On the utility of DVFS for power-aware job placement in clusters
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Optimal resource provisioning for cloud computing environment
The Journal of Supercomputing
Resource allocation in cloud computing: model and algorithm
International Journal of Web and Grid Services
State-of-the-art research study for green cloud computing
The Journal of Supercomputing
Multi-Layer Resource Management in Cloud Computing
Journal of Network and Systems Management
Coordinating self-sizing and self-repair managers for multi-tier systems
Future Generation Computer Systems
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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.