RConf(PD): Automated resource configuration of complex services in the cloud

Our system consists of two key technologies (RConf and RConfPD), both of which build on an analytical model based on robust queueing theory to accurately model arbitrary components. With the help of this model, RConf proposes an algorithm to ultimately find the optimal combination of component instances. Our real-world experiments show that, compared to greedy approaches, RConf provisions 20% less resources in the first place, and can reduce resource wastage on live resources by up to 50%. At the same time, RConfPD trades-off some of the optimality of RConf for a computational expense 1-2 orders of magnitude below that of RConf to provision time-sensitive services. Based on a primal-dual algorithm framework RConfPD relaxes the optimality constraints of RConf and removes dominated combinations to determine an approximation for the optimal solution. Our evaluation shows that RConfPD allows for fast decisions (in many cases <1ms), while maintaining 80%-99% of the solution quality of RConf.