Crowdsourced Resource-Sizing of Virtual Appliances

Using a population of VMware Virtual Center Virtual Ap- pliances (VCVA) and their respective workloads we de- scribe techniques for constructing a model of their resource consumption and performance, speci cally memory require- ments, and average operation-latency by mining logs of ap- plication (VCVA) performance. We use our model to provide sizing recommendations for the virtual appliance and iden- tify features that can be used to provide rough estimates of expected memory consumption. We show results of bet- ter than70% prediction accuracy (recall) for predicting Physical Memory Usage and better than80% prediction accuracy (recall) for predicting the average latency of work- load operations. We describe modeling techniques from sta- tistical machine learning that are amenable to representing complex, non-linear systems. Further, via the choice of tech- niques, we present an approach for reasoning about the lim- itations of our model, i.e., identifying when (and why) our model is expected to perform well and poorly.