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Anway Mukherjee

dblp:228/2744 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2018
0000-0002-7071-8742ORCID · reported

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

Systems, architecture and hardware · 1 · 1 first-author

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
Energy-efficient computing · 83% Cloud and datacenter computing · 8% Embedded and real-time systems · 8%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Energy-efficient computing › power management
dynamic voltage and frequency scaling
0.312018
Energy Management of Applications With Varying Resource Usage on Smartphones · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2018
Energy-efficient computing
energy management
0.312018
Energy Management of Applications With Varying Resource Usage on Smartphones · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2018
Energy-efficient computing › energy management
smartphone energy management
0.312018
Energy Management of Applications With Varying Resource Usage on Smartphones · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2018
Embedded and real-time systems › real-time scheduling
quality-of-service-aware scheduling
0.112018
Energy Management of Applications With Varying Resource Usage on Smartphones · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2018
Cloud and datacenter computing
resource management
0.112018
Energy Management of Applications With Varying Resource Usage on Smartphones · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2018

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

runtime phase detection · 0.3offline profiling · 0.3
YearPublicationVenuePosition
2018 Energy Management of Applications With Varying Resource Usage on Smartphones
abstract
The split-screen mode in smartphones allows for the simultaneous side-by-side execution of multiple applications, which permits multitasking and improves users' experience. However, such technology results in simultaneously running multiple foreground processes, which increases the power consumption of a smartphone and reduces its battery lifetime. We present an integrated system-level resource management framework that aims to minimize the total energy consumption of a smartphone with negligible impact on the quality of service (QoS) of applications whose resource usage characteristics are not precisely known offline or vary over time. Our proposed solution: 1) leverages applications' offline profiles to detect instantaneous phase changes (i.e., dynamic changes in resource usage patterns) of the workload of a given application at runtime and 2) adaptively adjusts both voltage and frequency settings of the processor and memory bandwidth to achieve the most energy-efficient configuration subject to QoS constraints. Our approach is also able to progressively reduce the energy consumption of newly installed real-world applications for which there exists no prior resource usage data. Experiments on a Nexus 6 smartphone show that our approach achieves an average energy reduction of 23% (19%) and up to 31% (27%) compared to existing work (and default Android governor) for different combinations of real-world applications running side-by-side in split-screen mode. For applications with no prior resource usage data, the proposed framework saves up to 22% (18%) of energy within at most 14 s when compared to existing work (and default Android governor).
Anway Mukherjee, Thidapat Chantem
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.1