@article{SisLab4501,
           title = {Towards an Elastic Fog Computing Framework for IoT Big Data Analytics Applications},
          author = {Manh Linh Pham and Truong Thang Nguyen and Tien Quang Hoang},
       publisher = {Hindawi},
            year = {2021},
         journal = {Wireless Communications and Mobile Computing},
        keywords = {fog computing, Internet of Things, elasticity, big data analytics, smart home},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4501/},
        abstract = {IoT applications have been being moved to the cloud during the last decade in order to reduce operating costs and provide more scalable services to users. However, IoT latency-sensitive big data streaming systems (e.g. smart home application) is not suitable with the cloud and needs another model to fit in. Fog computing, aiming at bringing computation, communication, and storage resources from
"cloud to ground" where closes to smart end devices, seems to be a complementary appropriate proposal for such type of application. Although there are various research e?orts and solutions for deploying and conducting elasticity of IoT big data analytics applications on the cloud, similar work on Fog computing is not many. This article firstly introduces AutoFog, a Fog computing framework, which
provides holistic deployment and an elasticity solution for fog-based IoT big data analytics applications including a novel mechanism for elasticity provision. Secondly, the article also points out requirements that a framework of IoT big data analytics application on fog environment should support. Finally, through a realistic smart home use case, extensive experiments were conducted to validate typical aspects of our proposed framework.}
}