A Proactive Customer-Aware Resource Allocation Approach for Data Centers

Internet application workloads typically vary over time, periods of low demand alternate with spikes which, if not properly handled, can saturate the allocated infrastructure and violate Service Level Agreements. In this work, we leverage the usage patterns associated with the different customers of an Internet application to make tailored workload predictions. These workload predictions are then used to proactively adapt the allocation of resources in a data center right before load spikes happen. Such proactive allocation strategy improves the overall resource utilization and, at the same time, guarantees the service level agreements. A real life prototype has been implemented to compare our solution with both over-provisioning and reactive approaches. Results show up to 60% reduction in response time over a reactive approach with the same adaptation frequency, and up to 84% reduction in the amount of resources allocated compared to a typical over-provisioning approach.