S-SWAP: scale-space based workload analysis and prediction

This work presents a scale-space based approach to assist dynamic resource provisioning. The application of this theory makes it possible to eliminate the presence of irrelevant information from a signal that can potentially induce wrong or late decision making. Dynamic provisioning involves increasing or decreasing the amount of resources allocated to an application in response to workload changes. While monitoring both resource consumption and application-specic metrics is fundamental in this process since the latter is of great importance to infer information about the former, dealing with these pieces of information to provision resources in dynamic environments poses a big challenge. The presence of unwanted characteristics, or noise, in a signal that represents the monitored metrics favors misleading interpretations and is known to aect forecast models. Even though some forecast models are robust to noise, reducing its inuence may decrease training time and increase eciency. Because a dynamic environment demands decision making and predictions on a quickly changing landscape, approximations are necessary. Thus it is important to realize how approximations give rise to limitations in the forecasting process. On the other hand, being aware of when detail is needed, and when it is not, is crucial to perform ecient dynamic forecastings. In a cloud environment, resource provisioning plays a key role for ensuring that providers adequately accomplish their obligation to customers while maximizing the utilization of the underlying infrastructure. Experiments are shown considering simulation of both reactive and proactive strategies scenarios with a real-world trace that corresponds to access rate. Results show that embodying scale-space theory in the decision making stage of dynamic provisioning strategies is very promising. It both improves workload analysis, making it more meaningful to our purposes, and lead to better predictions