Mohammad Sadegh Aslanpour - Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Sirjan, IranDepartment of Computer Engineering, Sirjan Branch, Islamic Azad University, Sirjan, Iran
Seyed Ebrahim Dashti - Corresponding AuthorDepartment of Computer Engineering, Jahrom Branch, Islamic Azad University, Jahrom, Iran

With the advent of cloud computing and the possibility of renting virtual resources on an as-needed basis, application services providers (ASPs) entered the realm of information technology in order to provide web-based applications. They rent virtual machines from cloud service providers in order to host their applications; however, it is possible that due to the dynamic nature of the workload, these resources may not be sufficient to meet the real needs of the ASP. In this point, the issue of automatic optimal resource allocation scaling is raised. With the aid of the scalability property of cloud computing, this issue can be improved in order to decrease the costs and ensure the quality of service. Researchers try to resolve this issue using reactive and proactive methods. In this study, a new framework is proposed which carries out automatic optimal resource allocation with the help of a combinatory method based on reactive and proactive scaling which is sensitive to past and present fluctuations. In this framework, the possibility of supervising resources is provided through two analyzers: the behavior of the virtual machine and the SLA behavior. The parameters affecting our two-sided decision making include virtual machine’s CPU utilization and the response time. In order to implement the proposed framework, the user requests for a website with a synthetic workload based on the real pattern of cloud environment were enumerated using CloudSim tool. The results show that compared to the one-sided methods, our proposed two-sided method is able to improve the costs of resource provision, the quality of service, scaling decision making overhead, and the resource utilization fluctuation control. This scaling mechanism can pave the way for vertical scaling as well as the utilization of Amazon’s Spot machines.

Cloud Computing, Resource Allocating, Auto- Scaling, Service Level Agreement-SLA, CloudSim