On the Anticipation of Resource Demands to Fulfill the QoS of SaaS Web Applications

Companies are currently turning to the use of web applications offered as Cloud services, expecting a certain QoS expressed by means of a maximum response time. Virtual Machines hosting these applications may vary their resource allotment as a consequence of a variation in the incoming workload intensity to guarantee the agreed response time. This allotment should be enough to avoid an under-provision that would lead to the violation of response time constraints, and low enough to avoid an over-provision that would lead to resource wasting. To anticipate the resource demands of web applications, we propose a Prediction System that combines statistical and Machine Learning techniques. This system is composed by the Immediate Predictor to anticipate the immediate CPU demand, useful to adapt pro-actively the resource allotments, and by the Capacity Predictor to forecast the CPU demand at a more distant future. The last prediction might be used to make an informed admission control by means of rejecting new applications that will not be able to fulfill their SLAs. Experiments show the accuracy achieved by the Prediction System and discuss its potential benefit to enhance the resource management process in a Cloud provider.