Daniel Gmach

TL;DR: This work presents a novel approach to model the energy flows in a data center and optimize its operation that can reduce both the recurring power costs and the use of non-renewable energy by as much as 60% compared to existing techniques, while still meeting the Service Level Agreements.

TL;DR: A trace based approach for capacity management that relies on the characterization of workload demand patterns, the generation of synthetic workloads that predict future demands based on the patterns, and a workload placement recommendation service to automate the efficient use of resource pools when hosting large numbers of enterprise services.

TL;DR: The results show that the most tightly integrated controller approach offers the best results in terms of capacity and quality but requires more migrations per hour than the other strategies.

TL;DR: An automated capacity and workload management system that integrates multiple resource controllers at three different scopes and time scales is described and results confirm that such an integrated solution ensures efficient and effective use of data center resources while reducing service level violations for high priority applications.

TL;DR: The integrated controller approach outperforms the use of either controller separately for the enterprise application workloads in this study and shows the influence of the blade and server pool infrastructures on the effectiveness of the management policies.