Computation offloading to save energy on handheld devices: a partition scheme

By Zhiyuan Li, Cheng Wang, Rong-Chang Xu. International Conference on Compilers, Architecture, and Synthesis for Embedded Systems, 2001

342 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3c24bf1754c57966005db8f5187fca69df03c518

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

A Dynamic Offloading Algorithm for Mobile Computing (2012, methodology): “Computation offloading provides a method to save energy [3]-[4] in which some of the application software components can be offloaded from mobile device to run on a remote server.”
A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing (2012, background): “[17] [16] are the early works to study the application partitioning problem.”
A survey of mobile cloud computing: architecture, applications, and approaches (2013, background): “Unlike [49,70], this scheme does not restrict the tasks to be partitioned at any specific level (e.”
Communicating While Computing: Distributed mobile cloud computing over 5G heterogeneous networks (2014, background): “Offloading typically requires code partitioning [7], [23], [24], aimed to decide which parts of the code should run locally and which parts should be offloaded, depending on contextual parameters, such as computational intensity of each module, size of the program state to be exchanged to transfer…”
Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges (2013, background): “Indeed, empowering computation capabilities of mobile devices is not a new concept and there have been different approaches to achieve this goal, including load sharing [4], remote execution [5], cyber foraging [6], and computation offloading [60], [61] that are described as follows.”
Computation Offloading Toward Edge Computing (2019, methodology): “Then, graph partitioning algorithms are applied to cut the graph by achieving some goals, such as saving energy [5], [86], [87], reducing execution time [88], minimizing network traffic [89], or maximizing throughput [90].”