@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 {2099,
	title = {Real-time Tracking of Stress Propagation using Distributed Granger Causality},
	year = {2013},
	month = {Sept/2013},
	publisher = {Proc. of ACM International Conference on Body Area Networks (BodyNets)},
	address = {Boston, MA},
	author = {E. K. Lee and P. Pandey and D. Pompili}
}
@article {2096,
	title = {Thermal Camera Networks for Large Datacenters using Real-Time Thermal Monitoring Mechanism},
	journal = {Journal of Supercomputing (Springer)},
	volume = {64},
	year = {2013},
	month = {2013},
	pages = {383-408},
	chapter = {383},
	abstract = {Thermal cameras provide fine-grained thermal information that enables
monitoring and autonomic thermal management in large datacenters. The real-time
thermal monitor network employing thermal cameras is proposed to cooperatively
localize hotspots and extract their characteristics (i.e., temperature, size, and shape).
These characteristics are adopted to classify the causes of hotspots and make energy efficient
thermal management decisions such as job migration. Specifically, a sculpturing
algorithm for extracting and reconstructing shape characteristics of hotspots is
proposed to minimize the network overhead. Experimental results show the validity
of all the algorithms proposed in this paper.},
	author = {H. Liu and E. K. Lee and D. Pompili and X. Kong}
}
@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}
}
@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 {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 {2078,
	title = {Proactive Thermal Management in Green Datacenters},
	journal = {Journal of Supercomputing (Springer)},
	volume = {60},
	year = {2012},
	month = {May},
	pages = {165-195},
	chapter = {165},
	abstract = {The increasing demand for faster computing and high storage capacity has
resulted in an increase in energy consumption and heat generation in datacenters. Because of the increase in heat generation, cooling requirements have become a critical
concern, both in terms of growing operating costs as well as their environmental and
societal impacts. Presently, thermal management techniques make an effort to thermally profile and control datacenters{\textquoteright} cooling equipment to increase their efficiency.
In conventional thermal management techniques, cooling systems are triggered by
the temperature crossing predefined thresholds. Such reactive approaches result in
delayed response as the temperature may already be too high, which can result in
performance degradation of hardware.
In this work, a proactive control approach is proposed that jointly optimizes the
air conditioner compressor duty cycle and fan speed to prevent heat imbalance{\textemdash}the
difference between the heat generated and extracted from a machine{\textemdash}thus minimizing the cost of cooling. The proposed proactive optimization framework has two
objectives: (i) minimize the energy consumption of the cooling system, and (ii) minimize the risk of equipment damage due to overheating. Through thorough simulations
comparing the proposed proactive heat-imbalance estimation-based approach against
conventional reactive temperature-based schemes, the superiority of the proposed approach is highlighted in terms of cooling energy, response time, and equipment failure
risk.},
	author = {E. K. Lee and I. S. Kulkarni and D Pompili}
}
@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}
}
@article {160,
	title = {Data Center Local Thermal Management Based on Thermal Cameras Networks },
	journal = {Chinese Journal of Electronics},
	volume = {20},
	year = {2011},
	month = {April},
	pages = {347-351},
	abstract = {Global data center capacity is growing
rapidly, consuming more financial resources and emitting
more greenhouse gases. A significant portion of typical
data centers energy consumption can be apportioned to
the cooling infrastructure. In this paper, we proposed a
Thermal camera network (TCN) to improve utilization of
the cooling infrastructure. It can cool the hotspots intelligently
instead of the whole data center. A TCN is
composed of multiple thermal cameras and actors, which
can wirelessly communicate with each other. Actors will
cool the region where hotspots appear as soon as thermal
cameras locate the hotspots. In that way, the TCN can
save substantial energy in some meaning. In addition, this
paper addresses the method of cooperatively detection and
formulates the coordinates of the hotspot located by multiple
thermal cameras.},
	author = {H. Liu and E. K. Lee and D Pompili and X. Kong}
}
@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}
}
@conference {120,
	title = {On the Impact of Neighborhood Discovery on Geographical Routing in Wireless Sensor Networks},
	booktitle = {Proc. of IEEE Sarnoff Symposium},
	year = {2011},
	month = {May},
	address = {Princeton, NJ, USA},
	abstract = {In Wireless Sensor Networks (WSNs), sensing nodes
operate in dynamic environments resulting in neighboring nodes
being discovered or lost at any moment causing the network
topology to change constantly. Hence, routing schemes especially
geographical ones (which use node positions to route data
packets) require periodic exchange of control packets to discover
neighboring nodes. Even though it is intuitive that the overhead
caused by their periodic broadcasts may affect the end-to-end
performance of the routing scheme, previous works have not
thoroughly studied the impact of transmission power and frequency
of control packets in static as well as mobile environments.
Hence, based on our study, Distributed Neighborhood Discovery
Protocol (DNDP) is proposed that can make online decisions to
find the best transmit power and frequency for sending discovery
packets so to minimize the effect on routing.},
	author = {E. K. Lee and J. P. Varkey and D Pompili}
}
@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}
}
@article {1927,
	title = {Thermal Anomaly Detection in Datacenters},
	journal = {Journal of Mechanical Engineering Science (SAGE)},
	volume = {1},
	year = {2011},
	month = {December},
	pages = {1-14},
	author = {Yang Yuan and E. K. Lee and Pompili, Dario and Junbi Liao}
}
@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}
}
@article {165,
	title = {Proactive Thermal Management in Green Datacenters},
	journal = {Journal of Supercomputing, Special Issue on Energy-Efficient High-Performance Parallel and Distributed Computing},
	year = {2010},
	abstract = {The increasing demand for faster computing and high storage capacity has
resulted in an increase in energy consumption and heat generation in datacenters. Because
of the increase in heat generation, cooling requirements have become a critical
concern, both in terms of growing operating costs as well as their environmental and
societal impacts. Presently, thermal management techniques make an effort to thermally
profile and control datacenters{\textquoteright} cooling equipment to increase their efficiency.
In conventional thermal management techniques, cooling systems are triggered by
the temperature crossing predefined thresholds. Such reactive approaches result in
delayed response as the temperature may already be too high, which can result in
performance degradation of hardware. In this work, a proactive control approach is proposed that jointly optimizes the
air conditioner compressor duty cycle and fan speed to prevent heat imbalance{\textemdash}the
difference between the heat generated and extracted from a machine{\textemdash}thus minimizing
the cost of cooling. The proposed proactive optimization framework has two
objectives: (i) minimize the energy consumption of the cooling system, and (ii) minimize
the risk of equipment damage due to overheating. Through thorough simulations
comparing the proposed proactive heat-imbalance estimation-based approach against
conventional reactive temperature-based schemes, the superiority of the proposed approach is highlighted in terms of cooling energy, response time, and equipment failure
risk.},
	doi = {10.1007/s11227-010-0453-8},
	author = {E. K. Lee and I. Kulkarni and D Pompili and Manish Parashar}
}
@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}
}
