ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading

By Sokol Kosta, Andrius Aucinas, Pan Hui, R. Mortier, Xinwen Zhang. 2012 Proceedings IEEE INFOCOM, 2012

1,235 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Information Systems

View Paper: https://www.semanticscholar.org/paper/faff5dac93f324923ccb2c5ebeb1844de1643bd9

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Citations in the corpus

Fog Computing: Principles, Architectures, and Applications (2016, methodology): “Computation offloading has been an active area of research in the mobile computing domain, with most of the proposals offloading workloads to the cloud [6][7][8].”
The Internet of Things, Fog and Cloud Continuum: Integration and Challenges (2018, background): “Therefore, offloading has been studied to help in reducing energy consumption [137, 138] and response time [139, 140] of applications.”
Optimization of Radio and Computational Resources for Energy Efficiency in Latency-Constrained Application Offloading (2014, background): “As in [3], [6], [7], [12], and [13], energy consumption and total execution time are the key performance indicators considered for the optimization.”
Joint offloading and computing optimization in wireless powered mobile-edge computing systems (2017, methodology): “Some practical examples of MEC efforts include the mobile assistance using infrastructure (MAUI) [3], ThinkAir [4], and the new MEC standardization J. Xu is the corresponding author. group launched by the European telecommunications standards institute (ETSI) [5].”
A Survey of Fog Computing: Concepts, Applications and Issues (2015, methodology): “ThinkAir [26] moves on to elasticity and scalability of the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images.”
Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing (2014, methodology): “Speciﬁc examples of mobile computation ofﬂoading techniques are: MAUI [19], ThinkAir [20], and Phone2Cloud [21].”
EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications (2019, background): “The remarkable feature shared by the Thinkair, Cloudlet and Music frameworks in [22]–[24] is that they rely on the virtualization of the served mobile devices, in order to enable them to offload computingintensive tasks to their clones running on distant nodes.”
Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling (2017, methodology): “In order to execute computation on cloud servers, the MDs and the servers are required to operate offloading frameworks, such as MAUI [3] and ThinkAir [4].”
Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer (2015, background): “MCO has been an active research area in computer science [3] where research has focused on designing mobile-cloud systems and software architectures [6], [7], virtual machine migration design in the cloud [8] and code partitioning techniques in the mobiles [6] for reducing the energy consumption and”
Computation Offloading Toward Edge Computing (2019, methodology): “[7] S. Kosta, A. Aucinas, P. Hui, R. Mortier, and X. Zhang, “ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading,” in Proc.”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, methodology): “Similarly to Reference [43], Reference [80] proposes a framework called ThinkAir, which utilize the smart-phone virtualization technique to facilitate the application mitigation from smart phone to cloud.”
Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions (2018, background): “Many research works have suggested Fog-based processing for IoT applications in smart transportation systems [97]– [99], Augmented and Virtual Reality [100], [137], [138], and healthcare [27], [139], [140].”
A hierarchical edge cloud architecture for mobile computing (2016, methodology): “On one hand, efﬁcient VM allocation algorithms have been developed for geo-distributed clouds with real-time request arrivals [4], and also support parallel method executions with multiple VM images [5].”
Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices (2016, background): “, MAUI [10] and ThinkAi r [11], were proposed.”
DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems (2015, methodology): “Previous studies related to the mobile cloud ofﬂoading tried to minimize energy consumption and processing delay of mobile devices under a ﬁxed size of workloads, static network environments and outdated device functionalities [4]–[6].”