Abstract
Cloud computing refers to the delivering or usage of hosted services over internet rather than a traditional data center. Hosted services can be renting infrastructure/resources on demand or using the cloud as a platform to develop applications or using the cloud to host software that are accessed by clients. The bottom line is to obtain affordable resources from a provider and pay as you go in a flexible manner. In doing so, not all resources need to be obtained upfront. The initial capacity can be rented out and the remaining can be scaled as per the need. To handle such scalability, auto-scaling systems helps tackling the need to maintain the finite set of resources that can serve the current need and on the other hand also reduce the resources when the current need decreases. Very often in a cloud based environment it makes sense to adapt proactive strategies to scale the resources than to react after the surge had occurred. The proactive strategies use a quantified metric as a input to provision resources on demand that could meet the future expectations. This metric is obtained by carefully analysing the historical data of the application and in turn can influence the scaling decisions. Conclusions are drawn about the accuracy of the metric based on different timelines of historical information along with the confidence levels with which the prediction is done.
Highlights
Cloud computing has facilitated a smoother transition from a traditional data center to a virtualized pool of resource that are dispersed geographically yet aggregated as one unified resource
Auto scaling systems helps tackling the dynamic requirements during resource utilization
These systems when equipped with proactive behaviour tend to analyse historical information for a particular application in order to draw insights out of a prediction model
Summary
Cloud computing has facilitated a smoother transition from a traditional data center to a virtualized pool of resource that are dispersed geographically yet aggregated as one unified resource. The resources in use can be up scaled or downscaled based on the requirement. This is where the scaling strategies play a effective role. Proactive strategies tend to use a quantified metric to scale resources before the surge occurred where as reactive strategies tend to use the excessive required demand after the surge has occurred. The current work is more focussed on analysing the predictions from the historical data over varied timelines with different confidence level. This analysis helps making better decisions on which prediction can serve as the quantified metric for the scaling system in a cloud computing environment
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