@proceedings {2106,
	title = {Enabling Real-time In-Situ Processing of Ubiquitous Mobile-Application Workflows},
	year = {2013},
	month = {Oct./2013},
	address = {Hangzhou, China},
	author = {H. Viswanathan and E. K. Lee and D. Pompili}
}
@proceedings {2098,
	title = {Learning-based Framework for Policy-aware Cognitive Radio Emergency Networking},
	year = {2013},
	month = {Dec/2013},
	publisher = {Proc. of IEEE Global Telecommunications Conference (GLOBECOM)},
	address = {Atlanta, GA},
	author = {E. K. Lee and H. Viswanathan and D. Pompili}
}
@proceedings {2075,
	title = {Model-based Thermal Anomaly Detection in Cloud Datacenters},
	year = {2013},
	month = {May},
	address = {Cambridge, MA},
	author = {E. K. Lee and H. Viswanathan and D Pompili}
}
@proceedings {1924,
	title = {An Autonomic Resource Provisioning Framework for Mobile Computing Grids},
	year = {2012},
	month = {September},
	address = {San Jose, CA},
	author = {H. Viswanathan and E. K. Lee and I. Rodero and D Pompili}
}
@inbook {2284,
	title = {Emergency Networking in Licensed Spectrum using Cognitive Radios },
	booktitle = {Cognitive Radio for Wireless Cellular and Vehicular Networks},
	year = {2012},
	publisher = {H. Venkataraman and G-M. Muntean, Eds., Springer},
	organization = {H. Venkataraman and G-M. Muntean, Eds., Springer},
	author = {H. Viswanathan and Dario Pompili}
}
@inbook {1906,
	title = {Emergency Networking in Licensed Spectrum using Cognitive Radios - Challenges and Insights},
	booktitle = {Cognitive Radio and its Applications for Next Generation Cellular and Wireless Networks, H. Venkataraman and G-M. Muntean},
	year = {2012},
	publisher = {Springer},
	organization = {Springer},
	chapter = {Emergency Networking in Licensed Spectrum using Cognitive Radios - Challenges and Insights},
	author = {H. Viswanathan and D Pompili}
}
@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}
}
@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}
}
@proceedings {1922,
	title = {Leveraging Mobile Grid Computing for Interference Alignment and Cancelation},
	year = {2012},
	address = {Orlando, FL},
	author = {Parul Pandey and H. Viswanathan and D Pompili}
}
@proceedings {1893,
	title = {Mobile Grid Computing for Data- and Patient-centric Ubiquitous Healthcare},
	year = {2012},
	month = {June},
	address = {Seoul, Korea},
	abstract = {A new context-aware data- and patient-centric
paradigm for ubiquitous healthcare is central to deliver personalized
healthcare solutions to the elderly and the physically
challenged. However, this new paradigm requires real-time in the-
field processing of wirelessly collected vital signs using inherently
complex physiological models and analysis of the processed
information (derived physiological parameters) under context
(e.g., location, ambient conditions, current physical activity)
to extract knowledge about the health condition of patients.
As the computational capabilities of biomedical sensor nodes
are insufficient to run these models, an innovative resource
provisioning framework that harnesses the computing capabilities
of under-utilized electronic devices in the vicinity (e.g., smart
phones, laptops, tablets, DVRs, medical terminals) to form a
mobile computing grid is presented. The framework is imparted
with self-optimization and self-healing capabilities for efficiency
and robustness under uncertainty, respectively. The proposed
mobile grid management framework serves as a key enabling
technology for Internet-of-Things- (IoT-) based next-generation
ubiquitous healthcare solutions.},
	author = {H. Viswanathan and E. K. Lee and D Pompili}
}
@article {103,
	title = {Research Challenges in Computation, Communication, and Context Awareness for Ubiquitous Healthcare},
	journal = {IEEE Communications Magazine},
	volume = {50},
	year = {2012},
	month = {May},
	pages = {92{\textendash}99},
	abstract = {A new paradigm for ubiquitous healthcare characterized by pervasive continuous vital sign data collection, real-time processing of monitored data to derive meaningful physiological parameters, and context-aware data- and patient-centric decision making, is central to deliver personalized healthcare solutions to the elderly and the physically challenged. However, this new paradigm requires real-time processing of wirelessly collected vital signs using inherently complex physiological models and analysis of the processed information under context (e.g., location, ambient conditions, current physical activity) to extract knowledge about the health condition of patients. As the computational capabilities of biomedical sensor nodes are insufficient to run these models, this article presents an innovative resource provisioning framework that organizes and harnesses the computing capabilities of under-utilized electronic devices in the vicinity (e.g., laptops, tablets, PDAs, DVRs, medical terminals) in home and hospital settings. Novel wireless communication solutions for reliable vital sign transmission and algorithms for acquiring context awareness to support this framework are also discussed.},
	author = {H. Viswanathan and Chen, Baozhi and Pompili, Dario}
}
@proceedings {1923,
	title = {VMAP: Proactive Thermal-aware Vitual Machine Allocation in HPC Cloud Datacenters},
	year = {2012},
	address = {Pune, India},
	author = {E. K. Lee and H. Viswanathan and D Pompili}
}
@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}
}
@article {161,
	title = {Self-organizing Sensing Infrastructure for Autonomic Management of Green Datacenters},
	journal = { IEEE Network},
	volume = {25},
	year = {2011},
	month = {July},
	pages = {34-40},
	abstract = {The scale and complexity of modern datacenters are growing at an alarming rate
due to the rising popularity of the cloud computing paradigm as an effective
means to cater to the ever increasing demand for computing and storage. The
management of modern datacenters is rapidly exceeding human ability, making
autonomic approaches essential. In this article, methods for acquiring thermal
awareness using real-time measurements and heat and air circulation models as
well as solutions for proactive autonomic datacenter management that exploit this
awareness are discussed. Novel communication and coordination schemes that
enable self-organization of a network of external heterogeneous sensors (e.g., thermal
cameras, scalar temperature and humidity sensors, airflow meters) into a multitier
sensing infrastructure capable of real-time datacenter monitoring are also
presented.},
	author = {H. Viswanathan and E. K. Lee and D Pompili}
}
@proceedings {1925,
	title = {SILENCE: Distributed Adaptive Sampling for Sensor-based Autonomic Systems},
	year = {2011},
	month = {June},
	address = {Karlsruhe, Germany},
	author = {E. K. Lee and H. Viswanathan and D Pompili}
}
