COMET: Code Offload by Migrating Execution Transparently
By Mark S. Gordon, D. Jamshidi, Scott A. Mahlke, Z. Morley Mao, Xu Chen. USENIX Symposium on Operating Systems Design and Implementation, 2012
435 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/81951f9b72e64948752abb319052f1d2dbd1f40d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Wei Gao
- Li Lin
- Haisheng Tan
- Amir Vahid Dastjerdi
- Rajkumar Buyya
- Shanhe Yi
- Liang Tong
- Zhenhua Han
Citations in the corpus
- Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications (2013, methodology): “As a demonstration, COMET [6] has experimented offloading JavaScript to the cloud and achieved an acceleration rate of up to 8.5¥ .”
- Computation Offloading Toward Edge Computing (2019, background): “The running of CloneCloud and COMET needs a full VM migration to synchronize VM states.”
- JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services (2018, background): “There are existing prior arts focusing on offloading computation from the mobile to the cloud[15], [41], [42], [43], [44], [45], [46].”
- Towards wearable cognitive assistance (2014, unknown): “COMET [12] uses distributed shared memory to enable transparent offloading of Android applications.”
- Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, background): “Furthermore apart from the assistance to be provided by remote cloud servers [296]–[299] it can also be provided by nearby cloudlets [300]–[303], or even by nearby mobile devices [304]–[306].”
- A Survey of Fog Computing: Concepts, Applications and Issues (2015, background): “COMET [15] leverages the distributed shared memory and VM synchronization primitives to augment smartphones or tablets with machines available in the network.”
- Application-aware traffic scheduling for workload offloading in mobile clouds (2016, background): “Other schemes further improve the offloading generality by supporting multi-threaded [5] and interactive applications [12].”
- Internet of Things: An Overview (2017, methodology): “Instead of using physical instances, ThinkAir[35] and COMET[36] leverage virtual machines offered by IaaS cloud providers as offloading targets to boost both scalability and elasticity.”
- Online job dispatching and scheduling in edge-clouds (2017, methodology): “COMET [16] adopted the distributed shared memory model to allow the application threads to migrate freely between the mobile device and the cloud server.”