Abstract
The personal devices such as laptops and smartphones are being equipped with better hardware, which leads to stronger computing abilities. At the same time, the demand of variousmultimedia applications requires increasing computational resources. We propose to build the multimedia fog computing platform, which aims at reducing the cost of using cloud computing. In this platform, the fog provider receives the jobs from the fog users and schedules them to the fog workers/devices. There are three main research problems: (i) prediction of the required amount of resources of the jobs, (ii) prediction of the available resources of the fog devices, and (iii) scheduling the jobs and the fog devices. This thesis focuses on the prediction of the amount of the available resources. We adopt three machine learning algorithms, namely, the Random Forest, Gradient Boosting Tree, and Neural Network, and implement them using open source libraries. We apply two datasets, desktop and datacenter datasets, where the traces come from real users and machines in the cloud datacenter, respectively. We use 80% of both datasets and perform 10-fold cross validation to fine-tune the hyperparameters of the proposed algorithms. The optimal combinations of the hyperparameters for both datasets are different. We learned that when the fog provider applies new datasets, or when the dataset dramatically changes, it is necessary to re-tune the hyperparameters. We implement a simulator and use the rest 20% of both available resource datasets and a real animation rendering jobs dataset to drive our simulator. The simulation results show that: (i) the Neural Network-based algorithm achieves 6.08% and 2.00% deviation in average for the desktop and datacenter datasets, respectively, and (ii) more accurate prediction of the amount of available resources leads to fewer failed jobs.