Marina Zapater attends as speaker to UCAmI 2012. The main goal of this conference is to provide a discussion forum where researchers and practitioners on Ubiquitous Computing and Ambient Intelligence can meet, disseminate and exchange ideas and problems, identify some of the key issues related to these topics, and explore together possible solutions and future works. The Ubiquitous Computing (UC) idea envisioned by Weiser in 1991, has recently evolved to a more general paradigm known as Ambient Intelligence (AmI). Ambient Intelligence then represents a new generation of user-centred computing environments aiming to find new ways to obtain a better integration of the information technology in everyday life devices and activities. Marina has presented our first results within the GreenDISC project, proposing several research lines that target the power optimization in computing systems. In particular, we deal with two novel and highly differentiated computer paradigms that, however, coexist and interact in the current application scenarios: the Wireless Sensor Networks (WSN) and the high-performance computing in Data Centers (DC). For further information, please, refer to the paper: M. Zapater, J. L. Ayala, and J. M. Moya, “GreenDisc: a HW/SW energy optimization framework in globally distributed computation,” , J. Bravo, D. López-de Ipiña, and F. Moya, Ed., Springer Berlin Heidelberg, 2012, pp. 1-8. doi:10.1007/978-3-642-35377-2_1

1. GreenDisc: A HW/SW Energy
Optimization Framework in Globally
Distributed Computation
Marina Zapater†, José L. Ayala*, José M. Moya‡
†CEI Campus Moncloa, UCM-UPM
*Universidad Complutense de Madrid
‡ Universidad Politécnica de Madrid
Marina Zapater | UCAMI 2012 1

2. Outline
• Motivation
• Proposed solution
– The GreenDisc Platform
• Holistic Optimization Approach
– Power optimization of all implied agents
• Conclusions
• Future Work
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3. Motivation
• The key to next-generation e-Health solutions are
wearable personal health systems
– Biomedical monitoring (European initiatives)
FET “Guardian Angels”. European initiative: FET Flagships 2013 - http://www.ga-project.eu/home
• World-wide sensor deployment:
– Very large amount of data
• Electrocardiogram sensor (ECG), Electromyogram (EMG), O2 and
CO2, temperature, lactate, movement sensors.
– Raw data must be turned into useful information
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4. Motivation
Current needs and issues
• Need for an accurate, integrated and long-term
assessment and feedback.
– Acquire, monitor and analize data 24/7
• Need for an application-specific architecture
– But also provide flexibility and enough performance
• Current issues: energy and heat
– High energy consumption and short battery lifespan of sensor
nodes.
• Heat produced by sensor nodes for biomedical applications
– Tackle the computation needs to obtain useful information from
all data
• Energy consumption at the Data Center level must not put at stake
e-Health deployments
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5. Contributions
GreenDisc Platform
• A HW/SW energy optimization framework
– Versatile platform that integrates processing, analysis and
wireless communication of biomedical data.
– Both at the WSN-level and at the Data Center level
• Supports e-Health applications (and future
evolutions) with lower costs and shorter time-to-
market
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6. GreenDisc Platform
Context and System Architecture
• Based on Wireless Body Sensor Networks (WBSN)
– All sensors transmit to a PDA
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7. GreenDisc Platform
Context and System Architecture
• Based on Wireless Body Sensor Networks (WBSN)
– All sensors transmit to a PDA
• Computation takes place at Data Centers
– Data storage and processing
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8. GreenDisc Platform
Holistic Optimization Approach
• Power optimization in the processing nodes of the
WBSN
– Design of embedded processors for signal processing
– Optimization at the radio interface
– Design automation of applications for the processing node
• Power optimization in data centers
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9. Holistic Optimization
Processing nodes of the WBSN (I)
• Design of embedded processors for signal processing
– Architectural modifications considering the application
mapping, the execution profile, and the compiler
optimizations
– Reducing the energy consumption of the main energy
consumption sources
• Selection of instruction memory architecture
• Design of functional units with tunable architecture (dynamic
reconfiguration)
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10. Holistic Optimization
Processing nodes of the WBSN (I)
• Instruction memory
produces highest energy
consumption
• Proper selection of
instruction memory
architecture impacts energy
consumption
– SPM - scratch pad memory
– CELB - central loop
buffer
– CLLB- clustered loop buffer
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11. Holistic Optimization
Processing nodes of the WBSN (II)
• Power optimization in the radio interface
– Reducing the amount of information to transmit:
• Framework for signal analysis to develop compressed sensing
techniques for several bio-signals
• Case studies for monitoring bio-signals with a QoS study
– Reduce the overhead of the transmision protocol:
• Study of the impact of tuning several parameters of the MAC layer
(development of 802.15.4 MAC analytical model)
Compressed sensing: signal processing technique for efficiently acquiring and reconstructing a
signal. Uses the signal sparseness or compressibility in some domain, allowing the entire signal to be
determined from relatively few measurements.
Candes, E. J. and Wakin, M. B. (2008) An Introduction to Compressive Sampling.
IEEE Signal Processing Magazine. Vol 2 (pp. 21-30)
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12. Holistic Optimization
Processing nodes of the WBSN (III)
• Design automation of applications in the processing
node
– Aims to reduce the amount of data transmitted to the
backbone
– Providing a generic high-level model of the architecture
• Analysis of the impact of design parameters in power
consumption
• Framework for automatic design and optimization of
applications
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13. Holistic Optimization
Energy Optimization in Data Centers
• Implementation of several resource managing
techniques at different abstraction levels
• Exploiting the heterogeneity of applications and
computing resources for energy minimization.
– Proper usage of heterogeneity can lead to significant
energy savings
• Analysis of cooling mechanisms and development of
control techniques
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14. Holistic Optimization
Energy Optimization in Data Centers
• Potential benefits of workload characterization and
dynamic assignment policies
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15. Conclusion and
Future Work
• This paper shows how the GreenDisc platform can
optimize the energy consumption of next-generation
e-Health application by combining the usage of:
– Optimization policies at several levels of the WBSN
– Agressive energy efficiency policies at the Data Center
• Future work will integrate all steps of the platform
and show the overall savings for a particular e-Health
workload.
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16. Questions?
Thank you for
your attention
Marina Zapater
Laboratorio de Sistemas Integrados (LSI)
Universidad Politécnica de Madrid
marina@die.upm.es
http://greenlsi.die.upm.es
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