An Agent based Resource Provision for IoT through Machine Learning in Fog Computing

Abstract

Internet of things (IoT) is a large scale distributed system that is growing at rapid fire pace. It is a technological revolution that makes devices smarter, computations intelligent and communications more informative. While IoT still presuming different definitions, its application had set a broader footprint in almost all walks of our daily life. The voluminous amount of data generated by the millions of IoT devices imposes a higher demand on the computation and storage resources. The compute resources to serve the IoT applications need to be chosen depending upon the heterogeneity of the IoT devices. The various constraints of IoT make resource provisioning in the cloud a non trivial task. Fog computing is the apt platform to deal with such constraints of IoT. The IoT challenges like heterogeneity, scalability and low latency can be addressed by fog computing by adapting intelligence features of machine learning in its resource management techniques. In this paper, we propose a resource management technique for fog computing in which an agent adapts centralized learning and distributed scheduling of IoT tasks. This paper considers the micro data center as the resource that have the hardware and software capability to use TCP/IP protocol suite in fog computing paradigm.