Modeling and predicting dynamics of heterogeneous workloads for cloud environments

Abstract

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 pro-actively anticipate in a timely manner the changes in workload intensity and characteristics. To support this decision process, we propose an integrated approach – that combines various workload characterization techniques – 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.