A performance enforcing mechanism for energy- and failure-aware cloud systems

Scheduling of multiple virtual machines onto fewer servers improves resource utilization and can reduce operational costs due to power consumption. However, virtualization technologies do not offer performance isolation, causing applications´ slowdown. In this work, we propose a performance enforcing mechanism, composed of a slowdown estimator, and a scheduling algorithm. The slowdown estimator determines, based in noisy slowdown data samples obtained from state-of-the-art slowdown meters, if tasks will complete within their deadlines, invoking the scheduling algorithm if needed. When invoked, the scheduling algorithm builds performance-, power-, and failure-aware virtual clusters to successfully execute the tasks. We conduct simulations injecting synthetic jobs which characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our strategy can be efficiently integrated with state-of-the-art slowdown meters to fulfil contracted SLAs in real-world environments, while reducing operational costs in about 21%.