SLA-aware resource allocation for application service providers in the cloud

Abstract

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.

LARPA: A learning automata‐based resource provisioning approach for massively multiplayer online games in cloud environments

“…Hence, the players are categorized into three distinct clusters, each of which has its own SLA limitations, and so each player is a member of a cluster and proportionally inherits attributes of its cluster. Finally, the executor component of LARPA utilizes Last Up First Down (LUFD) strategy when selecting the redundant VM as a consequence of performing scale‐down decisions …”

“…Note that we define it as the time consumed to process a request in all tiers, from the web to cellular and database tiers. For more information, refer to Ashlanpour and Dashti where the authors discuss desire response time of applications hosted in the cloud environment. Cost: The cost of renting VMs and SLA violations constitute the final cost which is calculated by Equation . …”

Triangulation Resource Provisioning for Web Applications in Cloud Computing: A Profit-Aware Approach

“…The planning phase designed with learning automata technique for resource utilization forecasting. Aslanpour and Dashti [6] proposed the hybrid technique with the combination of reactive and proactive auto-scaling technique. The planning phase has a two-sided policy having one side with SLA violation and the second side is resource utilization.…”

“…The execution phase in Amazon EC2 randomly selects the VM in the scale-down decision. The policy in state-of-the-art are designed and implement such as Last Up First Down (LUFD) [6], First Up First Down (FUFD) [7], cost-aware and load-aware [9,8] policies. The super professional executor has been used in the experiment [9].…”