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Papleologos Spanos

dblp:40/4414 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 67% Energy-efficient computing · 33%
Computer networks
1 paper
Internet of things and sensor networks · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Energy-efficient computing › power management
dynamic power management
0.012001
Adaptive Power-Fidelity in Energy-Aware Wireless Embedded System · RTSS 2001
Embedded and real-time systems
energy-efficient embedded systems
0.012001
Adaptive Power-Fidelity in Energy-Aware Wireless Embedded System · RTSS 2001
Embedded and real-time systems
real-time operating systems
0.012001
Adaptive Power-Fidelity in Energy-Aware Wireless Embedded System · RTSS 2001
Internet of things and sensor networks › networked embedded systems
wireless embedded systems
0.012001
Adaptive Power-Fidelity in Energy-Aware Wireless Embedded System · RTSS 2001

Methods — techniques the papers use, named apart from their topics

workload prediction · 0.1feedback control · 0.1dynamic voltage scaling · 0.1
YearPublicationVenuePosition
2001 Adaptive Power-Fidelity in Energy-Aware Wireless Embedded System
abstract
Energy aware system operation, and not just low power hardware, is an important requirement for wireless embedded systems. These systems, such as wireless multimedia terminals or wireless sensor nodes, combine (soft) real-time constraints on computation and communication with requirements of long battery lifetime. In this paper, we present an OS-directed dynamic power management technique for such systems that goes beyond conventional techniques to provide an adaptive power vs. fidelity trade-off. The ability of wireless systems to adapt to changing fidelity in the form of data losses and errors is used to tradeoff against energy consumption. We also exploit system workload variation to proactively manage energy resources by predicting processing requirements. The supply voltage, and clock frequency are set according to predicted computation requirements of a specific task instance, and an adaptive feedback control machanism is used to keep system fidelity (deadline misses) within specifications. We present the theoretical framework underlying our approach in the context of both a static priority-based preemptive task scheduler as well as a dynamic priority based one, and present simulation-based performance analysis that shows that our technique provides large energy savings (up to 76%) with little loss in fidelity (<4%). Further, we describe the implementation of our technique in the eCos real-time operating system (RTOS) running on a StrongARM processor to illustrate the issues involved in enhancing RTOSs for energy awareness.
Vijay Raghunathan, Papleologos Spanos, Mani Srivastava 0001
RTSS2