• Maitree B. Desai
• Reshma Dayma

Lots of new smart devices connected to the Internet of Things(IoT) are changing the way we live. In the current cloud computing model, latency, data volume, and bandwidth are all challenges that need to be addressed. Cloud and Internet of Things (IoT) architectures that bring services closer to the edge device are called "fog computing," and they work in conjunction with them. Fog computing's resource management is one of the open issues. The fog computing machine learning application has grown into powerful end-user and high-layer services that can do more analysis and give better answers to tasks. We suggested a Scheduling algorithm based on the ML technique. It uses K-mean clustering for grouping jobs based on time and cost. Clustering makes it easy to discover a collection of jobs per VM with minimal cost. iFogsim is being used to model the proposed approach. The simulation result reveals that our proposed scheduling technique improved execution cost and provided low network usage and better execution time than existing scheduling methods in iFogsim.

Fog Computing, Machine Learning, Task Scheduling, K-mean clustering, Internet of Things (IoT)

"Task Scheduling in Fog Computing Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.a755-a767, May-2022, Available :http://www.jetir.org/papers/JETIR2205100.pdf

"Task Scheduling in Fog Computing Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppa755-a767, May-2022, Available at : http://www.jetir.org/papers/JETIR2205100.pdf