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Ashwin Patti

dblp:93/9446 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none

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

Systems, architecture and hardware · 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 networks
1 paper
Edge and fog computing · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%
Software engineering, system software, and programming languages
1 paper
Runtime systems and virtual machines · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Edge and fog computing › mobile edge computing › computation offloading
application partitioning
0.112011
CloneCloud: elastic execution between mobile device and cloud · EuroSys 2011
Edge and fog computing › mobile edge computing › computation offloading › mobile computation offloading
mobile cloud offloading
0.112011
CloneCloud: elastic execution between mobile device and cloud · EuroSys 2011
Cloud and datacenter computing
computation offloading
0.112011
CloneCloud: elastic execution between mobile device and cloud · EuroSys 2011
Cloud and datacenter computing
mobile cloud computing
0.112011
CloneCloud: elastic execution between mobile device and cloud · EuroSys 2011

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

thread migration · 0.4static analysis · 0.4dynamic profiling · 0.4
YearPublicationVenuePosition
2011 CloneCloud: elastic execution between mobile device and cloud
abstract
Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity with more powerful machines ranging from laptops and desktops to commercial clouds. This paper presents the design and implementation of CloneCloud, a system that automatically transforms mobile applications to benefit from the cloud. The system is a flexible application partitioner and execution runtime that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud. CloneCloud uses a combination of static analysis and dynamic profiling to partition applications automatically at a fine granularity while optimizing execution time and energy use for a target computation and communication environment. At runtime, the application partitioning is effected by migrating a thread from the mobile device at a chosen point to the clone in the cloud, executing there for the remainder of the partition, and re-integrating the migrated thread back to the mobile device. Our evaluation shows that CloneCloud can adapt application partitioning to different environments, and can help some applications achieve as much as a 20x execution speed-up and a 20-fold decrease of energy spent on the mobile device.
Byung-Gon Chun, Sunghwan Ihm, Petros Maniatis, Mayur Naik, Ashwin Patti
EuroSys5