Abstract

Lately, pervasive and ubiquitous computing services have been under focus of not only the research community, but developers as well. Different devices generate different types of data with different frequencies. Emergency, healthcare, and latency sensitive services require real-time response. Also, it is necessary to decide what type of data is to be uploaded in the cloud, without burdening the core network and the cloud. For this purpose, Fog computing plays an important role. Fog resides between underlying IoTs and the cloud. Its purpose is to manage resources, perform data filtration, preprocessing, and security measures. For this purpose, Fog requires an effective and efficient resource management framework, which we provide in this paper. Moreover, since Fog has to deal with mobile nodes and IoTs, which involves objects and devices of different types, having a fluctuating connectivity behavior. All such types of service customers have an unpredictable relinquish probability, since any object or device can quit resource utilization at any moment. In our proposed methodology for resource estimation and management, we have taken into account these factors and formulate resource management on the basis of fluctuating relinquish probability of the customer, service type, service price, and variance of the relinquish probability. Implementation of our system was done using Java, while evaluation was done on CloudSim toolkit. The discussion and results show that these factors can help service provider estimate the right amount of resources, according to each type of service customers.