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.

Modeling and predicting dynamics of heterogeneous workloads for cloud environments The services and applications deployed nowadays in cloud environments are characterized by variable intensity and resource requirements. The variability of these workloads coupled with their heterogeneity affects the cost associated with the cloud infrastructure and the performance levels that can be satisfied. In these complex scenarios, resource provisioning policies have to take into account the actual workloads being processed and proactively anticipate in a timely manner the changes in workload intensity and characteristics. To support this decision process, we propose an integrated approach xe2x80x93 that combines various workload characterization techniques xe2x80x93 for modeling and predicting workload access patterns. The application of this approach has shown the importance of identifying models that specifically capture and reproduce the dynamics of these patterns and consider at the same time their peculiarities.