Dynamic scalability and contention prediction in public infrastructure using Internet application profiling

Abstract

Recently, the advance of cloud computing services has attracted many customers to host their Internet applications in the cloud. Infrastructure as a Service (IaaS) is on top of these services where it gives more control over the provisioned resources. The control is based on online monitoring of specific metrics (e.g., CPU, Memory, and Network). Despite the fact that these metrics guide resources provisioning, the lack of understanding application behavior can lead to wrong decisions. Moreover, current monitored metrics alone do not help in resources contention prediction, which is very common in shared infrastructures like IaaS. Nevertheless, the architecture of Internet applications, as multi-tier systems, makes contention prediction more complex while its influence can migrate from one tier to another. In this paper, we propose a pro-active global controller not only for dynamic resources provisioning, but also for predicting and eliminating contentions in multi-tier applications. Our technique combines monitored metrics, which are provided by current IaaS providers, with models that are built depending on the Internet applications profiling. The fitness of the monitored metrics to the application model is used for contention prediction. We examined our technique using RUBiS benchmark. The results express the efficiency of the developed algorithms in maintaining Internet applications performance even in shared infrastructures.