@article {1926,
	title = {Energy-efficient Thermal-aware Autonomic Management of Virtualized HPC Cloud Infrastructure},
	journal = {Springer Journal of Grid Computing, Special Issue on High Performance Grid and Cloud Computing},
	volume = {10},
	year = {2012},
	month = {September},
	pages = {447-473},
	issn = {1570-7873},
	author = {I. Rodero and H. Viswanathan and E. K. Lee and M. Gamell and D Pompili and Manish Parashar}
}
@article {2077,
	title = {Energy-efficient Thermal-aware Autonomic Management of Virtualized HPC Cloud Infrastructure},
	journal = {Journal of Grid Computing (Springer)},
	volume = {10},
	year = {2012},
	month = {Sept},
	pages = {447-473},
	chapter = {447},
	abstract = {Virtualized datacenters and clouds are
being increasingly considered for traditional
High-Performance Computing (HPC) workloads
that have typically targeted Grids and conventional HPC platforms. However, maximizing
energy efficiency and utilization of datacenter resources, and minimizing undesired thermal behavior while ensuring application performance and
other Quality of Service (QoS) guarantees for
HPC applications requires careful consideration
of important and extremely challenging tradeoffs.
Virtual Machine (VM) migration is one of the most common techniques used to alleviate thermal anomalies (i.e., hotspots) in cloud datacenter servers as it reduces load and, hence, the
server utilization. In this article, the benefits of
using other techniques such as voltage scaling and
pinning (traditionally used for reducing energy
consumption) for thermal management over VM
migrations are studied in detail. As no single
technique is the most efficient to meet temperature/performance optimization goals in all situations, an autonomic approach that performs
energy-efficient thermal management while ensuring the QoS delivered to the users is proposed.
To address the problem of VM allocation
that arises during VM migrations, an innovative application-centric energy-aware strategy for
VM allocation is proposed. The proposed strategy ensures high resource utilization and energy efficiency through VM consolidation while
satisfying application QoS by exploiting knowledge obtained through application profiling along
multiple dimensions (CPU, memory, and network bandwidth utilization). To support our
arguments, we present the results obtained
from an experimental evaluation on real hardware using},
	author = {I. Rodero and H. Viswanathan and E. K. Lee and M. Gamell and D Pompili}
}
@conference {2005,
	title = {Exploring Cross-layer Power Management for PGAS Applications on the SCC Platform },
	booktitle = {Proceedings of the 21st International ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC"12)},
	year = {2012},
	month = {06/2012},
	address = {Delft, the Netherlands },
	author = {M. Gamell and I. Rodero and Manish Parashar and R. Muralidhar}
}
@conference {167,
	title = {Energy-Aware Application-Centric VM Allocation for HPC Workloads},
	booktitle = {Proceedings of the Workshop on High-Performance Grid and Cloud Computing, a workshop at the 25th IEEE International Parallel \& Distributed Processing Symposium (IPDPS 2011)},
	year = {2011},
	month = {05/2011},
	address = {Anchorage, Alaska, USA},
	abstract = {Virtualized datacenters and clouds are being increasingly
considered for traditional High-Performance Computing
(HPC) workloads that have typically targeted Grids and
conventional HPC platforms. However, maximizing energy efficiency,
cost-effectiveness, and utilization of datacenter resources
while ensuring performance and other Quality of Service (QoS)
guarantees for HPC applications requires careful consideration
of important and extremely challenging tradeoffs.
An innovative application-centric energy-aware strategy for
Virtual Machine (VM) allocation is presented. The proposed
strategy ensures high resource utilization and energy efficiency
through VM consolidation while satisfying application QoS.
While existing VM allocation solutions are aimed at satisfying
only the resource utilization requirements of applications along
only one dimension (CPU utilization), the proposed approach
is more generic as it employs knowledge obtained through
application profiling along multiple dimensions. The results of
our evaluation show that the proposed VM allocation strategy
enables significant reduction either in energy consumption or in
execution time, depending on the optimization goals.},
	author = {H. Viswanathan and E. K. Lee and I. Rodero and D Pompili and Manish Parashar and M. Gamell}
}
@proceedings {1421,
	title = {Exploiting VM Technologies for Reactive Thermal Management in Instrumented Datacenters},
	year = {2010},
	month = {October},
	pages = {321-328},
	address = {Brussels, Belguim},
	author = {I. Rodero and E. K. Lee and D Pompili and Manish Parashar and M. Gamell and R.J. Figueiredo}
}
@proceedings {2278,
	title = {Towards Energy-Efficient Reactive Thermal Management in Instrumented Datacenters},
	year = {2010},
	publisher = {IEEE},
	address = {Brussels, Belgium},
	author = {I. Rodero and E.K Lee and Dario Pompili and Manish Parashar and M. Gamell and R. J. Figueiredo}
}
@proceedings {168,
	title = {Towards Energy-Efficient Reactive Thermal Management in Instrumented Datacenters},
	year = {2010},
	month = {10/2010},
	pages = {pp. 321-328},
	address = {Brussels, Belgium},
	abstract = {Virtual Machine (VM) migration is one of the most
common techniques used to alleviate thermal anomalies (i.e.,
hotspots) in cloud datacenter{\textquoteright}s servers of by reducing the load
and, therefore, decreasing the server utilization. However, there
are other techniques such as voltage scaling that also can be
applied to reduce the temperature of the servers in datacenters.
Because no single technique is the most efficient to meet temperature/
performance optimization goals in all situations, we work
towards an autonomic approach that performs energy-efficient
thermal management while ensuring the Quality of Service (QoS)
delivered to the users.
In this paper, we explore ways to take actions to reduce
energy consumption at the server side before performing costly
migrations of VMs. Specifically, we focus on exploiting VM
Monitor (VMM) configurations, such as pinning techniques in
Xen platforms, which are complementary to other techniques at
the physical server layer such as using low power modes. To
support the arguments of our approach, we present the results
obtained from an experimental evaluation on real hardware using
High Performance Computing (HPC) workloads on different
scenarios.},
	author = {I. Rodero and E. K. Lee and D Pompili and Manish Parashar and M. Gamell and R.J. Figueiredo}
}
