Cost-effective processing for Delay-sensitive applications in Cloud of Things systems

The steep rise of Internet of Things (IoT) applications along with the limitations of Cloud Computing to address all IoT requirements promotes a new distributed computing paradigm called Fog Computing, which aims to process data at the edge of the network. With the help of Fog Computing, the transmission latency and monetary spending caused by Cloud Computing can be effectively reduced. However, executing all applications in fog nodes will increase the average response time since the processing capabilities of fog is not as powerful as cloud. A tradeoff issue needs to be addressed within such systems in terms of average response time and average cost. In this paper, we develop an online algorithm, unit-slot optimization, based on the technique of Lyapunov optimization. It is a quantified near optimal solution and can online adjust the tradeoff between average response time and average cost. We evaluate the performance of our proposed algorithm by a number of experiments. The experimental results not only match up the theoretical analyses properly, but also demonstrate that our proposed algorithm can provide cost-effective processing while guaranteeing average response time.