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, specifically 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 than ∼70 % prediction accuracy (recall) for predicting Physical Memory Usage and better than ∼80 % 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.