-action method based on human experience and system alarm is widely used to handle this scenario in industry, which has shortcomings like reaction delay. In our paper, we want to solve this problem by deploying such websites on cloud, and use features of the cloud to tackle it. We present a framework of dynamic virtual resource management in clouds, to cope with traffic burst that applications might encounter. The framework implements a whole work-flow from prediction of the sharp rising workload to a customized resource management module which guarantees the high availability of web applications and cost-effectiveness of the cloud service providers. Our experiments show the accuracy of our workload forecasting method by comparing it with other methods. The 1998 World Cup workload dataset used in our experiment reveals the applicability of our model in the specific scenarios of traffic burst. Also, a simulation-based experiment is designed to indicate that the proposed management framework detects changes in workload intensity that occur over time and allocates multiple virtualized IT resources accordingly to achieve high availability and cost-effective targets.