Energy-efficient application-aware online provisioning for virtualized clouds and data centers

• Published in: International Conference on Green Computing pp. 31 - 45
• Main Authors: Rodero, I, Jaramillo, J, Quiroz, A, Parashar, M, Guim, F, Poole, S
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2010
• Subjects: Algorithm design and analysis; Autonomic Computing; Cloud Computing; Clouds; Clustering algorithms; Data Center; Efficiency; Energy; Quality of service; Resource management; Resource Provisioning; Runtime; Servers; Virtualization
• ISBN: 1424476127; 9781424476121
• DOI: 10.1109/GREENCOMP.2010.5598283

As energy efficiency and associated costs become key concerns, consolidated and virtualized data centers and clouds are attractive computing platforms for data- and compute- intensive applications. These platforms provide an abstraction of nearly-unlimited computing resources through the elastic use of pools of consolidated resources, and provide opportunities for higher utilization and energy savings. Recently, these platforms are also being considered for more traditional high-performance computing (HPC) applications that have typically targeted Grids and similar conventional HPC platforms. However, maximizing energy efficiency, cost-effectiveness, and utilization for these applications while ensuring performance and other Quality of Service (QoS) guarantees, requires leveraging important and extremely challenging tradeoffs. These include, for example, the tradeoff between the need to efficiently create and provision Virtual Machines (VMs) on data center resources and the need to accommodate the heterogeneous resource demands and runtimes of these applications. In this paper we present an energy-aware online provisioning approach for HPC applications on consolidated and virtualized computing platforms. Energy efficiency is achieved using a workload-aware, just-right dynamic provisioning mechanism and the ability to power down subsystems of a host system that are not required by the VMs mapped to it. We evaluate the presented approach using real HPC workload traces from widely distributed production systems. The results presented demonstrated that compared to typical reactive or predefined provisioning, our approach achieves significant improvements in energy efficiency with an acceptable QoS penalty.