Transfer Learning for CrossModel Regression in Performance Modeling for the Cloud Performance characteristics of a complex system in different configurations are expensive to obtain due to the cost of sampling the system performance. We introduce ModelMap, a novel transfer learning technique for the performance modeling of configurable systems. ModelMap captures many explicit and latent types of dynamic system evolution, including configuration changes, scaling and hardware upgrades, by deriving and modeling directly these kinds of incremental transformations between system andor application instances, over time. Modeling these transformations allows us to build accurate models for new configuration instances with just a few samples, and to interpolate across legacy models to build new models with no new samples at all. We experimentally test our method on a variety of system performance modeling and optimization scenarios. Compared to using conventional direct and incremental modeling techniques, our method achieves higher accuracy by up to an order of magnitude when the sampling budget is extremely limited, in particular between 0% to 5% of an exhaustive sampling budget. We also show how our method can be used to quickly derive an accurate resource allocation split that optimizes a given overall performance goal for cohosted applications in a virtualized environment.

SD: A DivergenceBased Estimation Method for Service Demands in Cloud Systems Estimating performance models parameters of cloud systems presents several challenges due to the distributed nature of the applications, the chains of interactions of requests with architectural nodes, and the parallelism and coordination mechanisms implemented within these systems. In this work, we present a new inference algorithm for model parameters, called state divergence (SD) algorithm, to accurately estimate resource demands in a complex cloud application. Differently from existing approaches, SD attempts to minimize the divergence between observed and modeled marginal state probabilities for individual nodes within an application, therefore requiring the availability of probabilistic measures from both the system and the underpinning model. Validation against a case study using the Apache Cassandra NoSQL database and random experiments show that SD can accurately predict demands and improve system behavior modeling and prediction.